Synthetic morphology and dynamic systems in Architecture by Carolina Figueroa

SYNTHETIC MORPHOLOGY AND DYNAMIC SYSTEMS IN ARCHITECTURE

MSc Digital Architecture Newcastle University Carolina Ramirez-Figueroa 110379843

What is beautiful in science is the same that is beautiful in Beethoven. There is a fog of events and suddenly you see a connection. It expresses a complex of human concerns that goes deeply to you, that connects things that were always in you that were never put together before (Victor Weisskopf cited in Jones, 2008, p.33)

Acknowledgements

It is not easy to express how much I owe to these people,

To my mother and father, for always being there in the rather chaotic and beguiling experience of architecture. And specially for encouraging me into always following my dreams. To my brother, for making sure to keep me updated on the most photogenic cat. To my supervisor and mentor, Dr. Martyn Dade-Robertson. For encouraging me into following the path least walked, for all the stimulating Kofi Bar chats and for keeping me in the right track. But specially for immersing himself into the swampy waters of synthetic biology as well and helping me tie loose ends just in time. All my appreciation. To Dr. Meng Zhang, for all their patients explanations to a complete biology neophyte. Particularly for letting me take part in her experiments and for all the lunch hours she invested in me. To all my friends, for keeping me sane throughout the last three months. To Karina, for her amazing friendship and for being there in all my nervous crisis despite time zone differences. And specially to Luis, for all the though-provoking conversations, support, love and encouragement. But above all, for convincing me that all of this actually made sense.

TABLE OF CONTENTS

I Introduction Initial considerations

8 8

mediating artifact

II Problematisation, research question and objectives

Objectives

Domain

III Literature review

Synthetic biology

Intersection between synthetic biology and architecture

Natural processes of form generation

IV Methodology

Introduction

Precedents

V In-silico simulation. Agent based particle system

Methodology

Findings

VI In-Vitro experiments. Bacterially induced calcium carbonate

Materials and methods

Findings

VII Discussion

VIII Conclusions and further work

Bibliography

Appendix

INTRODUCTION chapter i

Initial

considerations Synthetic biology is set to be the next great revolution in the history of science and technology. Just as the steam engine provided the backbone for the development of science, technology and culture in the 19th century, so did computation for the 20th century. Synthetic biology finds itself in the same place as computation in the brink of the microcomputer revolution. The seventies saw the plummeting of production costs associated to the production of microprocessors and the introduction of higher-level programming language, which liberated users from the complexities of machine code by offering a more human-friendly programming tool. Such tools fuelled the creation of hobbyist clubs where hackers would meet to share electronic parts and collectively construct the Do It Yourself (DIY) culture. The same individuals would later become the IT entrepreneurs that initiated the personal computer revolution of the late 1980s. Synthetic biology is generally considered to be the modification of biological systems to create new organisms by applying engineering design principles. Consequently, it has built in the notion of scale and abstraction.(Benner and Sismour, 2005, p.533). Standardised biological parts also called biobricks, which essentially are DNA sequences intended to codify specific behaviours, are made available through online public repositories. When inserted in nearly blank organisms, such basic sequences assemble new biological machines. It is expected that such biological parts would provide a framework to program biological systems through higher-level processes of parts assembly. Such abstraction hierarchy can be described as a higherlevel biological programming language in the making. A similar trend to the microcomputer revolution has emerged in synthetic biology. During the last years, there has been an exponential growth in the number of available biobricks. Costs associated to the process of DNA sequencing, the production of DNA out of raw amino acids, has seen a sustained decrease in price and the introduction of new sequencing techniques has shortened production times considerably. Additionally, a number of non-experts communities has been set up to experiment with biological systems. Notably different DIYBio (Do It Yourself Biology) clubs modelled after the original DIY clubs of computers, have been established internationally to open source laboratory experimentation under a philosophy of garage biology. Moreover, professional researchers in the field have initiated different organisations to promote the wider dissemination of synthetic biology experimentation. Drew Endy, dubbed as the â&#x20AC;&#x2DC;Steve Jobsâ&#x20AC;&#x2122; of synthetic biology (Hotz, 2011), started the International Genetically Engineered Machine (iGEM) competition. Hosted at MIT, the

competition calls on undergraduate students to propose, design and build a novel biological engineered system using standard biological parts. It is expected that the annual competition would prove the ease of designing biological systems by using interchangeable biobricks (Mooallem, 2010). Unsurprisingly, It has been suggested that synthetic biology will play a major influential role in technological development in the 21st century (Dubberly, 2008, p.1) creating new industries and pushing cultural shifts. Potential outcomes cover a wide range of applications ranging from biofuels, cheaper drugs, to novel diagnostic tools, to information systems based on biological modules (Khalil and Collins, 2010, p.367).

â&#x20AC;&#x153;Synthetic biology is set to be the next great revolution in the history of science and technology. Just as the steam engine provided the backbone for the development of science, technology and culture in the 19th century, so did computation for the 20th century. Synthetic biology finds itself in the same place as computation in the brink of the microcomputer revolution. â&#x20AC;&#x153;

Synthetic biology has the potential to profoundly impact architecture. Architecture can be fundamentally defined as disposition of materials in shapes and forms that enclose spaces in scales relevant to the human body. Hence, the material dimension is of special importance to architecture. The very definition of architectural styles and eras is articulated through the materials and production technologies associated with them. The possibility of engineering biological machines and systems opens up a potential new material reality as well. Natural occurring processes leading to the formation of materials after long periods of time, such as mineralisation leading to limestone, can be synthetically engineered to be manufactured in shorter period and shaped at will through biological agents. In other cases, biological machines, i.e. genetically modified bacteria, can be inserted in concrete cracks to catalyse the formation of a sealing polymer, thus making the concrete more flexible and resilient. The prospect of engineered biological materials has attracted a considerable amount of attention. Architect theorists and practitioners have initiated a debate on what has been dubbed as a literal biological paradigm for architecture (Hensel, 2006). Increasingly, architecture is no longer discussed in terms of building processes, but rather of growth, direction and nurture. The discussion spans across a large spectrum of topics. On one hand, architects start to freely speculate on the aesthetics of these new building blocks, more concerned with the effect that with the underlying processes. In doing so, a number of typological prototypes (Preissner, 2011) and morphological speculations on surface geometries (Iwamoto, 2011) were developed. On the other hand, the discussion has been focalised in creating a political argument around the possibility of self-constructed, self-healing cities (Armstrong, 2011) an argument not unlike the one advanced by 1960s experimental architecture group Archigram.

// CHAPTER I

In all cases nevertheless, efforts can be described as being variations of the existing movement of biomimicry, whereby biology is taken as inspiration model. Biological organisms are looked at the molecular level and replicated in a scaled up version to fit the anthropometric scale. A process of linear direct extrapolation is observed, whereby synthetic biology is looked at as a source for aesthetic and system analogy in architecture. In the most experimental cases, the formal thinking is one of architects waiting for science to catch up with the requirements of an already developed and wellestablished aesthetic of an upcoming biological architecture (Preissner, 2011). However, this biological paradigm is hardly new. The relationship between architecture and biology can be traced to the architectural theory in the late nineteenth century (Mertins, 2004) and inscribed to the model of design by analogy (Gentner et al., 2001): a mapping of patterns and processes from one territory to the other (Holyoak and Thagard, 1996). This research has found a gap in the intersection between architecture and synthetic biology. Whilst there is an evident potential for architecture, the current architectural thinking is not taking full advantage of such possibilities. As stated before, architecture is articulated through materials possibilities and technologies. Fundamentally, materials posses two important qualities to the construction of human structures: shape, the actual geometric configuration of the material, and substance, the molecular structure and the traits it enables. Geometric configuration determines construction processes and formal possibilities, such as the prismatic shape of masonry dictates the shape of straight walls and arches. On the other hand, molecular structure plays a crucial role in determining characteristics such as structural properties and surface appearances of materials. In some cases, it is the molecular structure that directly conditions the macroscale structural performance of materials, such as it is the case with oyster shell formation. Certain protein components in the soluble matrix of molluscan shells act to control deposition of Calcium Carbonate, allowing the

formation of pores in the crystal deposition (Wheeler et al., 1981). Such process would render a highly resistant material due to the structural connection generated through the porosity pattern. In other cases, nonetheless, molecular structure interplays with higher-scales structures to define specific traits in the material. There has not been any research in the tectonics of materials formed through synthetic biology, thus there is not notion as to the affordances of shape and substance for such processes and materials. The current discourse in architecture concerned with synthetic biology is established with an expectation of form that look biological. From there, design algorithms are reverse-engineered to achieve such goal. It will be argued in this research that the only validation process available under such circumstances is macroscale resemblance to engineered systems. Historically, architects have developed theories, styles and design processes through material engagement with the manufacturing processes. As means of illustration, master masons synthesised the possibilities of different stones through a long process of trial and error. However, the engineering processes of synthetic biology are aimed at creating biological machines at the molecular level. Conversely, architecture fundamentally concerns itself with anthropometric related scales. In order to address this gap, a strategy must be devised to enable a mutual understanding of architecture and synthetic biology. When architects look at biological organisms and processes, they understand it in terms of structures, space and the relationship of parts to the whole. Where a molecular biologist finds interesting how specific amino acids impact on the configuration of crystal, an architect might find an interesting fractal pattern of calcium crystal and a potential manufacturing process. The following section lays the supporting arguments for the narrative this dissertation is thread about. The concept of mediating artifacts is introduced and contextualised in architecture practice, and

“Historically, architects have developed theories, styles and design processes through material engagement with the manufacturing processes.”

relates it back to the architectural exploration of materials and processes enabled by synthetic biology. Further, in Part 2 of this research it will be presented a literature review concepts relating to synthetic biology, architecture and the current state of influence between both areas. In part 2, we will describe at length the methodology employed in this research. Part 3 will concern with description and findings of the experiments carried out in this work. Finally, Part 4 will give a detail discussion of the findings, and it will be argued that architectural research into synthetically engineered biological materials necessitates of a material engagement with the laboratory processes. Also, specific research avenues will be suggested for the future work in this area.

Mediating artifacts Pérez-Gómez and Pelletier originally defines mediating artifacts when stating that ‘the architect has not “made” buildings; rather, he or she has made the mediating artifacts that make significant buildings possible’ (2000, p.7). He encompasses a number of specific tools under the term of mediating artifact: verbal instructions, inscriptions, drawings, scale and digital models. They represent an interface between the designer and the material world to conceptualise, test and communicate design intents. Historically, such design tools has been highly dependant on the manufacturing technology and practices available to architecture. For instance gothic cathedrals were built under the premise of being a slow constructive practice, result of an additive process of decision-making by a multitude of craftsmen. Thus, construction rules were directly applied on site. Consequently, the first mediating artifacts were embodied in wooden templates that were used by stonecutter to determine a general profile of different building components (Spuybroek, 2012, pp.18-19). In contrast, the renaissance incorporated the notion of architects as bearers of buildings’ foreknowledge. Construction process remained a collective effort, however design

// CHAPTER I

commenced to be regarded as the intellectual effort of one individual who understood every detail beforehand. In consequence architects developed more detailed and prescriptive mediating artifacts, such as models and scale drawings, to document design intention. Production of architecture has evolved throughout the following centuries and radically changed the relationship between mediating artifacts and the built architectural object. Such evolution has slowly shaped the current paradigm: a prescriptive process of literal translation from artifact to built environment as described by Pérez-Gómez: The process of creation prevalent in architecture today assumes that a conventional set of projections, at various scales from site to detail, adds up to a complete, objective idea of a building (...) These projective representations rely on reductive syntactic connections, with each projection constituting part of a dissected whole. They are expected to be absolutely unambiguous to avoid possible (mis) interpretations, and to function as efficient neutral instruments devoid of inherent value other than their capacity for accurate transcription (Pérez-Gómez, 2005, p.217). In enabling architects to understand synthetically engineered systems, the current configuration of mediating artifacts are fundamentally unable to serve as exploration and testing tools. Existing mediating artifacts in architecture respond to the specific production technology available to architecture, which differ considerably from biological systems. Borrowing terms from mathematics, natural occurring structures can be broadly defined as non-linear dynamic phenomena that are often subject to processes of mutual interaction between its constituting elements. This definition involves that organisms generate shape by ever-changing processes that are the result of various agents acting upon the system, transforming it and being

transformed at the same time. Consequently, natural models are difficult to simulate and require a process of abstraction to approach its driving principles. In contrast, man-made structures can be described as static systems that are dependant upon the translation of a standardised set of projections. There is an evident difference between biological and manmade structure as annotated by spatial biologist Peter Lloyd Jones: By acknowledging that scale and dynamics in living and man-made structures are distinct, it is simple to comprehend why some many contemporary, biomimetic architecture generally represents a cold caricature of organismic systems. As such, this type of literal design approach should be abandoned, and replaced with a more rigorous approach that appreciates our commonalities and differences, focusing instead on the deeper relationships that coexists in architecture and biology (Jones, 2008, p.33). Following the train of thought established in this section, it can be concluded that current spatial investigation techniques in architecture, such as scale models, are unfit to simulate and understand synthetically engineered biological systems. This conclusion is drawn after confronting the fundamental nature of man-made structures, the natural realm of architecture, and biological systems, the intended area of study. This research proposes a digital mediating artifact as a tool to investigate the qualities of designing with synthetically engineered biological materials. In doing so, it is necessary to define a methodology to digitally describe the essential behaviour of such systems. In essence, it is expected that the structure of the proposed artifact will differ from the existing tools in architecture. ‘The architect has not “made” buildings; rather, he or she has made the mediating artifacts that make significant buildings possible’ ( Pérez-Gómez, 2000, p.7).

Digital simulation has been pointed (DeLanda, 2011) as one of the most suited techniques to model emergence, the fundamental quality of natural systems. Emergence states that novel and complex properties are the result of causal interaction of individual agents with simple behaviours. Therefore, it is impossible to apply deductive logic to forecast system behaviour out of the laws governing individual agents. The following section will introduce the research question, which will serve to the greater purpose of enabling a mutual understanding of architecture and synthetic biology.

// CHAPTER I

PROBLEMATISATION AND HYPOTHESIS chapter ii

The backdrop set in the previous pages serves to introduce the research question this work considers: How is it possible to investigate the qualities of working with synthetically engineered materials from an architecture relevant perspective?

Figure 2.1 Bacteria colonies within an Agar plate. Picture from in-vitro experiments in this research

The introduction to this research stated directly the first major gap in the intersection of architecture and synthetic biology. By looking at the properties of man-made structure and biological systems, we know inherently that the qualities of designing architectural objects that will be produced by biological systems differ considerably from the current paradigm of construction. In a broad approximation, we may not hope to conceptualise a thin vertical structure and expect for biological units to arrange themselves so that they produce such structure as it is currently done with masonry pieces. However, architectural research has not produced any knowledge further than that. A second gap was merely hinted at. Whilst synthetic biology holds the potential to work with higher levels of abstraction, thus providing the possibility of creating biological organisms out of the combination of simple standardised biobricks, it does so at a molecular level. Fundamentally, architecture concerns with the manipulation of material to create spaces at anthropometric scales. This difference of scales is the second major gap in the field. The hypothesis this dissertation puts forward is that it is possible to investigate the properties of designing with synthetically engineered materials through a combination of laboratory and digital approaches. In the introduction to this work, two material qualities were defined as being of special interest to architecture: composition and shape. It is expected that the combination of a laboratory and a digital process will help the designer to understand the spatial affordances of new materials. In the first hand, digital simulation will shed light on the shape material can take depending on the variables involved in its formation. On the other hand, a laboratory experiment observed by the designer will give cues as to the possibilities of such material in an architectural context. Moreover, the combination of both experiences will serve to offer initial considerations on what qualities should be considered to be integrated in the design of new man-made biological structures.

The greater motivation of this research is to contribute to a research avenue, which enables synthetic biology and architecture to understand each other and as a consequence find common territory. In so doing, it is of paramount importance to address both gaps, which has been described here. Whilst the method abstract provided here helps to partially answer some aspects of the first gap, it is nonetheless far from giving concrete answers to the conflict of scales. However, the motivation for this research is the rationale that it is impossible to address the conflict of scales without first start to acknowledge the specific qualities of designing with synthetically modified materials. It is expected that architects will be able to contribute in the development of synthetically modified materials, and would therefore find themselves in a position to incorporate them in architecture in the most advantageous way. However, it is understood that the length of this work can only but help in this endeavour in a minor degree. The main contribution of this work is in starting the discussion of what further questions and areas of research need to be addressed to understand the complexity of natural systems from an architectural perspective.

Objectives

The main aims of this research is established as:

1. Investigate the qualities of working with synthetically engineered materials from an architectural perspective.

With this in mind, the following objectives have been established:

• Understand the main processes by which structures in nature are brought into being.

• Understand the realities of architectural research conducted in laboratory settings.

• Describe computationally the basic steps followed by biological systems in self-assembly processes.

• Give a first approach to how the gap between scales in synthetic biology and architecture may be addressed.

// CHAPTER II

Domain Limestone is an extensively used building material in architecture. Its first applications can be traced back to Ancient Egypt and Greece, with the construction of the Giza Pyramids and the extensive use of marble, a highly crystalline form of limestone, in Greek architecture (Oates, 2008). Given this long history, limestones have been used varying capacities in construction, including load-bearing elements and as cladding in thin sheets (Smith et al., 2010). Additionally, crushed and processed limestone is widely used as aggregates in the production of cement, concrete and different forms of paints.

allowed the researcher to gain knowledge of the realities of building materials in the lab. As it will be developed further throughout this work, there is a myriad of constrains and factors affecting experimental work.

Limestone results from a natural process of sedimentation of calcium carbonate (CaCO3) crystals, which occurs over the course of hundreds of years through the slow sedimentary deposition of chemicals, such as calcium, in warm waters. The chemical process giving way to calcium carbonate can be described as the precipitation or addition of carbonate in highly alkaline environments, specially rich in Ca2+ ions (Ercole et al., 2012, p.1). This process may lead to a polymorphous outcome, that is it can adopt different morphologies depending on the specific conditions that undergoes during its production: vaterite, aragonite and calcite. Crystals involved in the formation of limestone are calcite and aragonite. It is believed (Smith et al., 2010) that specific amount of these components, alongside the presence of impurities, would determine the specific type of limestone developed, thus determining physical characteristics such as porosity, hardness, colour and texture.

This work will follow a methodology comprising two standpoints. On one hand, the experimental work in laboratory settings served as an observational study to gain knowledge on the mechanisms of crystal formation. The researcher acted as an observer and did not have a direct involvement in the technical development of the experiments. However, the personal contact with the molecular biologists served as a manner to interpret results and enquiry specific details of the work, which were of special interest to a potential application in architecture.

Biologically controlled mineralisation (BCM) is an emerging field concerned with biologically mediated or induced production of mineral structures, such as calcium carbonate. Rodríguez-Navarro et.al annotates that BCM ‘involves remarkable control of size, morphology, and phase selection, resulting in complex, hierarchal organic-inorganic structures with unusual physicochemical properties’ (Rodriguez-Navarro et al., 2012, p.4017). In the specific case of calcium carbonate, bacteria are introduced to a calcium rich environment to control de production of crystal. The specific bacterial activity, which gives way to the production of Calcium carbonate, varies going from simple metabolism, to more specific transformation at protein level (Ercole et al., 2012, p.1). Research on the area is specially attractive to architecture, as it involves application ranging from selfheating components for existing buildings to the creation of new materials (Decho, 2010, p.1).

Concurrently, a computational prototype was developed to explore the properties of designing with synthetically engineered materials. The premise this computation description takes as departing point is that the qualities of designing with such materials differ diametrically from the ones associated with the current means of production in architecture. Knowledge gained through literature review and in the biomineralisation experiment served to model the system after a non-linear dynamic phenomenon. Therefore, the system functions around attractors, strength forces and areas of influences. It will be described in the methodology that such decisions follows an abstraction of the phenomena observed in the laboratory experiments.

During the early stages of this research, an opportunity arose to collaborate with a molecular biologist at Northumbria University in a series of experiments looking at Calcium Carbonate Biomineralisation using Bacillus Pasteurii. This opportunity helped shaped this research and

When introducing the first set of mathematical abstraction to describe morphogenesis, Alan Turing reflected: ‘This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped

that the features retained for discussion are those of greatest importance in the present state of knowledge‘(Turing, 1952, p.5). The computation description developed in this research is a falsification: an abstraction model, which ignores certain features in order to understand and simulate others. In the context of architectural research, such model falls in the category of a mediating artifact. It serves therefore as an interface to understand and enquiry the observed system. In this particular case, it served to partially

◀ Figure 2.2 Calcium carbonate crystal from the in-vitro expreiments in this research ▶ Figure 2.3 Centre for Bacterial Cell Biology at Newcastle University

explain the distribution of crystals observed in a bacteria colony. Following this observation, a design technique was built to manipulate chemical attractors, therefore shaping the actual deposition of crystals in the system. The following sections will cover the basic concepts, which serve as backdrop to this research. First, the concept of synthetic biology will be developed further, and will lead to a brief review of the state of the art in the area. Then, the relationship between architecture and synthetic biology will be covered, including a description of the historical relationship between architecture and biology. It will be concluded that the current architectural prototypes in synthetic biology are an extension of the biological analogy. Also, a chapter is dedicated to the general process which nature employs to develop form. Finally, technical considerations on the specific domain will be offered. In the second part of this research, the specific methodology will be described, followed by the results and discussion.

‘This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge‘ (Turing, 1952, p.5).

// CHAPTER II

literature review chapter iii

Synthetic biology

In 1953, Watson and Crick developed what proved to be a revolutionising model to explain the information model of genetics: DNA. His model proposed a double strand, which was made up of four fundamental building blocks, also known as nucleotides: Adenine, Thymine, Guanine and Cytosine. The combinatorial rules established that each base pair could only pair with another specific binding nucleotide, and in this codification lies all the information necessary to define the specificity of each biological unit (Benner and Sismour, 2005, p.534). This breakthrough allowed researchers to start reading and understanding genetic material. Further breakthroughs would equip scientist with more tools to extend the range of capabilities from merely â&#x20AC;&#x2DC;readingâ&#x20AC;&#x2122;. Recombinant DNA, the process of molecular cloning of DNA sequences in living organism, allowed to cut and paste entire sequences across organisms (Andrianantoandro et al., 2006, p.1). DNA synthesis on the other hand enables the creation of DNA sequences through in-vitro techniques, opening the range of possibilities of creating entirely new sequences, thus not theoretically constrained to the range of DNA sequences found in existing living organisms. However, the information model of designing organisms in the DNA level involves a high level of complexity. The DNA model involves a programming language of sorts, based on the combinatorics of four base pairs, therefore exhibiting modularity and an inherently circuitlike connectivity. Theoretically, it has been liken to the connectivity of electronic systems, whereby parts can be combined to perform a desired behaviour in a circuit. However, it is often observed that theoretical sequences exhibit unexpected behaviour when implemented in an actual biological unit. Under such paradigm, every genetic modification would require a researcher with an extensive knowledge in the specific organism to be modified, leading to slow development (Hallinan et al., 2012, p.263). Synthetic biology is design to tackle this potential slow modification of organisms. It combines the modification of biological systems to create new organisms with engineering design principles including notions of scale and abstraction. In order to transcend the natural limitations of working at DNA level, synthetic biology relies on an abstraction model that allows to treat DNA sequences as interchangeable parts that can be universally implemented in living cells. The model is similar to the one used in computer science to tackle the complexity of designing systems at the level of machine language. Instead of working at low level, programming languages are designed to feature a human-friendly

syntax. As seen on figure 1.1, the lowest level corresponds to raw DNA. Higher up, parts codify a definable behaviour and are composed of specific DNA sequences. From this level up, Devices and System correspond to complex set of behaviours and purposefully designed organisms such as bacteria and cells. There have been a wide variety of efforts towards standardisation in synthetic biology. The ultimate goal is to create a collection of compatible biological parts, namely nucleic sequences which are optimised to conform to a series of technical standards (Shetty et al., 2008, p.2). Ideally two parts, which have been engineered by two independent teams, could be combined together to create a new standardised part (Ibid). This approach reduces the level of complexity involved in synthetic biology research. Standard DNA sequences, codifying a specific behaviour, are incorporated in public online repositories.

Systems Devices Parts DNA (sequence) Figure 3.1 Synthetic biology abstratction model

Although there has been a number of proposed standards in the field, currently the most widely recognised is the BioBrick assembly standard. This canon has been used in groups and initiatives such as the International Genetically Engineered Machines competition (iGEM), whereby undergraduate students are encouraged to design and implement biological systems by combining biobricks assembly standard parts (Mooallem, 2010). The competition has been running for eight years and has produced biological systems that go from bacterially detectors of arsenic concentration, to red blood cell substitutes (Foundation, 2012). The 2010 iGEM edition awarded gold medal to the Newcastle University BacillaFilla project. BacillaFilla is a genetically modified variation of Bacillus subtilis. The bacteria, described as a concrete healing agent, is designed to be sprayed into cracks of damaged concrete structures. Once inside, it would populate the cracks producing a filler composed of Calcium Carbonate and bacterial glue (filamentous B. subtilis cells and levansucrose glue) (Firth, 2010). This chemical combination produces a resistant whilst flexible substance to seal the concrete, knitting the damaged structured together (Daily, 2010). BacillaFilla follows the same protocol in producing calcium carbonate as the experiments observed in this research: metabolism in the bacterial organism produces urease, which in turns affects pH. Under the heightened pH conditions, the calcium rich environment found inside the cracks pulls in carbon dioxide from the air creating calcium carbonate crystals (Firth, 2010).

// CHAPTER III

ntersection between synthetic biology and architecture This section has given a quick overview of synthetic biology, covering the main areas of research and current development. As elaborated, the distinguishing trait of synthetic biology is the concept of scale and abstraction, introducing the concept that changes in the molecular level of DNA affects the function of the overall system. This research follows the abstraction model of synthetic biology to propose a mediating artifact that operates on the higher-level components, therefore investigating on the qualities of designing with biological systems without concerning with the low-level codification of such organisms. The following section will review how architecture practice has reacted to the advances in synthetic biology. It will start with a historical review of the biological metaphor in architecture, followed by a detailed account of the current design thinking discussing synthetic biology in architecture. It will be concluded that the current discourse in architecture can be characterised as an extension of the organic metaphor in architecture, which can be traced to the architectural thinking of the early 20th century, and specially to the architectural oeuvre of Mies Van de Rohe, Le Corbusier and Buckminster Fuller. Moreover, current architectural projects revolving around the potential of synthetic biology overlook the abstraction model described in this section, making a literal extrapolation of the molecular biological level. The investigation carried out in this work departs from this point to explore the possibilities of crystal formation at microscopic scale. Instead of proposing a literal formfinding method based on the extrapolation of these patterns, it acknowledges that the dynamics controlled in the artifact will influence the properties and deposition of the engineered biological material.

â&#x20AC;&#x153;Synthetic biology is design to tackle this potential slow modification of organisms. It combines the modification of biological systems to create new organisms with engineering design principles including notions of scale and abstraction

Biological and system

analogy in architecture The biological paradigm found in most of the architectural projects inspired by synthetic biology is hardly new. The relationship between architecture and biology can be traced to the architectural theory in the late nineteenth century (Mertins, 2004) and inscribed to the model of design by analogy (Gentner et al., 2001): a mapping of patterns and processes from one territory to the other (Holyoak and Thagard, 1996). The current discourse in architecture, specially in the area of digital architecture, has been characterised by a fascination for biological process enhanced by processing power in computational systems (Weinstock, 2010, p.26). Central to this is the concept of morphogenesis, identified as the process nature follows to evolve shapes (Menges, 2012). When speculating on a new â&#x20AC;&#x2DC;Literal biological paradigmâ&#x20AC;&#x2122; (Hensel, 2006, p.18), Michael Hensel identifies a breakthrough in the Architecture-Biology relationship: The long-proclaimed biological paradigm for architectural design must for this reason go beyond using shallow biological metaphors or a superficial biomorphic formal repertoire. The consequence is a literal understanding of the design product as a synthetic life-form embedded within dynamic and generative ecological relations (Ibid). Steadman (2008b) posits that the biological analogy in architecture can be better described through the organic model, whereby the traits of living organisms are adopted by architecture in a high-level abstraction and extrapolation. He goes on to trace this relationship to the theory of architecture in Ancient Greece, and is partly attributed to a quest to find balance and proportion in design, similar to the ones find in natural systems (2008a, pp. 1-2). The nature of such analogy may be further clustered around two topical subjects. First, a visual one concerned with the visual

Figure 3.2 Centre for Bacterial Cell Biology at Newcastle University

composition. Such analogy will follow and organic approach and establish relationships between the whole and the parts. Beauty was therefore achieved through the visual balance of having an integrated organism. On the other hand, the functional analogy is an evolution of the organic paradigm. Not only balance and integration was sought after, but it was theorised that the architectural object had to be functional and well adapted to the environment in order to be beautiful. Under this category we find the work of Buckminster Fuller and the modernist movement (2008a, pp. 8-15). Nonetheless, several biological analogies have been established in architecture, stemming from either the systemic or the aesthetic paradigm. However, the details of such analogy are modelled after the field of biology it draws inspiration from. When analysing the evolution of biological analogy in architectural theory throughout the twentieth century, it can be outlined through the organic, ecological and Darwinian analogical models (Steadman, 2008), each of them nurtured by the advancement in the fields of life sciences. There is a common narrative in every metaphor developed throughout the 20th century: the notion of immanence and emergence. Such philosophical foundation lies beneath the concept of simulation, which will be proposed in this research as a model for architectural design when considering biological building bricks.

Immanent turn Mertins (2004a) finds the biological analogy in architecture as being closely interconnected to the confrontation transcendence to immanence. The latter is normally explained through the concept of emergence, the complex pattern observed when individual agents with simple rules of behaviour interact. As can be gathered from this definition, the final outcome of emergent systems cannot be expected after a simple examination of the individual rules governing the agents. Therefore, such property

has been likened to the variation and novelty of form generation in biological systems. Immanence is regarded as the first modern gesture as it denies transcendental forces as the source for structuring the world around us. In its stead, it favours a material explanation as a result from the interaction of physical entities. In the context of form generation, architecture would find itself in a major discussion and ensuing paradigm shift in form finding. The impact of such a concept would be extensive in architecture. As an extension of considering form as a result of a material process of mutual interactions, historical styles are regarded as remnants of old authorities and institutions (Mertins, 2007). It was therefore deemed necessary to explore new design methods that would account for the material processes involved in the manufacture of architecture. Starting circa 1900, H.P. Berlage lead a movement of progressive architecture concerned with generating a new design methodology based on basic geometric units that would interact and emerge form. He would state: â&#x20AC;&#x153;the laws under which the Universe is formed, and is constantly being reformed: it is the laws which fill us with admiration for the harmony with which everything is organised, the harmony which penetrates the infinite even to its invisible atomsâ&#x20AC;? (Berlage cited in Mertins, 2004b, p.5). What is worth remarking of Berlage is the conceptual influence he exerted on the International style. It has been asserted that Berlage (Berlage cited in Mertins, 2004a, p.5) referred to an 1860 study on radiolarians when suggesting the all-encompassing rules that govern the universe. The study, produced by biologist Haeckel, presented a thorough review on single cell marine organisms accompanied by detailed artistic illustration. Radiolarians are able to secrete highly complex and variated skeletons of silica (Richards, 2008), quite considerable for simple one-celled organisms. Dâ&#x20AC;&#x2122;Arcy Thompson would further develop a mathematical treaty on different morphogenetic processes, in which one section would be dedicated to analyse the radiolarian. In his On growth and form

“On growth and form would prove to affect deeply architectural thinking in the oncoming years. The basic principles of emergence would branch out in different expressions.” (Thompson and Bonner, 1992) he would assess that “the radiolarian can generate continuous skeletons of netted mesh or perforated lacework that are more variegated, modulated and intricate—even irregular— than any snowflake” (Thompson cited in Mertins, 2004a, p.364) On growth and form would prove to affect deeply architectural thinking in the oncoming years. The basic principles of emergence would branch out in different expressions. For designers such as Buckminster Fuller, Ludwig Mies van der Rohe, Vladimir Tatlin and Le Corbusier, the process found in radiolarians was to be understood as a cosmological force transcending different scales (Tilder and Beth Blostein, p.103). For a second group, clustered around the figure of Frei Otto, understanding of emergent processes was to be appropriated further in architecture. They would advocate to understand form as the result of energy transaction within the environment, therefore of interactivity. Their thinking would closely allign with that expressed by biologist and philosopher Waddington in that form “is produced by the interaction of numerous forces which are balanced against one another” (Waddington cited in Mertins, 2004b, p.367). As revised in this section, the impact of biological advances and theories has been instrumental in changing architectural thinking historically. This is specially true for the last two centuries, to the point where the advent of computation in design processes has exponentially increased the crossfertilisation between architecture and biology. However, this relation has remained to the level of analogy. Although concepts of morphogenesis and emergence have permeated our understanding of architecture, it has remained in a conceptual level of inspiration. This limitation has been specially defined by the reality of the materials used in the production of architecture. Whilst biological processes are dynamic in nature, our fabrication technology compels us to produce static structures. In the next section, it will be described how the current thinking in architecture, specially the one

// CHAPTER III

associated with the intersection of design and synthetic biology, can be described as well through the biological analogy. Although it has been widely theorised that such works transcend the analogy to approach a literal biological paradigm in architecture (Hensel, 2006), the same traits of direct extrapolation across scales is observed. In the first section of this literature review it was described how synthetic biology advances useful concepts of scale and abstraction. Following this model, it can be concluded that although the molecular level has a direct impact on the higher-level functions of organisms, patterns do not repeat literally across scales. It is proposed in this research that in order to approach synthetically engineered systems, it is necessary to develop a notion of the influence of how atomic processes may shape the macroscale.

Current impact of

synthetic biology in architectural thinking Development in the area of digital architecture has seen in the last ten years an explosion in the recourse to the biological analogy. Empowered by the computational capabilities and visualisation tools, the movement has received a considerable amount of attention in media (Sellars, 2011, Holmes, 2012, Griggs, 2012). It is important to stress how the current proposals in the area seek to transcend the analogy. Although their philosophical foundation is heavily grounded on the same principles of environmental interactivity, they start to reassess this condition with the use of actual living materials. It is expected that in the near future, synthetic biology will allow architecture to be created by self-growing entities capable of dynamic adaptation to the environment. Such materials would be embedded with the necessary information in the same way biological organisms are codified through DNA. Consequently, such principle has been likened to a genetic blueprint that would constitute the instruction for structures to unravel (Croning, 2011, p.39). As expected, such promise has created a

“This research takes the opposite approach. It is interested in investigating the qualities of designing with synthetically engineered materials.”

broad range of responses in architectural debate. Even when some articles discussing of the possibilities of an evolving and self-organising architecture can be traced back to 2004 in an article entitled The synthetic sublime (Perez, 2004) presented in the ACADIA conference, the first joint publication to address the topic in the 2008 edition of AD journal Neoplasmatic Design. Articles revolved around the early research possibilities in conflating genetic engineering and synthetic biology with architecture. In so doing, the term neoplasmatic was coined to refer to semi-living entities that would blur the boundary between natural and artificial material (Cruz and Pike, 2008a). They might exhibit some living characteristics, but would not be defined as proper biological material. Articles were heavily based on a speculative discourse nevertheless. Catts and Zurr identifies tissue engineering as a suitable area to be applied into the creation of art objects, and covers an early attempt at this in the Tissue Culture & Art project (Catts and Zurr, 2008). On the same tone, Armstrong (2008, pp.83-85) proposed the possibility of literally ‘growing’ building out of purposefully modified organisms. However, the article by Steve Pike is noteworthy in that it details a new framework whereby architects would collaborate with microbiologists and mycologist in creating novel built artifacts. The capacity is proposed to be similar as the one that currently takes place when dealing with specialists in different subsystems for a building. He elaborates: For the designer to utilise microorganic material in a meaningful way, with any degree of achievable intent, it is imperative that the material may be manipulated and controlled, as for other traditionally available materials. In the capacity of coordinator, designers must engage the expertise of specialists, drawing upon a body of knowledge that they themselves cannot be expected to possess. For architects, the conventional assembly of sub-consultants will be extended to include microbiologists

and mycologists alongside structural engineers and mechanical and electrical engineers. Such an interdisciplinary approach is essential for the successful creation of partially living architectural hybrids (Pike, 2008b, p.18). Following Neoplasmatic design, a research avenue would be opened to investigate on the possibilities of a new aesthetic for artifacts manufactured by means of biological units. Architecture would be though of in terms of an organisms emerging out of a chemical building unit, specially through processes of accretion or deposition of material. One of the most visible trends in this group is the one of protocells. Protocells are chemical units exhibiting some properties that are generally associated with biological systems. The protocell model has been establish as a long term effort to engineer, control and build synthetic structures that emulate cell subunits, such as the membrane and intracellular matrix. Protocell research started in the 19th century, when Moritz Traube assembled a chemical system of semipermeable membrane of copper ferrocyanide. Such a chemical barrier exhibited selective permeability, as found on natural cell membranes (Hanczyc, 2008, pp. 4-5). The focus of protocell research has been characterised as an efforts to understand the chemical interaction that gave way to life in the early stages of the universe. It has been proposed that a correct balance of chemicals and environmental conditions could recreate the processes that allowed life to emerge out of nonliving material (Hanczyc, 2009). Given these traits, protocells are considered a simple sort of material that can be chemically programmed to grow structures. It has been suggested that a sustained investment may theoretically lead to the production of the first synthetic protocell, incorporating motility, selective membrane, motility and RNA (Hanczyc, 2008, p.14). At present, research in the area has only achieved assembly of synthetic units exhibiting selective permeability and motility. One of the downsides of protocells is that they remain a theoretical model that has not been

possible to replicate. Therefore, any attempt to model and simulate its behaviour has remained in a very hypothetical early process. Nonetheless, protocells has been instrumental in the research avenue leaded by Neil Spiller and Rachel Armstrong through the AVATAR research group at Greenwich University. The 81st edition of AD journal, entitled protocell architecture, has explored the material tectonics arising from a fabrication paradigm based on protocells. Notably, Paul Preissner offers in Back to the Future, a typological prototype for the protocell, the Protocell Tower. He goes on to validate the research taking place in the area ...the reality of this biochemical science is often a bit behind some of the formal thinking that has taken place within architecture over the past 15 years. Another way to put it is that we have been waiting for this for a while and have already figured out how to deal with its looks (and a number of us architects have decided we like it!) (Preissner, 2011, p.108) The article features a series of Computer Generated visualisations of Preissnerâ&#x20AC;&#x2122;s typological prototype. It takes a few seconds to the trained eye to decipher the modelling process undertaken to achieve the typology. The visualisation features a tall tower building with a regular rectangular grid-floating facade. In the corners of this floating facade, Preissner has proposed irregular variation of this grid, which appear to conform to a model of VoronoiDelaunay tessellation. The footnote reads New aesthetic anomalies offer ways in which to casually and yet radically restructure the visual expectation and pleasures of our city environments (...) Adapting to the connective structure, the cellular material can rapidly develop new formal expressions that retain visual rhythmâ&#x20AC;&#x2DC; (Ibid). The irregular variation observed in the model is to be interpreted as Preissnerâ&#x20AC;&#x2122;s depiction of molecular

// CHAPTER III

variation found in cells. A blunt analogy can be found in this interpretation. The form generation process consists of a modifier stacked on top of an otherwise perfectly regular grid, with the aim of introducing a biologically looking irregularity. A specific effect is sought after, and the form generation is reversed engineered to comply. Such process relegates the natural model as a form inspiration merely. Although there is evidence of a preoccupation to address how design might be transformed by such technology, the debate has remained relatively shallow, as it has not been grounded on any actual scientific basis. This section has accounted for the architectural reaction to synthetic biology. Even when the formal discourse has been oriented towards a literal biological paradigm for architecture, it has been demonstrated how the current work in the field is a variation of the long-existing biological analogy in architecture. Two arguments are particularly instrumental in this statement: the notion of abstraction and scale. As reviewed in the first section of this literature review, synthetic biology introduced the conception of an abstraction model to biology based on complexity and scale. Based on this model, the molecular level determines the behaviours of biological systems. The discourse detailed in this section fails to recognise this. It creates a linear extrapolation of molecular arrays across scales. Moreover, the topological prototypes which has been presented follows the same design strategy: they focus of effect, and reverse engineer the generation process after that. This research takes the opposite approach. It is interested in investigating the qualities of designing with synthetically engineered materials. In doing so, it approaches the topic from the natural process: form is a consequence and not the intended goal. Furthermore, two main material properties were defined for this exploration: shape and substance. The following section will cover the main processes of form generation in nature, such as emergence, self-assembly and natural scaffolding. These concepts will inform the design of the computational description proposed in this work.

Natural processes of formgeneration

One of the main arguments this dissertation is built around is that biological systems are fundamentally different from man-made structures. As introduced early on, architectural practice resorts to mediating artifacts in an effort to understand and test how different variables interact and affect the architectural system. However, the current set of mediating artifacts is based on static linear phenomena. Even when buildings change and evolve over time, the main structure remains mainly static and requires an active external intervention to reconfigure. This argument serves to put forward the notion that such artifacts are unfit to investigate design with synthetically engineered materials. This chapter will serve to introduce the fundamental nature of all natural systems, namely: emergence, self-assembly and scaffolding. Moreover, it will give some conceptual hints as how nature creates form. The concepts developed in this section will serve to inform the computational description proposed in this work, and will introduce some of the concepts used later on to describe findings in the in-vitro and in-silico experiments of this thesis.

Emergence Emergence involves the notion that complex systems are the direct result of the combination of combined agencies (Lewes, G.H. cited in De Wolf and Holvoet, 2005, p.2). The concept is, nonetheless, difficult to define precisely as there are a number of schools of thoughts and theories diverging on the consideration of emergent systems. A widely accepted definition is advanced by De Wolf (2005): A system exhibits emergence when there are coherent emergents at the macro-level that dynamically arise from the interactions between the parts at the micro-level. Such emergents are novel w.r.t. [with regards to] the individual parts of the system (De Wolf and Holvoet, 2005, p.1). The introduction of emergence in life science led to a significant change in the ontological perspective of biology. Prior to emergence, biology resorted to mystic concepts such as ‘life force’ and the ‘élan vital’ in explaining the complex behaviours exhibited by organic systems. The introduction of emergent behaviours came hand in hand with the immanent turn, described at length in the chapter Intersection between synthetic biology and architecture. Broadly speaking, philosophical thinking shifted towards

an immanent orientation, whereby any process is considered a material phenomenon of mutual interactions, departing from the transcendental viewpoint, where a superior or mystical force is accepted to be the source of order and coherence. De Wolf (2005, pp. 3-5) goes on to elaborate on the main properties exhibited by emergent systems:

• Micro-Macro effect, which states that patterns • • • •

•

exhibited by high-level systems are the result of low-level interactions; Radical Novelty, referring to the fact that the macro-level behaviour are not expressed at the agent level in any form; Coherence, agents maintain a higher level identity and attain the effect of a whole; Dynamical, the system configuration changes constantly as a function of time; Two-way link, high-level patterns are caused by the low-level interactions, and the individual agents are influenced by patters arising in the high-level system as well; Decentralised control, control is only exerted by the combined agency of low-level components.

Ultimately, such traits of emergence build up to conclude that emergent systems generate their patterns internally, without any interference or active manipulation of an external agent. Therefore, any attempt to explain emergent patterns through inductive logic, that is the transference of truth from general principles to specific cases without additional information added in each level, would be rendered insufficient. When building a masonry structure, external agents to the system would exert an active manipulation of the agents (masonry pieces). Moreover an information transference is taking place from a centralised control. The structure is built based on instructions found, presumably, on a mediating artifact such as a architectural drawing. Given these conditions, such system cannot be described as emergent. The production process can be characterised as static, linear and prescriptive. Specific topographical information is codified in a mediating document, which is later translated directly into an architectural object through a process of linear extrapolation of topographical relationships. Conversely, the interaction of biological units exhibits the criteria to be classified as emergent. When dealing with systems that aim to produce form, the definition of emergence need to be evolved towards self-assembly, which will be covered in the following section.

Self-assembly and natural scaffolding A strong relationship between emergence and self-assembly can be found in the literature. Emergence refers to the properties exhibited by a system when the high-level patterns hold a casualty relationship to the low-level interaction of agents. There are a number of associated behaviours, which are commonly considered to be typical of emergent systems as annotated by De wolf and described in the previous section. When a system exhibits such traits, It can be broadly defined as self-assembly. Halley and Winkler define self-assembly

▶ Figure 3.3 Bacteria colony from the in-vitro expreiments in this research

systems as ‘a nondissipative structural order on a macroscopic level, because of collective interactions between multiple (usually microscopic) components that do not change their character upon integration into the selfassembled structure’ (Halley and Winkler, 2008, p.14). Richard Jones offers a classic example of self-assembly. When put in a box, a self-assembly jig-saw puzzle will reorder itself into its coherent version. However blunt, this image serves to introduce the concept of a system that follows a series of steps into evolving its internal configuration. Combined with the properties studied for emergent systems, it can be inferred that such system will be able to perform such reordering without any exterior control, and that low-level agents are embedded with the information to achieve its desired configuration. As expected, the overall pattern is not expressed explicitly in the instructions built into the constituent agents. It is a result of simple laws of interaction.

// CHAPTER II

In nature, different biological units are designed to evolve shape. Such form generation processes can be broadly classified in two sub-types: tissue formation and biomineralisation. In tissue formation, cells follow an adhesion protocol to assemble three-dimensional structures. It has been suggested that the combination of adhesion mechanisms acts as a translation of genetic material into spatial structures (Gumbiner, 1996, p.345). However, the adhesion interactivity of cells can only be structurally sustained through a mesh of rigid structures known as cytoskeleton. The cytoskeleton is a proteinbased structure embedded within the cells own cell membrane. Such structures connect across cells and offer a temporary structure to tissue formation (Ibid). In such cases, the structure is formed by the own cells, and a rigid scaffolding is formed only for developing the tissue architecture. Generally, tissues act as interfaces within a greater biological system, helping to define the boundaries of specific functions performed by units of lower complexity such as organs. Biomineralisation mechanisms however aim to create inorganic structures out of the local interaction of biological units. In general terms, a mesh of biological units is organised to sustain the precipitation, of a rigid material such as crystals. The term precipitation refers to the chemical process whereby a solid substance emerges out of the combination of two solutions. Generally, the resultant solid is known as precipitate (Zumdahl and DeCoste, 2012, p.106). In this process, biological units serve as a dynamic organic scaffold. In this regard, Decho (2010, p.2) states: In biomineralization, organic molecules are used to initiate precipitation, influence the continued growth of the precipitate, or selectively inhibit precipitation (...) The precipitate is formed over an insoluble organic matrix, which acts as a structural mold and contains nucleation sites. The organic matrix, or more specifically their proteins, is intimately associated with the resulting precipitate

The organic matrix is instrumental in defining the spatial configuration of the precipitated material, it contains nutrients and facilitates the communication between bacterial organisms. Therefore, we can define organic matrix as a macroscale environment sustaining metabolism and the extracellular activities leading to the formation of crystals (Ibid). Biofilms on the other hand are formed when bacterial communities are assembled together. The complex relationships taking place inside biofilms has been defined by means of analogy to human cities. Watnick and Kolter describe biofilms as environments in which constituent members cluster together for specific well-defined purposes. They further elaborate: â&#x20AC;&#x2DC;the natural biofilm is less like a highly developed organism and more like a complex, highly differentiated, multicultural community much like our own cityâ&#x20AC;&#x2122; (Watnick and Kolter, 2000, pp.2675-2676). The high differentiation observed in such environments involves a heterogeneous distribution of resources such as nutrients and oxygen (Ibid). Therefore, spatial distribution is a direct consequence of the conditions each microenvironment affords to sustain microbial activities. This trait is highly similar to distribution of inhabitants in any city: each agent decides on its geographical settlement as a function of self-interest to fulfil individual needs. Similarly, cell distribution in biofilms is determined through a space optimisation scheme to allow readily access to nutrients: bacteria distribute themselves according to who can survive best in the particular microenvironment and also based on symbiotic relationships between the groups of bacteria (27, 28, 30). Thus, the bacteria in a multispecies biofilm are not randomly distributed but rather organized to best meet the needs of each. The process of crystal formation is dependant upon chemical reaction brought about by processes initiated by the microbial cells. In the case of bacterially mediated calcium carbonate, the proposed domain for this research, urease is created

â&#x20AC;&#x153;Emergence refers to the properties exhibited by a system when the high-level patterns hold a casualty relationship to the low-level interaction of agents. â&#x20AC;?

as a by-product of bacterial metabolism. Urease changes the pH on the environment, facilitating the production of calcium carbonate on calcium rich environments. In both form generation processes, tissue generation and biomineralisation, a recurring pattern of scaffolding is observed. In order to sustain the tridimensional expression of shape, biological units are supported by transitional structures. In tissue formation, cells create a common protein rigid mesh to support adhesion mechanisms. By contrast, biofilms can be described as dynamic chemical scaffolding to microbial activity. Microbial cells cluster together in communities that sustain crystal formation. It has been suggested (Decho, 2010, p.3) that biofilm offer a starting point in investigating control over the precipitation process, and a consequent manipulation of shape. This section laid the foundation for the implementation of a mediating artifact that allows architects to experiment, understand and further investigate the qualities of designing with synthetically engineered materials. The concept of biofilm and bacterial distribution is central to the proposal of the computational description advanced in this research. Such algorithm simulates the basic interactions within biofilms, accounting for the distribution of bacteria based on an optimal distribution of resources. Although there are a number of more complex symbiotic relationships taking place in actual biofilms, it has been regarded as a sufficient starting point to investigate design by manipulating a dynamic chemical scaffold.

// CHAPTER III

METHODOLOGY chapter iv

This research set out to investigate the qualities involved in designing with synthetically engineered materials. Although there are a number of studies researching on different aspects of form generation in nature, the studies tackling the implication of synthetically biological materials are rare if not inexistent altogether.

Figure 4.1 Computer generated imaged from the particle system experiments

Architecture is necessarily embodied in material practices. Even when its main focus is on creating spaces, which are relevant to the anthropometric scale, it has to be produced through craft. Kenneth Frampton uses this conceptual basis in describing the modern movement in architecture as much related to the conceptualisation of space as it is of the material possibilities of concrete and steel. He chooses the concept of tectonic to condense the notion of the influence of material properties on architecture. The term is carefully picked on the merits of its Greek root to signify an artisan working on all sorts of materials (Frenkel et al., 2001, pp. 2-9). In keeping with the philosophy advanced by Frampton, this research proposes to investigate the qualities of designing with synthetically engineered biomaterials form the standpoint of tectonics. Two properties have been defined in this work to describe architectural tectonics: shape and composition. Shape determines the craft processes of production. For instance, the particular shape of masonry pieces determines production mechanisms such as bond patterns. Composition on the other hand determines the interactivity of materials within the system. For instance, the chemical composition of bricks determines the type of mortar to be used in creating a monolithic structure. Together, both properties determine the shapes we can design. In order to investigate the qualities of designing with synthetically engineered materials, a dual strategy is proposed to explore both material aspects: shape and composition. A computational description, also described as in silico experiment based on the terminology used for this kind of experiments in synthetic biology, is proposed to understand the shape component. In doing so, the algorithm takes inspiration from the concept of natural scaffolding developed in the section Natural processes of form generation. Further, this research included the collaboration with Dr. Meng Zhang, research associate at Department

of Biomedical Sciences, Northumbria University. This experiment, referred as in vitro in consideration to the term applied to lab experiments in synthetic biology, looked at understanding the composition aspect of biological materials in the context of bacterially induced biomineralisation of Calcium Carbonate. The following two sections give a general overview of previous experiences, which use the dual strategy to understand the qualities of materials and processes in the context of biological systems. The first section described a series of experiments conducted by Jenny Sabin, assistant professor in the school of Architecture at Cornell University, in collaboration with Peter Lloyd Jones, Spatial Biologist at University of Pennsylvania. The second section will offer an introduction to the dual evolutionary strategy, a methodology developed in synthetic biology to address the complexity of designing biological systems. In both cases, it will be observed that digital mediating artifacts play a crucial role in codifying understanding of natural systems, aiding in shaping research efforts.

Precedents

Sabin + Jones LabStudio The Sabin+Jones LabStudio is defined as â&#x20AC;&#x2DC;a hybrid architectural-biological research and design unit (...) therein, we are primarily interested in architectural models and design tools that emerge through the study of cells and tissuesâ&#x20AC;&#x2122; (Sabin, 2009, p.54). The research association includes a two-way collaboration between Dr. Peter Lloyd Jones, Professor of Pathology and Laboratory Medicine, and Jenny Sabin, assistant professor of Design and Emerging Technologies Cornell University. The collaboration between architecture and biology has enabled research that looks into the tools and skill architects may offer in the context of biomedical research. Their focus is to leverage the architectural tool set to explore in what

// CHAPTER IV

A dual strategy for ways it could contribute to the diagnosis, treatment and research of lung-related diseases (Popp, 2009). According to Dr. Jones, architects bring a different mentality that contrasts the enclave thinking so frequent in the scientific community. (Ibid) One of their research objectives is set forth as creating models to capture and cultivate the complexity of biological systems, made evident in the dynamic reciprocity of living organisms (Sabin and Jones, 2008, pp. 63-65). When considering their work-process, Sabin (2009) elaborates: ‘demands collaboration and requires participation in two-way dialogues. It favours process driven research over goal driven research. Its vitality is dependant upon intuition or knowing where to look. This clarity comes forth from doing, making, collaborating, and failing’ (Sabin, 2009, p.57). This definition can be viewed as consistent with the research through design approach, whereby design artifacts are employed as an exploration tool to address a subject matter (Dalsgaard, 2010, p.201). The research fostered by Sabin+Jones LabStudio aims to get a better understanding of biological systems by applying architectural tools such as digital tridimensional models, which in the context of this research are known as mediating artifact. For instance, in the Nonlinear systems biology and design research, data was collected from confocal microscopy images (Sabin and Jones, 2008, p.61). Then, biological data is analysed in order to find similes with algorithmic tools used in architecture. In the case of Nonlinear systems biology It was observed that patters emerging from cell connectivity could be loosely described and modelled using a Voronoi-Delaunay algorithm (Ibid). Finally, to address the research problem, a software prototype was developed to study the aforementioned dynamics. (Ibid)

synthetic biology

The main objective of synthetic biology, engineering biological systems, is achieved either through the active modification of existing organisms or by the codification of entirely ‘synthetic’ units. Designing and codifying natural organisms is a paramount challenge. Advances in the field of microbiology have yielded a catalogue of proteins and genes on specific organisms, and each one has been correlated to a specific behaviour. Data is available as biobricks, genetic sequences with defined functions and structures, and put together in registries of standard biological parts (Hallinan et al., 2012). However, it has been observed that the combination of biobricks does not render an additive behaviour. Given its character of dynamic nonlinear systems, the interaction between sequences produces unexpected behaviours, which are nearly impossible to predict (Endy, 2008). This is, modifying one variable has an impact on the performance of other individual variables, and of the general system in result. Given this complexity, different strategies have been devised to simulate and design biological systems. Although the area of bioinformatics have managed to produce fairly advanced simulation models of existing organisms, it is still necessary to design genetic circuits under a series of simplifying assumptions. To address this, Hallinan et al have proposed dual evolutionary strategy. Under this model, a first design prototype is codified and simulated in a computational modelling language. Traits yielded by individual proteins are taken into consideration to predict their interaction and impact on the final design. It is nonetheless expected that this simulation will differ from the one observed when the actual organism is fabricated in the lab settings. Therefore, computational simulations and actual experiments are run in parallel. Patterns observed on the lab experiments inform and modify the simulation. The process is iterative in nature. Findings in each strand inform decisions and modification that take place on the other one.

Figure 4.1 Dr. Meng Zhang during the bacterially induced biomineralisation expreiments

// CHAPTER IV

It is expected that the dual strategy will enable synthetic biologist to learn how actual organisms behave, and inform the design process through the computational reasoning harnessed. As analysed in the introduction to this work, architecture finds itself in a similar position. The debate over a synthetic biology paradigm in architecture has been dominated by aesthetic speculations over design process that does not hold any resemblance to actual biological processes. Designing structures that depend upon the behaviour of natural organisms is a paramount challenge. It involves understanding the possibilities of such systems in a number of levels that are yet poorly understood. Therefore, the methodology followed in this research is modelled after processes, which are already followed in synthetic biology to understand how biological units behave under different conditions.

IN-SILICO SIMULATION. AGENT BASED PARTICLE SYSTEM chapter v

Methodology A computational description was devised to investigate the shape aspects of biologically engineered materials. As described in the previous chapter, shape control in natural system is created through the dynamic formation of scaffolding systems. Specifically, it was sought that such software prototype will encode a dynamic scaffolding system, featuring two main elements. First, a particle system that would represent cell distribution inside the matrix. Second, elements that will attract cells by means of chemical affinity. The concept of attractors was developed after analysing properties of natural biofilms described by Watnick and Kolter (2000) regarding its heterogeneous composition. According to this principle, concentration of oxygen and nutrients is more concentrated in specific areas of the biofilm, therefore affecting the distribution of microbial cells. As a general rule, bacteria will spatially allocate to maximise access to nutrients. Following these principles, a design strategy was devised to create a system with control over the spatial position and specific properties of such attractors. Further, cell distribution will serve to simulate patterns of crystal formation around the local chemical focus created by bacteria. The simulation was created in two discrete algorithms. The first one, scripted in Processing, simulates the interactional mechanisms of cells. The second, scripted in Grasshopper, takes the cell distribution from the first algorithm and generate crystals in the surrounding of each agent. The approach of dividing the computational description was chosen to allow efficiency in the simulation process. Initially, two programming environments were assessed: Processing and Grasshopper. Processing is an open-source programming language initially developed at MIT to allow designers and artists to gain software literacy, therefore enabling them to create their own exploration tools. The core language is based on Java and shares some elements with C language series (Reas et al., 2007, pp.2-6). Grasshopper on the other hand is a visual programming environment that runs as an extension of McNeel Rhinoceros 3D. It allows users to create scripts by means of visually assembling and connecting modules. After some initial informal exploration by the researcher, it was determined that Processing allowed a faster handling of large datasets and offered greater control on the visual interface of the intended digital mediating artifact. On the other hand, Grashopper offered a more reliable 3D environment, along with the possibility of

exporting geometry to any format for rendering purposes. Therefore, it was decided to combine both to achieve an optimal workflow. The first simulation runs a particle system based on local interaction affected by the forces exerted by attractor elements. Once the simulation is completed, it is possible to export cell distribution as a list of coordinates in CSV format. Once cell distribution is allocated, it is passed through to the grasshopper component loads the coordinates and creates create clusters of crystals around each cell.

Processing prototype

The simulation scripted in processing is modelled after the structure of a physics driven particle system based on Boids rules and optimised through Verlett physics integration. In this section, it will be discussed the motivation for choosing this particular arrange of techniques, as well as give a detailed account on the general implementation structure.

Algorithmic structure Different algorithms have been devised to integrate mathematical abstraction of life-like behaviour, specially emergent properties of self-organising and self-assembly systems. Particle based systems are described as a literal translation of properties exhibited by emergent systems (DeLanda, 2011). Under this algorithm, simple interactivity instructions are given to autonomous agents. The system is then iteratively calculated, giving new positions for each agent as a result of local interactions with other agents. (Pearson, 2011). Particle based systems provide an efficient way to codify individual behaviours to a large set of autonomous agents. In the algorithm described in this section, each agent is embedded with steering behaviours that takes into consideration the position of neighbour bacteria to allign its own direction, therefore giving them peer awareness. Moreover, a number of attractor acts as chemical agents, which causes the colony of bacteria to follow a particular stream, as if looking for the best possible location to find oxygen and nutrients. The combination of such elements allows the system to exhibit selfassembly properties.

// CHAPTER V

Steering behaviours are codified using Boid rules. Originally devised by Craig Reynolds in 1987, Boids rules are used to describe particle systems found in nature, specially flocking behaviours. The basic implementation of boid rules includes a set of three behaviours: (1) Separation, the condition to keep separated from other particles, (2) Alignment, an average direction based on neighbours’ state, (3) Cohesion, an immediate reaction in position based on neighbours’ direction (Pearson, 2011, p.109). Mathematically speaking, each particle is treated as a vector continuously changing position. Consequently, a particles system needs to integrate equations to calculate the new vector positions accounting for the current position and the modifications conditions integrated for each particle. The particular process to perform this calculation has an important impact on computational resources of the simulation. One of the most stable and efficient integration methods is known as Verlet Integration. Given the fact that new particles’ position is calculated taking into consideration the basic variables of velocity and acceleration, Verlet Integration dynamically calculates velocity based on the current position of each particle. In this manner, each particle does not store its own velocity, which may lead into the cumbersome process of updating an extra database at each iteration (Shiffman, 2011). The prototype referred in this section uses Toxiclibs components to create a particle system based on Verlet Integration and Boid behaviours. Toxiclibs is a library for the Processing environment that allows an efficient Verlet integration for particle systems.

Implemented modules in Processing The following lines will describe the specific mechanisms implemented in the programme, as well as give account for its motivation as observed in the in-vitro series. The ideas presented here should be treated as a brief review as they will be analysed in greater depth on the discussion chapter.

• Dynamic control of number of cells. Throughout its living cycle, each cell will come to a point of duplication. This behaviour is codified as an increment in the number of cells. Each new cell will be placed in the same coordinates as the existing ones.

• Dynamic control of separation distance between cells. It is observed throughout the in-vitro experiments that cells will determine its position to their neighbours depending on chemical conditions in the environment. The module of distance between cells was implemented as an implementation of this complex behaviour.

• Manual control over position of attractor.

Attractors stand for chemical substances, which are relevant to the cells life cycle. In some cases, such chemicals are used in the metabolic functions, or will trigger the expression of a specific behaviour. The manual control in the simulation is introduced as a design mechanism. It is speculated that the manufacturing process associated to biological systems may include a chemical scaffold to control the morphology of deposited material.

• Independent control over area of influence

for every attractor. The area of influence is conceptualised as a virtual sphere in which each attractor has influence over cells.

• Independent control of strength for every

attractor. The strength determines the acceleration that each cell takes when being influenced by the attractor. Chemically, this variable stands for affinity between cell and attraction substance.

• Random walk. It is observed that every cell

present random walk in aqueous environments. Although the solution used in the in-vitro series, Agar composite, restrain random walk in cells.

• Reset of all cells’ position.

• Stop condition through counter. As concluded

from the In-vitro experiments, time plays an important role in each experiment. Crystal morphology is different each day throughout the experiment, which is due to the continuous catalysing bacteria action. Therefore, it is expected that one of the main design mechanism is a kill switch in the bacteria, allowing controling the morphology of the crystals which are formed. In further in-vitro iterations, such switch could be integrated either as the absence of nutrients, or even as the introduction of a new substance.

// CHAPTER V

User interface

Figure 5.1 Graphic user interface developed for the particle system

In the early stages of development, variables were changed directly in the code to experiment with their impact on the overall system. However, this technique proved to be confusing and prone to error. As a solution, a graphic user interface was implemented to run in a secondary window. The library controlP5 for Processing was used to handle graphic elements such as sliders and buttons

Figure 5.2 Particle system running with tree attarctors

Figure 5.3 Particle system running with one attractors

Figure 5.4 Particle system running with two attractors

// CHAPTER V

â&#x20AC;&#x153;The grasshopper algorithm was designed to simulate crystal clustering around chemical centres created by bacteria.â&#x20AC;?

Development roadmap This software prototype was created whilst the in-vitro experiments were performed. Observations and conclusions drawn from the lab-setting experiments informed the structure and modules for the final algorithm. Given this iterative process, a roadmap was followed to incrementally implement new behaviours and modules. Following, a description of such stages: First stage. The particle system allowed running only one attractor. The modules of separation and random walk were integrated and tested in conjunction and separately. Second stage. The system now integrates multiple attractors with random positions. Each one of them holds the same values for Area of Influence and Strength. Third stage. Each attractor is codified to have an independent value of strength and area of influence. Fourth stage. Attractorâ&#x20AC;&#x2122;s positions are now manually controlled through a twodimensional slider in the user interface. Fifth stage. The system integrates a counter, which gives the possibility to control life cycles.

Experiments A series of experiments were run to asses the impact each variable has over the overall system. The following lines give an account of the protocol observed: The first series of experiments observed variation over a one-attractor particle system. Different values for Area of Influence and Strength were assessed.

A second series of experiments manipulated values for area of influence and strength on a two-attractor system. Positions for each attractor were manipulated manually. The third, fourth and fifth series of experiments were based on particles systems with three, four and five attractors respectively. The previous experiments shed light on how each parameter affected the distribution of cells independently. Knowledge gained is used in this last series of experiments to develop more detailed and complex shapes. Different connections and affordances of the system were explored and analysed to generate a shape catalogue.

Algorithmic

structure in Grasshopper The grasshopper algorithm was designed to simulate crystal clustering around chemical centres created by bacteria. Under this premise, bacteria distribution found in the processing module is exported as a point cloud using comma-separated protocol. Files are loaded in Grasshopper and passed on to a string data component. After, a Split component is used to recognise each coordinate as an individual element. Then, a Transpose component builds a matrix to allow an easier data handling from this point on. Once inside the matrix, data is distributed in three branches, each one standing for X, Y, Z coordinates of each cell. Each strand is separated and then conflated into a vector type.

The vector type array is then expanded to simulate the production of multiple crystals around the same bacteria. As described in section Part 1 of this dissertation, the production of crystals in biofilms is dependant upon chemical conditions created in the microenvironment surrounding bacteria. Therefore, each bacteria allocated in the processing simulation, is used as the focus to simulate the creation of a predefined number of crystals (nc) within a sphere of influence. In order to enact this process, the type vector array inherited from the CSV file is expanded to accommodate nc positions per each allocated bacteria. The expanded dataset is used to generate random position vectors relative to each bacterium. Further, these vectors are combined with the original bacteria coordinate to generate a nc number of crystals around each bacteria. Finally, each crystal is represented using a sphere. The radius for each sphere is randomised to simulate the variation observed in the creation of crystals in natural biofilms.

Figure 5.5 and 5.6 show the algorithmic structure develped in grasshopper to simulate crystal clustering

// CHAPTER V

Findings

This section presents results from the experiments carried out using the digital mediating artifact developed This section presents results from the exp in this research. Results are classified in five categories, following the same system presented in the previous artifact developed in this research. Results section and shown as shown as Computer Generated Imagery. Images shown on this section were created system presented in the previous section a importing the resulting simulation of crystal formation into Cinema 4D. 3D models were rendered using VRay Imagery. Images shown on this section we for C4D rendering engine. crystal formation into Cinema 4D. 3D mod engine. Results are presented with the following structure. First, a reference table is presented outlining values used in the cell distribution simulation. Each experiment presented is codified using a unique reference name. Then, Results are presented with the following st images are presented in a shape catalog. Finally, a description is presented outlining thevalues conclusions atthe each outlining used in cell distribution iteration. using a unique reference name. Then, ima

description is presented outlining the conc

Single attractor series

Variations over a one-attractor particle system

Variations over a one-attractor particle sys

AT1-A

ts results from the experiments carried out using the digital mediating n this research. Results are classified in five categories, following the same n the previous section and shown as shown as Computer Generated own on this section were created importing the resulting simulation of o Cinema 4D. 3D models were rendered using VRay for C4D rendering

ed with the following structure. First, a reference table is presented d in the cell distribution simulation. Each experiment presented is codified ence name. Then, images are presented in a shape catalog. Finally, a nted outlining the conclusions at each iteration.

eries

e-attractor particle system

Iterations

183

Number of cells

900

Separation distance

50.22

COORDINATES

AREA OF INFLUENCE

ATTRACTOR STRENGTH

-400,400,400

750

NUMBER

0.22

demonstrates the influence of attractors over cells distribution. The spread each attractor generates a area of influence following the approximate phere. 42 The Area of Influence variable determines the radius of the sphere he attractor strength determines how fast each cell is pulled towards the

NUMBER

AT1-A experimentdemonstrates demonstrates the influe AT1-A experiment of shows each over attractor generate thecells influence of that attractors shape of a perfect sphere. cells distribution. The spreadThe of Area of Infl of influence, whilst the attractor strength d cells shows that each attractor attractor. generates a area of influence following the approximate shape of a perfect sphere. The Area of Influence variable determines the radius of the sphere of influence, whilst the attractor strength determines how fast each cell is pulled towards the attractor. Two attractors series

ATTRACTOR

Two attractors series

First serie featuring more than one attractor. Variation was made to asses the impact of each value on the combined area of influence generated by the system of attractors.

First serie featuring more than one value on the combined area of influ

AT2-A

ATTRA

171

706

Iterations Number of

cellsthe impact of each First serie featuring more than one attractor. Variation was made to asses value on the combined area of influence generated by the system ofSeparation attractors.

50.22

distance

AT2-A

ATTRACTOR NUMBER

COORDINATES

AREA OF INFLUENCE

ATTRACTOR STRENGTH

AT2-B

Iterations 171 700 First serie featuring more than one attractor.0Variation364,293,186 was made to asses the impact of each 5 value on theNumber combined of area of influence generated by the system of attractors.Iterations 300 1 426,488,604 700 5 706 cells Separation distance

AT2-A

50.22 ATTRACTOR

Number of cells

COORDINATES

AREA OF INFLUENCE

364,293,186

700

Number of COORDINATES 1 to NUMBER 426,488,604 706 aturing more than one attractor. Variation was made asses the impact of each 700 cells combined area of influence generated by the system of attractors. Separation Iterations 300 0 364,711,275 50.22

AREA OF INFLUENCE 5

NUMBER

171

Iterations

AT2-B

ATTRACTOR

2-A 171

Number of cells

COORDINATES NUMBER Separation

AT2-B 0

50.22

Number of cells

NUMBER

700 364,711,275 5

AT2-C

426,133,604 700 ATTRACTOR COORDINATES NUMBER

706

1600

Number of 706 cells COORDINATES

ATTRACTOR

INFLUENCE

Separation 50.22 distance 364,711,275

A AT2-C 1 426,133,604

1 AREA OF INFLUENCE 305

TTRACTOR NUMBER

305

NUM

50.22

5 AT2-C 5

ATTRA

NUM

1600

Number of Acells TTRACTOR

706

STRENGTH Separation distance

50.22

AREA OF 5 ATTRACTOR INFLUENCE STRENGTH The pattern observed in AT2-A sho

364,293,186 A426,480,604 TTRACTOR STRENGTH

ATTRA

ATTRACTOR STRENGTH

Iterations

AREA OF

ATTRACTOR

700

ATTRACTOR STRENGTH

426,488,604 300

Separation 50.22 distance Iterations

NUMBER

426,133,604

50.22 ATTRACTOR 364,293,186 700 COORDINATES5

distance

1 Iterations

300

AREA OF INFLUENCE

ATTRACTOR

706

2-B

706

Separation STRENGTH distance

305

distance

NUMB

AREA OF INFLUENCE

that each other. Dis 350do not overlap 10.15 showing two nearly perfect spheres attractor exhibits a5slight perturbati 700 attractor. This perturbation is made impression of being pulled by the o ATTRACTOR

AT2-B shows a consistent behavio STRENGTH 700 5 706 pulled by attractor 1, which is confi Iterations 1600 example illustrates a phenomenon 0 364,293,186 350 10.15 The pattern observed in AT2-A shows cell distribution for two attractors with influence areas 0 and 1 influence area for attractor 50.22 Numberthat of do not overlap each other. Distribution is consistent with that of the previous series, 1 426,480,604 700 5 706 results in an emergent shape. The cells showing two nearly perfect spheres attracting a similar number of cells. However, each Separation attractor50.22 exhibits a slight perturbation in the microenvironment adjacent to the opposing distance attractor. This perturbation is made evident in the asymmetrical distribution of cells, giving the // CHAPTER V impression of being pulled by the other attractor. 43

2-C

ATTRACTOR NUMBER

COORDINATES

AREA OF

ATTRACTOR

AT2-A

ATTRACTOR

NUMBER

Iterations

171

364

Number of cells

706

426

ATTRACTOR

300 5

Number of cells

706

426

Separation distance

50.22

ATTRACTOR

1600

364

Number of cells

706

426

Separation distance

50.22

First serie featuring more than one attractor. Variation was made to asses the impact of each value on the combined area of influence generated by the system ofSeparation attractors. 50.22 distance

AT2-A

ATTRACTOR

COORDINATES

AREA OF INFLUENCE

NUMBER

AT2-B 5

Iterations

171

364,293,186

700

Number of cells

706

426,488,604

Iterations 700

Separation distance

50.22

AT2-B

ATTRACTOR

COORDINATES

AREA OF INFLUENCE

NUMBER

Iterations

300

364,711,275

305

Number of cells

706

426,133,604

700

Separation distance

50.22

AT2-C

ATTRACTOR

COORDINATES

NUMBER

Iterations

1600

364,293,186

Number of cells

706

426,480,604

Separation distance

50.22

ATTRACTOR STRENGTH

5 AT2-C

Iterations

NUMBER

AREA OF ATTRACTOR INFLUENCE STRENGTH The pattern observed in AT2-A shows cell distr

that do not overlap each other. Distribution is c 350 10.15 showing two nearly perfect spheres attracting a attractor exhibits a slight perturbation in the mic 700 5 attractor. This perturbation is made evident in t impression of being pulled by the other attracto

AT2-B shows a consistent behaviour with the p pulled by attractor 1, which is configured with t example illustrates a phenomenon of reciproca The pattern observed in AT2-A shows cell distribution for two attractors with influence areas 0 and 1 overlap, the influence area for that attractor The pattern observed in AT2-A shows cell distribution for two attractors with influence areas do not that do not overlap each other. Distribution is consistent with thatresults of the previous series, in antwo emergent shape. The final shape is overlap each other. Distribution is consistent with that of the previous series, showing nearly perfect showing two nearly perfect spheres attracting a similar number of cells. However, each spheres attracting a similar number of cells. However, each attractor exhibits a slight perturbation in the attractor exhibits a slight perturbation in the microenvironment adjacent to the opposing microenvironment adjacent to the opposing attractor. This perturbation is made evident in the asymmetrical attractor. This perturbation is made evident in the asymmetrical distribution of cells, giving the distribution of cells, giving the impression of being pulled by the other attractor. impression of being pulled by the other attractor. AT2-B shows a consistent behaviour with the previous observation. Cells are predominately pulled by AT2-B shows a consistent behaviour with the previous observation. Cells are predominately attractor which is configured twice Area of influence as attractor 0. Thisas example illustrates pulled1,by attractor 1, whichwith is configured with twice Area of influence attractor 0. Thisa phenomenon of reciprocal stimulation between both attractors. As influence area for attractor and 1 example illustrates a phenomenon of reciprocal stimulation between both attractors. 0As overlap, the combined area of influence for the system results in an emergent shape. The final shape influence area for attractor 0 and 1 overlap, the combined area of influence for the system is determined interaction between values: area of influence, by attractor strength and coordinates. results inbyanthe emergent shape. Thethe final shape is determined the interaction between the The difference in distribution between AT2-B and AT2-C seems to suggest that when the difference in values between attractor is higher, the resulting shape will resemble more the sphere of influence of the more weighted attractor. The observed behaviour of reciprocal stimulation will be tested in the following experiments by including

values: area of influence, attractor strength and coordinates. The difference in distribution between AT2-B and AT2-C seems to suggest that when the difference in values between attractor is higher, the resulting shape will resemble more the sphere of influence of the more weighted attractor.

Three attractors The observed behaviour ofseries reciprocal stimulation will be tested in the following experiments Variations on a three-attractor by including more attractors.system. Four experiments were conducted to test the influence of different values on the reciprocal stimulation of attractors’ area of influence.

Three strength attractors series of influence, attractor and coordinates. The difference in distribution 2-B and AT2-C seems to suggest that when the difference in values between gher, the resulting shape on willaresemble more the sphere of influence of the more Variations three-attractor system. Four experiments were conducted to test the actor. influence of different values on the reciprocal stimulation of attractors’ area of influence.

d behaviour of reciprocal stimulation will be tested in the following experiments more attractors.

AT3-A

ctors series

ATTRACTOR

COORDINATES

AREA OF INFLUENCE

186,115,480

770

NUMBER

192

Iterations Number of

703 400,391,382 a three-attractor system. Four experiments were1 conducted to test the cells different values on the reciprocal stimulation of attractors’ area of influence. Separation 50.22 2 595,702,364 distance

3-A 192 703 50.22

3-B

315 780

ATTRACTOR values: area of influence, attractor STRENGTH between AT2-B and AT2-C seem attractor 5 is higher, the resulting sh weighted attractor. 5 The observed behaviour of recipro by including more attractors. 5

A A A A A Three attractors series C C AT3-B S and coordinates. I S area of influence,I attractor strength The difference in distribution

ATTRACTOR

NUMBER values:

OORDINATES

REA OF TTRACTOR TTRACTOR OORDINATES NUMBER NFLUENCE TRENGTH

REA OF NFLUENCE

TTRACTOR TRENGTH

between AT2-B and AT2-C seems to suggest that when the difference in values between 254 186,115,480 770shape 5 0 133,186,480 750 5 on a three-attractor Variations attractor is higher, the resulting will resemble more the sphere of influence of the more sys influence of different values on the weighted attractor. Number of 801 1 400,391,382 315 426,666,622 5 1 604.99 5 cells Iterations 0

The observed behaviour of reciprocal stimulation Separation 37.86 more attractors. 2 595,702,364 5 2 780 586,328,207 distance by including ATTRACTOR

AREA OF Cattractors OORDINATES seriesINFLUENCE

NUMBER Three

254

801

37.86

ATTRACTOR STRENGTH

AT3-A

Iterations

ATTR

NUM

192

Number of

703 133,186,480 750 5 cells Variations on a three-attractor system. Four experiments were conducted to test the Separation influence of different values on the reciprocal 5 stimulation of attractors’ area of influence. 426,666,622 604.99 50.22 distance

586,328,207

AT3-A

515

ATTRACTOR

COORDINATES

NUMBER

A AT3-B S

AREA OF

INFLUENCE

Iterations

186,115,480

770

Number of cells

703

400,391,382

315 cells

Separation distance

50.22

595,702,364

distance 780

ATTRACTOR

COORDINATES

AREA OF INFLUENCE

ATTRACTOR STRENGTH

Separation

NUMBER

NUM

254

192

Number of

ATTR

TTRACTOR TRENGTH

Iterations

AT3-B

// CHAPTER V

will be tested in the following experiments 515 5

801

37.86

Iterations

254

133,186,480

750

Number of cells

801

426,666,622

604.99

Separation distance

37.86

586,328,207

515

AT3-C

ATTRACTOR

COORDINATES

AREA OF INFLUENCE

ATTRACTOR STRENGTH

NUMBER

Iterations

304

133,186,480

750

2.80

Number of cells

801

426,666,622

645

2.80

Separation distance

37.86

586,328,207

560

2.80

C AT3-D

ATTRACTOR

304

Iterations 0

370 133,186,480

750 133,186,480 2.80

750

2.80

801

Number of 1 cells

801 426,666,622

645 426,666,622 2.80

750

2.80

7.86

Separation 2 distance

37.86 586,328,207

560 586,328,207 2.80

750

2.80

NUMBER

ATTRACTOR NUMBER

AREA OF ATTRACTOR AREA OF ATTRACTOR COORDINATES NUMBER INFLUENCE STRENGTH INFLUENCE

OORDINATES

COORDINATES

AREA OF INFLUENCE

ATTRACTOR STRENGTH

AT3-C

Iterations

304

Number of

ATTRACTOR NUMBER

801 1 This series of experiments explores the750 specific influence each parameter bear on cells shaping 0 133,186,480 2.80 the overall area of influence for the system. AT3-A provides a simple example ofSeparation mutual 2 interaction between attractors. In order750 to asses the specific influence in 37.86 801 1 426,666,622 2.80 influence of Area ofdistance the system, attractors are position roughly in a straight line and Attractor strength values kept equally2constant586,328,207 at 5. It is observed that even when the Area of influence value for attractor 1 7.86 750 2.80 ATTRACTOR AREA OF ATTRACTOR is assigned the lowest value in the system (315), it is observed a greater concentration of ATTRACTOR COORDINATES NUMBER NUMBER INFLUENCE STRENGTH cells in the microenvironment surrounding the central attractor. This pattern is possibly due to Iterations 370 the mutual stimulation which overlaps in the same spatial 0 Iterations of the three 304 areas of influence, 0 133,186,480 750 2.80 position as attractor 1. Number of Number of 801 1 801 1 426,666,622 645 cells 2.80 cells influence each parameter bear on shaping iments explores the specific AT3-B, AT3-C and AT3-D share attractors positioned on the same coordinates, creating a nfluence for the system. AT3-A provides a simple example of mutual Separation to further evaluate the impact of each parameter on theSeparation controlled environment system. For 2.80 37.86 2 37.86 586,328,207 560 distance n attractors. In order to distance asses the specific influence of 2 Area of influence in instance, the evidence presented by AT3-B as compared to AT3-C suggests that Attractor ors are position roughly in a straight line and Attractor strength values kept Strength leads to dense distribution of cells. AT3-B is configured with a Attractor strength of 5. It is observed that even when the Area of influence value for attractor 1 5 and creates a distribution similar to that of a triangle. In comparison, AT3-C with AREA OFAttractor ATTRACTOR ATTRACTOR est value in the system (315), it is observed a greater concentration of COORDINATES NUMBER INFLUENCE strength set on 2.80 distributes cells in a manifold-based shape. This explanation seems toSTRENGTH vironment surrounding the central attractor. This pattern is possibly due to be consistent with findings in experiment AT3-C. When both values, area of influence and This series of experiments explores the on of the three areas ofIterations influence, which overlaps in the same spatial 370 0 throughout 133,186,480 750 2.80 attractor strength, are set to consistent values all attractors, it is observed a area moreof the overall influence for the syste r 1. symmetrical distribution. Resulting shape can be described as being an intermediate state Number of interaction between attractors. In order 801 1 426,666,622 750 2.80 cells between a triangle and a manifold-like structure. the system, attractors are position rough AT3-D share attractors positioned on the same coordinates, creating a equally constant at 5. It is observed that Separation ment to further evaluate the impact of each on the system. For 37.86parameter 2 586,328,207 750 2.80 distance is assigned the lowest value in the syste nce presented by AT3-B as compared to AT3-C suggests that Attractor Four and more attractors series cells in the microenvironment surroundin ense distribution of cells. AT3-B is configured with a Attractor strength of the mutual stimulation of the three areas tribution similar to that of a triangle. In comparison, AT3-C with Attractor position as attractor 1. 0 distributes cells series in a manifold-based This of explanation The final of experimentsshape. comprises variations seems on fourto and five attractors systems. This series of experiments explores the specific influence each parameter bear on shaping the overall area of findings in experiment AT3-C. When both values, area of influence and series ofAT3-A experiments the specific influence each parameter bear on shaping influence forThis the system. providesexplores a simple example of mutual interaction between In order toshare attracto AT3-B,attractors. AT3-C and AT3-D are set to consistentthe values throughout all attractors, is observed a more overall area of ofArea influence for theitinsystem. AT3-A provides aposition simple example of mutual asses the specific influence of influence the system, attractors are roughly in a straight line controlled environment to further evalua ution. Resulting shape can be described as being an state interaction between In intermediate order to Itasses the specific influence of Area of influence in and Attractor strength values keptattractors. equally constant at 5. is observed that even when the Area influence instance, theof evidence presented by AT and a manifold-like structure. the system, attractors are position roughly in a straight and Attractor strength values kept value for attractor 1 is assigned the lowest value in the system (315), it isline observed a greater concentration Strength leads to dense distribution of c equally constant at surrounding 5. It is observed that even when thepattern Area of value for attractor 1 similar to tha of cells in the microenvironment the central attractor. This is influence possibly due to the mutual 5 and creates a distribution is assigned the lowest value in the system (315), it is observed a greater concentration of strength set on 2.80 distributes cells in a tractors series cells in the microenvironment surrounding the central attractor. This pattern is possibly due toin experimen be consistent with findings the mutual stimulation of the three areas of influence, which overlaps in thestrength, same spatial attractor are set to consistent position as attractor 1. xperiments comprises of variations on four and five attractors systems. symmetrical distribution. Resulting shap between a triangle and a manifold-like s AT3-B, AT3-C and AT3-D share attractors positioned on the same coordinates, creating a controlled environment to further evaluate the impact of each parameter on the system. For 46 instance, the evidence presented by AT3-B as compared to AT3-C suggests that Attractor Four and more attractors series

370

AT3-C

AT3-D

stimulation of the three areas of influence, which overlaps in the same spatial position as attractor 1. AT3-B, AT3-C and AT3-D share attractors positioned on the same coordinates, creating a controlled environment to further evaluate the impact of each parameter on the system. For instance, the evidence presented by AT3-B as compared to AT3-C suggests that Attractor Strength leads to dense distribution of cells. AT3-B is configured with a Attractor strength of 5 and creates a distribution similar to that of a triangle. In comparison, AT3-C with Attractor strength set on 2.80 distributes cells in a manifold-based shape. This explanation seems to be consistent with findings in experiment AT3-C. When both values, area of influence and attractor strength, are set to consistent values throughout all attractors, it is observed a more symmetrical distribution. Resulting shape can be described as being an intermediate state between a triangle and a manifold-like structure.

Four and more attractors series

The final series of experiments comprises of variations on four and five attractors systems.

ATF-A

Iterations

296

Number of cells

703

Separation distance

72.53

ATTRACTOR

COORDINATES

AREA OF INFLUENCE

ATTRACTOR STRENGTH

NUMBER

Iterations

296

568,794,577

510

7.35

Number of cells

703

151,746,373

615

8.40

Separation distance

72.53

311,257,115

687,408,426

865

Number of cells

Separation distance

ATF-B Iterations

200

ATTRACTOR NUMBER

NUM

ATF-B 740 6.12 Iterations

// CHAPTER V

ATTR

NUM

200

5.0 700 35.54

COORDINATES

AREA OF INFLUENCE

ATTRACTOR STRENGTH

400,350,400

650

7.87

Separation distance

ATF-A

NUMBER

INFLUENCE

510

7.35

Number of cells

703

151,746,373

615

8.40

Separation distance

72.53

A 2 311,257,115 ATF-B

TTRACTOR NUMBER740

COORDINATES 6.12

ATF-B ATTRACTOR NUMBER

200 687,408,426

865 400,350,400 5.0

650

Number of cells

700

204,266,400

255

Separation distance

35.54

204,533,400

700

533,266,391 ATTRACTOR

170

ATTRACTOR AREA OF ATTRACTORAREA OF COORDINATES INFLUENCE INFLUENCE STRENGTH

Iterations

296 Iterations 0

200 568,794,577 0

510 400,350,4007.35

Number of cells

703 Number of 1 cells

700151,746,373 1

615 204,266,4008.40

255

5.07

Separation distance

72.53Separation 2 distance

311,257,115 35.54 2

TTRACTOR NUMBER700

COORDINATES 5.60

A 740 204,533,4006.12 ATF-C

687,408,426 Iterations

568,794,577

510

703

151,746,373

615

2 311,257,115A ATF-B

TTRACTOR 740 NUMBER

Iterations 3 Number of cells Separation distance

ATTRACTOR NUMBER

700

Iterations

Number of cells 35.54 Separation distance

COORDINATES

400,350,400

700

204,266,400

Number of cells

Separation distance

Separation distance 8.40

COORDINATES 6.12

204,533,400 43.27

STRENGTH

533,533,400 650 7.87

5.07

TTRACTOR NUMBER700

COORDINATES 5.60

850

43.27

533,533,400

AREA OF

ATTRACTOR

7.87

400,240,400

5.07

350

266,533,533 ATTRACTOR

350

533,533,266

350

5.07

266,533,533

350

5.07

533,533,533

350

5.77

655

5.07 5.77

5.07

266,533,266 5.77

350

5.07

533,533,266

350

5.07

350

5.07

350

5.77

3 AREA OF 266,533,533 ATTRACTOR

3000

400,240,400

850

266,533,266

350

5.07

43.27

533,533,266

350

5.07

266,533,533

350

5.07

533,533,533

350

5.77

INFLUENCE

STRENGTH

533,533,533 494.99 5.07

STRENGTH

5.07

170

494.99

533,533,266

350

5.60

AREA OF INFLUENCE

350

266,533,266

700

655

266,533,266 5.77

533,533,533 494.99 5.07

494.99

COORDINATES

655

ATTRACTOR STRENGTH

ATTRACTOR NUMBER

43.27

170 400,240,400 5.07

AREA OF INFLUENCE

170 400,240,400 5.07 655

255

3 AREA OF 533,266,391 ATTRACTOR

850

204,266,400

255

3000 533,266,391

5.00

533,533,400

650

850

533,266,391

5.0

3000

865 3000

STRENGTHAREA OF ATTRACTOR INFLUENCE COORDINATES NUMBER INFLUENCE

865

A 2 204,533,400 ATF-C

7.35 4

INFLUENCE

200

Iterations

ATTRACTOR 3 STRENGTH Number of cells

ATF-C 0 400,350,400

200 687,408,426

STRENGTH

533,533,400 650 7.87

NUMBER

AREA OF INFLUENCE

COORDINATES NUMBER

296

865

ATTRACTOR STRENGTH

568,794,577

AREA OF INFLUENCE

TF-C

687,408,426

AREA OF

COORDINATES

35.54

740

296

ATTRACTOR

TF-B

3 COORDINATES

311,257,115

Iterations

ATF-A

72.53

ATTRACTOR

Iterations 3

TF-A

72.53

350

The previous series of experiments shed light on the specific impact of each parameter on the overall system. It has been suggested shape qualities brought about by the values set for each parameter, giving some clues as different strategies to negotiate form generation under this system. Therefore, this series of experiments were thought to expand knowledge on this area. Each experiment aimed at reproducing predefined shapes. ATF-A aimed at reproducing a quadric surface using four attractors. Values for each attractor were interactively tuned to approximate the desired shape. ATF-B and ATF-C used a five attractor system to reproduce more complex shapes. In the case of ATF-C, it was intended a cell distribution akin to a pyramid. It is observed during the course of these experiments that there are two crucial periods in the manipulation of systems with a high numbers of attractors. The first moment is finding an interesting shape through the manipulation of different variables. The second challenge is to keep the shape as constant as possible. Given the nature of dynamic non-linear system, the simulation is continuously evolving. Therefore, it is difficult to attain sharp pre-defined shapes. These sections have outlined some of the initial considerations that stems from working with a mediating artifact that is designed to emulate the dynamic, non-linear nature of natural systems. As suggested in the introduction to this chapter, the digital artifact used in this series of experiments is intended to simulate cell distribution and the ensuing crystal formation. The design process of such systems differs fundamentally from the prescriptive methods associated with current architectural design, as suggested in the introductory sections of this research. The following chapter will outline the in-vitro experiments that were carried out to gain a better understanding on the composition of synthetically engineered biological materials. After results from this series are presented, it will be discussed the relationship between both laboratory and digital experiments. It is expected that conflating both experiments will offer some insights into the qualities of designing architecture under a material paradigm based on biological systems.

// CHAPTER V

IN-VITRO EXPERIMENTATION chapter vi

Materials and methodos

Figure 6.1 Detailed view of an optical microscope in the laboratory

The set of experiments described in this section were designed to replicate microbiological precipitation of calcium carbonate and performed in collaboration with Dr. Meng Zhang, research associate at Department of Biomedical Sciences, Northumbria University. As stated before precipitation refers to the chemical process whereby a solid substance emerges out of the combination of two solutions (Zumdahl and DeCoste, 2012, p.106) .In natural contexts precipitation allows the production of inorganic rigid structures, specially through the formation of minerals. For instance, in biological systems minerals are used in the formation of skeletal structures, generally through a crystallisation process based on calcium-based minerals such as calcium carbonate and calcium phosphate (Addadi and Weiner, 1992, p.153). However, the breadth of naturaloccurring generation of minerals extends well into the realms of geological formations, soils, oceans, saline lakes among others (Hammes et al., 2003, p.4901). Organisms have developed a number of control processes to direct mineralisation. One of the most common, microbial methods, are of special interest to synthetic biology to design biologically based materials (Addadi and Weiner, 1992, p.153). Theoretically, bacterial organisms can be genetically modified to control the specific pattern in which minerals are formed. In turn, mineralisation patterns determine some specific material properties such as structural capacity and surface finish. In the introduction to this work, it was stated that laboratory work would allow a material engagement with biologically engineered materials. The laboratory experiments presented in these sections provide an insight into one of the first processes of bioengineered materials. Also, it provides a first-hand experience into the complexities of designing and implementing biological materials. The set of laboratory experiments referred in this research set out to study biologically mediated precipitation of Calcium Carbonate (CaCO3) using Sporosarcina Pasteurii, a microbial organisms formerly referred to as Bacillus Pasteurii. Bacillus Pasteurii facilitates the precipitation of

calcium carbonate through the production of Urease, which is a by-product of bacterial metabolic activity (StocksFischer et al., 1999, pp.1563-1564). Bacillus Pasteurii acts as a chemical catalyser to facilitate the formation of Calcium Carbonate in Calcium rich environments. The chain of reactions leading to the formation of calcium carbonate can be characterised as follows:

1. Bacterial metabolism increases the alkalinity in its

surrounding spatial area, leading to a chain of chemical reactions that will end up precipitating Calcium Ions (Ca2+) with soluble Carbonate Ions to create CaCO3 crystals (Stocks-Fischer et al., 1999, p.1563).

2. Chemical changes brought about by bacterial

metabolism create nucleation sites, that is focalised spatial areas with specific chemical conditions to promote the formation of minerals in the form of crystals (Hammes et al., 2003, p.4901).

The chemical reaction leading to the formation of Calcium Carbonate would theoretically occur in carbonate rich environments over long periods of time. Therefore, bacteria are introduced to such environments to accelerate the process. Specific protocol is based on the studies by Stocks-Fischer et al (1999) and Hammes et al (2003) and is explained in the following section.

Specific protocol Bacterial organisms Bacillus Pasteurii were ordered from the Leibniz Institute DSMZ-German collection of Microorganisms and Cell Cultures. Under the specific DSMZ coding system, the strain used in this experiment has a catalogue number DSM-33. Bacteria were ordered as a vacuum dried culture contained in ampoule. Once it had arrived, organisms were removed form the ampoule, rehydrated and placed into petri dishes containing nutrientrich Agar to grow colonies. Agar is a solid medium used in microbiology that provides nutrients to facilitate cellular growth whilst minimising motility. It is possible to additionally

// CHAPTER VI

d spatial areas with specific chemical conditions to promote the formation of s in the form of crystals (Hammes et al., 2003, p.4901).

cal reaction leading to the formation of Calcium Carbonate would theoretically arbonate rich environments over long periods of time. Therefore, bacteria are to such environments to accelerate the process. Specific protocol is based on the Stocks-Fischer et al (1999) and Hammes et al (2003) and is explained in the ection.

protocol

rganisms Bacillus Pasteurii were ordered from the Leibniz Institute DSMZ-German of Microorganisms and Cell Cultures. Under the specific DSMZ coding system, the in this experiment has a catalogue number DSM-33. Bacteria were ordered as a ied culture contained in ampoule. Once it had arrived, organisms were removed mpoule, rehydrated and placed into petri dishes containing nutrient-rich Agar to nies. Agar is a solid medium used in microbiology that provides nutrients to chemical compounds to control specific dissolve It was stated in the introduction to this chapter ellular growthdissolve whilst minimising motility. It is possible to the additionally conditions of specific cell experiments. that one of the aims in participating in a laboratory ompounds to control the conditions of cell experiments. experiment was to understand the complexities The which first chemical reaction which trigger of laboratory work. Design is normally produced hemical reaction trigger the complex process of the Calcium Carbonate s described complex through the following formula:Carbonate formation is process of Calcium through a structure of abstraction layers. In order to described through the following formula: initiate a material engagement with bio-engineered materials, it was deemed necessary to understand the phenomenology of working in a laboratory. CO(NH 2 )2 + H 2 0 → NH 2COOH + NH 3 Given the time limitations, it was not possible for the From the previous formula, it can be concluded researcher to complete the health and safety courses the presence of Urea (CO(NH 2)2) in aqueous to qualify for active laboratory work. Nevertheless, environments is theoretically enough for the special attention was paid into understanding bacteria to trigger the formation Calcium Carbonate. and describing the processes carried out during However, it has been suggested (Stocks-Fischer et the research. The following couple of paragraphs al., 1999) that environments rich in Urea and Calcium describe the steps to prepare agar and place Chloride increases bacterially induced mineralisation bacteria into growth medium. of calcium carbonate. In order to asses this thesis, the experiment aimed at comparing specific crystal Agar is a commercial compound that comes formation and morphology by comparing Bacillus available in powder. In order to create the gelatine Pasteurii activity on two chemical environments: one composition that is required for microbiological rich in urea (CO(NH 2)2) which will be referred as experiments, powdered agar is dissolved in water Sample #1, and the other rich in both urea (CO(NH and heated up to 121ºC for 15 minutes in an 2)2) and calcium chloride (CaCl2) which will be equipment called Autoclave. This process assist in referred as Sample #2. The following steps were the sterilisation of the growth medium to guarantee followed: no unplanned microbial organisms goes into the experiment. Liquid agar is then poured into petri dishes, where it is once again directly exposed to fire 1. Sample #1 was prepared with Tryptic soy agar to sterilise for a second time. After some minutes, the and 20 g/l urea. An initial set of bacteria was compound solidifies into a gelatine substance. placed to grow in order to assess that the bacteria is active and viable to conduct the Once the gelatine agar is ready, bacteria sets mineralisation experiments. are pulled from the containing ampoule using a manual pipette. Using the pipette’s fine end, stripes 2. Once sample #1 exhibited formation of are created in the surface of the solidified agar. bacteria colonies, thus confirming viability for Consequently, bacteria growth follows the grooves experimentation, a further set of the culture created by the stripes in the crystal formation. was isolated and placed in sample #2. Sample #2 was previously prepared with Tryptic soy Agar, 20 g/l urea and calcium chloride. Both samples were continuously monitored to spot mineralisation.

3. Both samples were photo-documented after 7

days of growth activity. Results are shown in the next section.

Figure 6.2-6.19 Photographic sequence showing the different processes in performing the bacterially induce mineralisation experiments. Dr. Meng Zhang is shown rehydrating bacteria, preparing agar and placing cultives

// CHAPTER VI

Findings

This section presents electron microscopy imagery of the two samples observed after a growth period of seven days. Results are presented in two sections. The first section shows electron microscopy data collected from sample 1, which was prepared in a Urease rich environment. The second section present electron microscopy images from sample 2, which was prepared with a Urease+Calcium Chloride environment. Each image is displayed with a lower bar including the magnification ratio and a graphical scale indicating the relative size of the organisms displayed.

Figure 6.20

Sample 1

Figure 6.21

Figure 6.20 and 6.21 show bacterial colonies. Electron microscopy images are presented with a 300x magnification and a scale in Âľm. Figure 6.20 shows a well-defined individual colony, whilst Figure 6.21 shows two colonies sharing some components.

Figure 6.22

Figure 6.23

Figure 6.22 and 6.23 shows the bacterial colony at a greater detail. Figure 6.23 shows the colony structure at 3000x magnification. Individual bacteria can be distinguished in Figure 6.22 with a 8000x magnification. At this scale, it is difficult to distinguish the individual shape of each bacterial organism.

Figure 6.24

Figure 6.25

It is possible to distinguish cellsâ&#x20AC;&#x2122; outline clearly in Figure 6.24. At 15000x magnification, it can be observed that Bacillus Pasteurii exhibits an elongated, capsule like structure. When compared against the graphical scale, it can be concluded that each bacteria has a size of approximately two Âľm. Evidence gathered from this series of photographs suggests that bacteria in Sample #1 did not lead to any mineralisation process over the course of one week. Comparatively, the following section presents evidence for bacterial activity in Sample #2 over the same period of time.

Sample 2

Figure 6.26

// CHAPTER VI

Figure 6.27

Figure 6.26 and 6.27 shows evidence of mineralisation, which can be attributed to microbial activity. Figure 6.26 portrays a structure based on spherical minerals, calcium carbonate crystals, connected though thin tubular structures. This pattern can be observed at greater magnification in Figure 6.27. It should be noted that both figures at shown at a graphical scale of between 400 and 500 Âľm, that is half a millimetre.

Figure 6.28

Figure 6.29

Figure 6.30

Figure 6.31

Figure 6.32

Figure 6.33

Figures 6.28 to 6.33 shows a gradual magnification of the calcium carbonate crystals. Figure 6.28 and 6.29 shows the basic node-connection structure described in the previous paragraph. It is worth-noting that calcium carbonate crystals grow embedded in the agar medium, which is evident in 6.29. Also, crystal formation follows patterns of clustering around focal points, which can be attributed to a concentration of bacterial activity in specific areas. Tubular structures connecting clusters are considered to be of organic nature. Although previous studies in the area (Stocks-Fischer et al., 1999, Hammes et al., 2003) does not account for the nature of the tubular structures, it is speculated that they serve as communication conduits for bacteria. Figure 6.11 offers a more detailed view of such clustering patterns at 100 Âľm scale. Different clustering patterns may be observed in figures 6.31 and 6.32. When observed in greater detail (figure 6.33) crystals reveal a fractal arrangement in the surface. A deposit of agar creates protuberances on the surface, causing edges to appear ill defined.

Figure 6.34

// CHAPTER VI

Figure 6.35

Figure 6.36

Figure 6.37

Figure 6.38

Figure 6.39

FFigure 6.34 provides a closer view at the connection between the tubular connections and the crystal structure. As detailed previously, it is considered that the tubular connections are organic structures, functioning as a connection for the bacterial organisms embedded in the crystal clustering. Figure 6.35 offers a detailed outline of the fractal pattern on the surface of the crystals. Protuberances on the surface are agar deposits. Figure 6.36 shows a fragmented crystal. It is worth noting that several elongated protuberances are observed on the flat surface of the crystal. Stocks Fischer et al (1999) suggest that bacteria are enclosed in the midst of crystal formation. It can therefore be concluded that the protuberances observed in figure 6.37 are bacteria embedded in the surface. The characteristic profile of bacteria is observed at greater magnification in figure 6.38 and 6.39.

// CHAPTER VI

DISCUSSION chapter vii

This chapter offers a critical analysis of the results obtained by this research, drawing upon both series of experiments, in-vitro and insilico, in order to assess the qualities that designing with bioengineered materials brings to architecture. This chapter has been categorised in six sections. (1)The first section will account for the conceptual argument that lead to this research, making special emphasis on the state of the art of how architecture conceptualises synthetic biology. (2)The second section will explain the reasons for considering the experiments performed in this research as mediating artifacts. Section three through to six offers a discussion on the different insights learnt from the experiments. The discussion has been organised around four concepts: (3) Biological shape is the result of cross-scales interactions, (4) Models of reproducibility and fidelity (5) Shape as information transactions with the environments (6) Time dependancy Finally, this dissertation will be brought to an end by concluding the main accomplishments of this work and delineate future work.

Towards a literal biological paradigm for architecture

The first motivation to this research was understand the implication of designing architecture with biological parts. The discourse of a literal biological paradigm, started by Mengel and Hensel (Hensel et al., 2006), advocated an architecture that fulfilled the life-like criteria of ‘adaptability with dynamic and generative ecological relations’ (Hensel, 2006, p.18). This approach received several criticisms, as it was understood to a shallow appropriation of concepts from science, thus leading to a frivolous engagement with biology (Roudavski, 2009, pp.365-366). Hensel (2006, p.19) writes: To pursue seriously the proposition of synthetic-life architectures it is important to take a close look at biological processes and materials, all the way down to the molecular scale, involving biochemistry in the understanding of the advanced functionality and

performance capacity of biological organisms The understanding of biological processes evolved into thinking of architecture as being potentially constructed using biological organisms (Armstrong, 2008), thus advancing the notion of a bioarchitectural composites (Cruz and Pike, 2008b, pp.6-9). The opening argument of this research is that the introduction of biological systems as materials in architecture implies a major shift in the way architecture is conceptualised and produced. Our current design practices are predicated upon a catalogue of shapes and procedures that are feasible with the technical resources available to the production of the architectural object. Kenneth Frampton presents the argument of the tectonic of materials when investigating the development of the modern movement in architecture, describing how the conceptualisation of space has been shaped and modified by the material possibilities. Therefore, modernism derives of the tectonic qualities of concrete and steel (Frenkel et al., 2001). In keeping with the same philosophy, this research sets out to investigate the implications of using biological engineered materials in architecture. Specifically, it aims to understand the qualities of design that emerge form this paradigmatic shift. In doing so, it was deemed necessary to develop two mediating artifacts that will account for two main properties of materials: shape and substance. The following section discusses results of the experiments performed in this research. Also, it will be outlined the reasons for considering the lab experiment and the computational description as mediating artifacts. Special attention will be paid to what the process and object of mediation is. The chapter will be brought to an end by summarising the main learning outcomes of using both mediating artifacts.

Mediating artifact Early on this dissertation we explained the term of mediating artifact, and defined it as the series of tools that allows designer to create a physical artifact. In defining the architectural tools of design as mediating artifacts, P茅rez-G贸mez (2005) borrowed the term from activity theory. Generally, mediating refer to the ability of tools to interface between the subject and an object of action (Collins et al., 2002). Therefore a scale model, drawing or even a verbal instruction acts as mediation, because it facilitates the materialisation of an abstract entity into an architectural object.

// CHAPTER VII

Moreover, mediating artifacts represents tools of tectonic investigation in architecture: Architects require a means of translating their working methods into a language that can affect and be affected by the world (...) models today act to articulate questions, to guide experiments, and to generate arguments (...) provide both the rigorous, objective and identifiably relevant proofs of concept that working with matter requires (Abruzzo et al., 2008, p.17) This research suggested that the experiments carried out, a computational description and a laboratory experiment, constitute a novel form of mediating artifact for a design practice based on the premise of materialising the architectural object with biologically engineered materials. They constitute mediation between thinking of a new architectural paradigm and its actual materialisation. They are probes to dissect the natural in order to apprehend possible ways in which we could start devising design strategies. Computational descriptions are already widely used in architecture as a drafting and shape generation tool (Terzidis, 2003), thus their quality as a mediation artifact is well established. On the other hand, laboratory experiments are rarely employed in architecture as a design tool. This research contends that the Bacillus Pasteuri experiments are a new form of mediating artifact, in that they represent an idealised system under a set of strictly controlled conditions. The aim of the series of laboratory experiments is to mediate the dynamics of the biological system prior to implementation in the wild. The model will need to be modified in order to be tested in real world scenarios, where a number of variables is added to the system thus increasing considerably its complexity. The process of incremental abstraction observed in the lab experiments can also be described as a gradient of abstraction layers, whereby each level isolates a set of variables to be tested. The logic

underneath this model is not alien to architecture, and works similarly to the process of creating architectural models. For instance, urban models are configured to include only the volumetric shape of buildings, which mediates the overall spatial presence of the real building. In the following sections, we offer a set of insights generated through the combined used of both mediating artifacts.

Biological shape is the result of cross-scales interactions Both artifacts mediate the production of architecture from the molecular level. The laboratory experiments studied bacterially induced mineralisation using Bacillus Pasteurii. The computational description simulates basic bacterial interactions, and proposes a design strategy by manipulating biomineralisation nucleation sites. Nevertheless, molecular scale is normally regarded as a territory alien to architecture. For instance, Steve Pike collaborated with microbiologist Conrad Mullineaux in creating a microbiological responsive architecture. A conflict of scale is delineated as central to this project: micro-organic material transform their environment at microscopic level, whilst architecture concerns with human scales. In order to address this conflict, the biological systems were constrained to glass exhibition vessels filled with cyanobacteria colonies reacting to light (Pike, 2008a, pp.74-76). However, molecular-based interactions are considered to be instrumental in the definition of higher-level properties of biological materials, specially composition and shape. For instance, protein components embedded in the matrix of molluscan shells control the deposition of Calcium Carbonate, causing permeable arrangement of crystals (Wheeler et al., 1981). This phenomenon is responsible for the high resistance of oyster shells. Moreover, it has been suggested that crystal

â&#x20AC;&#x153;If architecture is to be constructed with biological systems, such as bacteria and biomineralisation, it is necessary to physically engage with the material. â&#x20AC;?

deposition at molecular scale interact with higher levels of organisation in determining the macro-level architecture of coral skeletons (Gladfelter, 2012). If architecture is to be constructed with biological systems, such as bacteria and biomineralisation, it is necessary to physically engage with the material. As little research has been done in the field, this research deemed prudent to start at the bottom scale of molecular structures. For instance, the computational description set out to describe a basic model of molecular interactions inside a bacterial community. The premise was that the mutual stimulation between attractors leads to the formation of a chemical scaffold, similar to the one observed in bacterial communities and, higher up in the complexity stack, in biofilms. When comparing these results with the electron microscopy images, it can be assessed that the computational description serves to describe interaction up to a certain level of fidelity. Computational description is based solely on physical interactions of bacteria and attractors, whilst a higher complexity is observed in the laboratory experiments. This discrepancy can be attributed at the conflict of scales. The computational description works in a molecular level to define the shape of a higher-level structure such as the bacterial community. However, the actual definition of how bacterial communities function seem to be linked to mechanism of two-way feedback between cell and the surrounding environment, as suggested by Decho (2010). Consequently, a further mechanism needs to be implemented in the computational description to account for the interaction across scales.

Models of reproducibility and fidelity

The computational description functioned as a proof of concept to explore the implication of constructing architecture in biological systems. As a starting point, It was defined that biological systems

// CHAPTER VII

are dynamic non-linear phenomena. The script addressed this by simulating a particle system that follows the mechanics of bacteria inside bacterial communities. The experiments presented in chapter five reveal interesting patterns. First, even when two simulations are run using the same values, they do not yield exactly the same result. After running the simulation of cell distribution, coordinates for each bacterium were exported in a CSV format. Comparing the coordinates for two simulations run with identical initial values, it emerges that coordinates do not match across simulations. Even when the general shape of the simulation is similar, the individual molecular transactions seem to differ significantly. This behaviour contrasts with the nature of the current process of production in architecture. A mediating artifact such as a scale drawing is expected to be implemented and translated to the architectural object following precise instructions. Man-made structures are dependant upon reproducibility within millimetres. It can be concluded that the models of reproducibility and fidelity differ in man-made and biological structures. Similarly, experiments conducted with the digital artifact produced in this research proved that is difficult to direct shape based on the properties of self assembly, that is the organisation of matter following local interaction of agents. The ATF series of experiments, detailed in chapter five, demonstrated that it is not feasible to reproduce well-defined shapes in systems such as bacterial communities. This finding hints at the possibility that biological materials will more likely lead to a redefinition of current architectural typologies. If the future production processes of architecture are based mainly on biological systems, they will require an analysis of the possible shapes to be created. It seems plausible to suggest that biological agents such as bacteria are not fit to produce a rectangular pillar or an arch.

Shape as information transactions with the environments

Electron microscopy images presented in chapter 6 reveal a similar trend as the one described in the previous section. Even when a recurring pattern of crystal clustering can be detected, it can be observed that each single cluster is unique in its spatial distribution. It can be therefore concluded that the architecture of biological systems cannot solely be defined by the molecular interaction of agents, but need to take into account the complex information transactions with the environment. Form is result of reciprocal feedback between cells and surrounding environment. If cluster distribution is observed to differ across different microenvironments within the same sample, it is possibly due to different concentrations of oxygen and nutrients (Watnick and Kolter, 2000). Moreover, metabolic activity at bacterial level seems to influence the spatial configuration of bacterial communities. For instance, figure 6.9 reveals interesting patterns in the internal configuration of communities. In this image, the formation of crystal seems to function as a defining boundary for the internal structure of the agar base. In the same vein, the experiments conducted by Dr. Meng Zhang looked at investigating the morphology of calcium carbonate crystals under different conditions. Specifically, the experiments in which this research collaborated included the assessment of Bacillus Pasteurii in a Urea+Calcium chloride rich environments. Crystal morphology observed in the electron microscopy images is specific to the type of bacteria and the chemical composition of the environment. This evidence is consistent with the notion of interdependency between environment and organism in the definition of shape.

Time dependancy Finally, both systems exhibit a strong correlation between shape and time: crystal formation and bacterial distribution varies considerably over time. In the laboratory experiments, crystal formation increases over time, and the clustering patterns and morphology tend to evolve as well. The same holds true for the computational simulation, which is made evident on the fact that the algorithm is based on an iteration-dependent routine. The strong time dependancy observed in the laboratory experiments motivated the implementation of a further algorithm, attached in the CD accompanying this work, that performs bacterial distribution based on cycles. The interface is constructed around the paradigm of specifying different values for each cycle, which are constrained to a preset number of iterations.

// CHAPTER VII

CONCLUSIONS AND FURTHER WORK chapter vii

This work presented an investigation on the qualities of working with biologically engineered materials. In doing so, a methodology was proposed that encompasses two mediating artifacts: a computational description of bacterial communities, and a collaboration in a series of bacterially induced precipitation of calcium carbonate. In both cases, the research was performed at the level of molecular interaction of bacterial organisms. It has been concluded that the scale and dynamics involved in the production of form differ fundamentally in biological and man-made systems. Different arguments have been developed to sustain this claim. First, biological shape is the result of complex interactions across scales. Whilst certain properties of biological materials can be explained from the molecular level, some other are also result of the interaction between the molecular and higher scales of organisation. Second, the models of reproducibility and fidelity are different in biological systems as compared to architectural systems. Even when two organisms undergo the same conditions, the result varies in the molecular level. Third, shape in biological systems does not depend exclusively of the units interacting in a system, but also on the complex information transactions with the environments. For instance, the morphology of crystals shown in the laboratory experiments series is exclusive of the Bacillus Pasteurii and the chemical configuration of the environment. Fourth, biological systems are time dependant. In both cases, computational description and experiments in the laboratory, it is observed that the state of the system depends on the particular time frame in which it is observed. Systems are ever evolving. Based on these arguments, it can be concluded that at this point, the research presented in this work does not have a direct translation to anthropometric scale. Patterns found in the computational description do not represent an abstraction of a building. The qualities and complexities of working with biological systems, described in the previous section, seems to suggest that a significant amount of research needs to be done before it is clear how biologically engineered materials may fit in the production of architecture. For instance, work needs to be carried out in understanding how molecular level interactions are integrated into higher levels of scale and

organisation in producing shape in biological systems. The non-determinacy of the experiments conducted hint that biological materials cannot be integrated custom-made into the current architectural form.

Further work A number of potential research efforts stem from the work presented in this dissertation, and can be broadly classified in two categories: research efforts based in laboratory, and those based in computational descriptions. First, the application of a scaffolding principle on the laboratory setting. Bacillus Pasteurii can be genetically modified to follow a specific trail other than oxygen and nutrients. For instance, a scaffolding system could be constructed on the basis of a modified version of Bacillus Pasteurii that follows a specific light wave. Under these conditions, a biomineralisation system could encompass two subsystems: (1) mechanical elements, that electronically controls a light emitting component, and (2) biological elements, the bacteria condensing calcium carbonate. This principle would allow the integration of bacterially induced mineralisation with a parametric control of the attractors shaping the distribution, as proposed in the computational description on this work. This hypothetical hybrid mechanical-biological system could also lead to the creation of material composites. As the specific morphology of crystal is dependant upon the coupling of bacterial organism and environment, different classes of bacterium could be genetically modified to follow different light waves. Then, when put together in the system, they could create a material with different crystal morphology on each layer. The resulting material would exhibit different physical properties across their section, each depending on the specific morphology of each layer. As for the computational description, reported results suggest that further work needs to be done in the two way feedback in bacterial communities. Currently the systems inform bacterial distribution based on the chemical scaffolding created by the set of attractors. However, there is no feedback mechanism that allows for the local interaction of bacteria to modify the shape of the chemical scaffolding. Additionally, further computational descriptions need to account for the integration of scales. It has been suggested that natural form generation is dependant upon the integration of events across scales. Under this principle, the interactions at the molecular level are affected by interaction at the level of biofilms for instance.

// CHAPTER VIII

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APPENDIX

SYNTHETIC MORPHOLOGY AND DYNAMIC SYSTEMS IN ARCHITECTURE