Proliferation

Proliferation By Sara El Jamal MArchDesign Bartlett School of Architecture RC7 2017

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RC7 -MARCHDesign -The Bartlett School of Architecture - UCL

Sara El Jamal

Cover 3D represtentation of growth using DLA - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http:// www.entagma.com/vex-in-houdini-diffusion-limitedaggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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Proliferated

Dissertation: 2017

Proliferation By Sara El Jamal Bartlett School of Architecture RC7 Draft Dissertation submitted in partial fulfillment of the requirements for the degree of Masters in Architectural Design requirement, RC7 Bartlett School of Architecture, UCL. 2016-2017

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RC7 -MARCHDesign -The Bartlett School of Architecture - UCL

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Thesis: Proliferate By: Sara El Jamal Project: Permeable Plateau Collaborators: Marisa Dewi Jeng Yeng Li Ding Hao Idil Yucek

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Sara El Jamal

Dissertation: 2017

Proliferated

+ Acknowledgement The completion of this undertaking could not have been possible without the participant and assistance of so many people whose names may not be enumerated. Their contributions are sincerely appreciated and deeply acknowledged. However, immeasurable appreciation and deepest gratitude extended to the following persons who in a way or another have contributed in making this study possible.

I wish to express my gratitude to my thesis advisor, Professor Mario Carpo for his generous advice and enless guidance and support throughout my research. I would like to also thank our BiotA-Lab tutors Marcos Cruz, Richard Beckett, Christopher Leung, Javier Ruiz, and Shneel Malik for their encouragements, kind and understanding spirit, and their enthusiasm with regards to my research and the living plateau group work.

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Sara El Jamal

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Dissertation: 2017

Table of Contents

1.0 Introduction 10

CHAPTER I: Hypothesis 13 Random complexity or fractal methodology? 13 1.0.1 Emerging structures 15 1.0.4 Optimisation 20

CHAPTER II: A Paradigm Shift 25 A new kind of Science 25 2.0 The Universe 26 2.0.1 Cellular Automata 28 2.0.2 Game of life Theory 31

CHAPTER III: Nodes and Codes 33 Agent Based Modelling 33 3.0 Agent Based Modelling 34 3.0.1 Precedent Studies 36 3.0.2 Agents and Patches 38

CHAPTER IV: Design Process and Implementation

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The Permeable Plateau 55

CHAPTER V: Macro and Micro-Colonies

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Brophytes and Microbes 59 5.0.1 Network of Interactions 60 5.0.2 Computing Behaviours 63

CHAPTER VI: Modelling Growth 69 Cultivating Species 69

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Adaptation in Natural and Artificial Systems

Echo

Aggregation

John H Holland

Netlogo

1990

1990

John H Holland

1975

Houdini FX

Andy Lomas 2015 - 2017

1996

Diffusion Limited Aggregation 1981

A New Kind of Science Stephan Wolfram

2002

Thesis Timeline

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Game of Life John Conway

1970 Agent Based Modelling Thomas Schelling’s Seggregation Model 1970-1980

Morphogensis Alan Turing

1952

Turing Machine Alan Turing

1940

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1.0 Introduction

Designers and Architects usually perceive

the elements in buildings as being rigid and lifeless.

Materiality in the realm of design is treated as an additional skin rather than the main component that affects the overall shape. Similar to human skin, the materials that are chosen to be the second layer of the design repel and protect from any exterior inhabitants that can invade and grow. However, one cannot be entirely certain whether these materials were designed to attract colonies of different species or not. These species can then be used as a building system that can be adaptive and transformative in its environment and the geometrical shape that it strives on.

The design of this thesis aims to explore

various geometrical components on a horizontal surface that can host different kinds of species and promote growth in a controlled state. The controlled state can be achieved by simulating biological growth more accurately to promote colonisation of multispecies, taking into account the many biological and environmental dynamics involved. This thesis will assess the potential of agent-based modelling (ABM) in predicting the growth of species on material surfaces to achieve a level of bioreceptivity1.

1 Bioreceptive: Is a term used in BiotA research cluster to

describe a synthetic relationship between forms and nature. To be bioreceptive means to be ‘alive’ by attracting and hosting multiple species in a controlled state as part of the design.

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Proliferated

Fig 1 Permeable Plateau - Conceptual Render ElJamal, S. Dewi, M. Ding, H. Yucel, I. Jeng, Y. (2017). Proliferate - Permeable Plateau.

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RC7 -MARCHDesign -The Bartlett School of Architecture - UCL

Sara El Jamal

3D represtentation of growth using DLA - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http:// www.entagma.com/vex-in-houdini-diffusion-limitedaggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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+ CHAPTER I: Hypothesis Random complexity or fractal methodology?

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Fig 2 The Life Cycle of Moss Growth (Physcomitrella Patens) Frontiersin.org. (2017). development of psychomitrella. [online] Available at: http://www.frontiersin. org/files/Articles/25695/fpls-03-00166-HTML/image_m/fpls-03-00166-g001.jpg [Accessed 10 Jul. 2017].

Life is a driving force that proliferates, develops and changes. It is described as an: “intermediate energising of conscious being [that] liberates into sensitive action and reaction, a form of the creative force of existence which was working subconsciously or unconsciously, absorbed in its substance, It supports and frees into action the apprehensive consciousness of existence called to mind and gives it a dynamic instrumentation so that it can work not only on its forms but forms of life and matter� (Aurobindo, 1939).

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1.0.1 Emerging structures

Growth, a term that is often associated with nature, species and life,

is a fascinating subject that has been the core research of many biologists and researchers. The main link between all living organisms is that they are all made up of cells that regenerate and substitute one another. Life in all its complexity starts off with a single cell. Cells first divide to form an embryo, then after reaching a certain number, the cells specialise in different functions to form tissues, organs and systems. This symbiotic1 behaviour is defined by the anthropologist Charles Darwin who claimed that the evolving forms of species could be classified using their natural selection2. The evolution of the Growth is not only about the cell division, but more importantly about how the cell transforms to have a particular task that will form a more significant entity. Thus, according to Darwin: “As many more individuals of each species are born than can survive; and as, consequently, there is a frequently recurring struggle for existence, it follows that any being, if it vary however slightly in any manner profitable to itself, under the complex and sometimes varying conditions of life, will have a better chance of surviving, and thus be naturally selected. From the strong principle of inheritance, any selected variety will tend to propagate its new and modified form.� (Darwin, 1866)

1 Symbiosis: interaction between two different organisms living in close physical association, typically to the advantage of both. Dictionary, s. (2017). symbiosis Meaning in the Cambridge English

Dictionary. [online] Dictionary.cambridge.org. Available at: http://dictionary.cambridge.org/dictionary/english/ symbiosis [Accessed 10 Jul. 2017].

2 Natural Selection: the process whereby organisms better adapted to their environment tend to survive and produce more offspring. The theory of its action was first fully expounded by Charles Darwin, and it is now regarded as be the main process that brings about evolution. Bbc.co.uk.

(2017). BBC Bitesize - GCSE Biology - Natural selection and selective breeding - Revision 1. [online] Available at: http://www.bbc.co.uk/education/guides/z6trd2p/revision [Accessed 10 Jul. 2017].

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1.0.2 Chaos and Reductionism New

complex

phenomena

were

developed by scientists who were intrigued to discover the driving force behind the different complex behaviours found in nature. These theories were first introduced by renowned scientists such as Adam Smith, Donald Hebb and Charles Darwin. Adam Smith’s ‘Invisible Hand’1 in Economics focused on increasing the individuals’ interests to benefit the entire community. Donald Hebb’s ‘Cell Assembly’ theory centred on the analysis of the data stored in the human brain as a result of the hierarchal interactions

between

individual

neurones

(Abu Taih, 2010). Whereas, Charles Darwin’s theory of evolution stated that the complex form derived from nature were the result of the interaction between different organisms and species that emerged from the idea of the natural selection. Nonetheless, their early studies and observations were contradicted by many scientists who were influenced by Newton’s philosophy on reductionism2.

1. Invisible Hand: The phrase was first introduced by

Adam Smith which means “The unobservable market force that helps the demand and supply of goods in a free market to reach equilibrium automatically is the invisible hand.” The Economic Times. (2017). Definition of ‘Invisible Hand’ - The Economic Times. [online] Available at: http://economictimes.indiatimes.com/ definition/invisible-hand [Accessed 10 Jul. 2017].

2. Reductionism: “the practice of analysing and describing a complex phenomenon in terms of its simple or fundamental constituents, especially when this is said to provide a sufficient explanation.”

Dictionary.com. (2017). the definition of reductionism. [online] Available at: http://www.dictionary.com/ browse/reductionism [Accessed 10 Jul. 2017].

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The classical definition of reductionism stated that nature is a linear system made of discrete parts that make up the whole system. The idea of reductionism emerged in a way to understand a complex system by breaking it down into individual parts. If the starting state of a system is known, a hundred percent predictability of what the system would look like can be achieved. The variability of the system is constant no matter what scale one is looking at. Thus, this contributes to the notion of the system being a fractal system rather than a noise system. However, this logic did not apply to many systems especially the ones dealing with complex biological behaviour. These systems were known as non-linear systems, where the sum of their parts did not equal the output of the whole. (Abu Taih, 2010)

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+ Fig 3 Conceptual Illustrtation of Reductionism ElJamal, S. (2017). Proliferate - Permeable Plateau.

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1.0.3 Complex Emergence

Systems

Sara El Jamal

and

Complex systems is a theory that

emerged as a result of nonlinear systems that

did not follow the idea behind reductionism.

This theory was proven difficult for people to comprehend due to its high level of complexity as its name suggests. “A complex system is any system featuring a large number of interacting components (agents, processes, etc.) whose aggregate activity is nonlinear (not derivable from the summations of the activity of individual components) and typically exhibits hierarchical self-organization under selective

every resultant is clearly traceable in its components, because these are homogeneous and commensurable. It is otherwise with emergents, when, instead of adding measurable motion to measurable motion, or things of one kind to other individuals of their kind, there is a co-operation of things of unlike kinds. The emergent is unlike its components insofar as these are incommensurable, and it cannot be reduced to their sum or their difference.”

(Lewes, 1875 )

pressures.” (Luis M, 1999) The technological developments in the field of science led to the awareness of the limitations that linear

systems and mathematical equations brought

upon the ability to understand complex behaviours. Complex systems have become

more apparent since people became more aware of the level of complexity that surrounds their daily interactions. As scholars became more interested in complex systems, a new phenomenon arose as a characteristic of these complex systems; Emergence. The term “Emergence” was first introduced by the British

philosopher G.H. Lewis who suggested the analogy between the sum and the difference of the interactions between discrete parts. He states that: “ Every resultant is either a sum or a difference of the co-operant forces; their sum, when their directions are the same — their difference, when their directions are contrary. Further,

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Fig 4 The Branching Diagram of Darwin’s Origin of

Speecies Theory (A) Lineage “An evolutionary species is a lineage (an ancestraldescendant sequence of populations) evolving separately from others and with its own unitary evolutionary role and tendencies.” (Simpson, 1961: 153) The Diagram Shows that in (A) the branching system overlaps one another and is not continous branching system.

(B) Clades which clearly highlights the continous nested system.

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Fig 5 Ancestor Diagram

Both (A) and (B) are segmented.

However in (A) the ancestor shown in black discontinues and extinct (in grey) at speciation event. Where as in the (B) branch the ancestor branch is more presistance than the ancestor shown in (A)

QUEIROZ, K.E.V.I.N.D.E., 2011. Branches in the lines of descent: Charles Darwin and the evolution of the species concept | Biological Journal of the Linnean Society | Oxford Academic. OUP Academic. Available at: https://academic.oup.com/ biolinnean/article/103/1/19/2452442/Branches-in-the-lines-of-descent-Charles-Darwin [Accessed July 14, 2017].

Throughout time, Post-Darwinian biologists

about the change in entropy of the entire

Darwinian

system. If the process absorbs free energy, the

phenomena and regularly tested numerous

complex system will have a smaller entropy

theories and hypothesis. Herbert A. Simon,

than the elements; if it releases free energy,

political and computer scientist, who is known

the opposite will be true.” (Simon, 1962) Simon

for his work on the architecture of complexity,

compares the conventional methodology of our

divides his theory on the evolution of complex

daily and regular interactions with the biological

forms into three categories. First, the complex

evolutionary process. Natural selection is

by simple rules that arise to create random

solving process. He states that to achieve the

process. Second, the hierarchy of the species

desired result using a trial and error method, the

proposed in Darwin’s theory of evolution is

system in which the primary element is based

that the element -Nylon- is made from identical

sources from which the problem-solving system

became

more

aware

of

the

structures found in nature can be identified

not compulsory. For instance, Simon explains

further explored using the human problem-

on must be selective. “When we examine the

components known as monomers. These

or the evolving system derives its selectivity, we

components are regarded in a hierarchal

discover that selectivity can always be equated

manner with a span of one element.

with some feedback of information from the

“Third,

the evolution of complex systems from simple

environment.” (Simon, 1962).

elements implies nothing, one way or the other,

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RC7 -MARCHDesign -The Bartlett School of Architecture - UCL

Sara El Jamal

1.0.4 Optimisation

Optimisation is viewed as an effective

strategy that is obtained when dealing with the natural selection of species. The complex structures found in nature divert the attention from focusing on the main steps that derive its selective properties. This theory was first introduced by the pioneer American scientist, John H Holland.

He states that to fully comprehend the obstacles of the adaptive process that organisms inhabit, one must construct a theory that can reveal their rigorous characteristics and predict the outcome derived from them. (Holland, 1975)

Holland describes a complex adaptive

system as a system that hosts an agent that has a network of interactions. These ‘adaptive’ agents compete and adapt to one another based on their agenda- that may have other sub-agendas included. The sub-agendas are also known to be the aggregate agents that can affect the main adaptive component just as much as the adaptive component can affect the aggregate.

This symbiotic relationship in a practical system, where the rules might differ in the way agents interact, is conditional. Agents can adapt to one another by sharing information, or change their characteristics as the gain more experience or when they change their environment.

Holland emphasised the importance

of building the right model to narrow down and control the selective propagation of the driving force behind the complex system. He suggests that there are three kinds of model: ‘Data Driven’ (for instance environmentally driven such as the weather forecast), Existing Self-reproducing

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System. (John Von Neumann Automaton), and experimental hypothesis. “For natural systems, this means that theory must provide techniques for prediction and control; for artificial systems, it must provide practical algorithms and strategies.”

(Holland, 1975) Holland’s theory is to divide the adaptive tasks into three characteristics; field, structures, and operators. However, each characteristic yield different output depending on the environment of the system undergoing the adaptation process.

On the other hand, there is a tremendous

uncertainty regarding the composition of the environment itself, which might affect the structural framework of the adaptive plan. As a result, one must generate different environmental settings that can have multiple possibilities for the structure to perform differently. The system will change its course even if the prediction and anticipation of the given system do not come true.

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types of systems across scales and diciplines

A ‘Complex’ System

which exhibit common behaviors

Emergence

Hierarchies Control Structures Size

Giving rise to a number of hierarchical levels

Emergent behavior that cannot be simply inferred from behavior of

Dramatically Interacting

Self- Organization Decomposability Into Subsystems

Many Components Complex System Involve

Types of Sub Systems A ‘Simple’ System

Fig 6 Complex systems interact with the environment along other kinds of interactions which include “emergent systems properties - the higher level features; interdependencies between these elements and emergent properties; the multiple nested levels and hierarchies that can form; and if the elements and properties are included in other systems.” (Lucas, 2016). Web. Photofugue.com.au. (2017). processPhotography - Introducing Complexity. [online] Available at: http://photofugue.com.au/Pages/ Introducing%2520Complexity.html [Accessed 10 Jul. 2017].

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RC7 -MARCHDesign -The Bartlett School of Architecture - UCL

Sara El Jamal

1.0.5 Digital Morphology

The ability to transform one’s perception

The work of Tibor Ganti in ‘the

to a molecular scale requires a credible knowledge

principle of life’ influences Hensel’s understanding

of the given subject. Simulating a single cell that

of living forms. Ganti’s work emphasised that

proliferates to perform a chaotic behaviour had

a living organism inhabits five key features to

to be tested. Computation triggered the birth of

maintain its survival among the living world;

diverse methods and techniques that have helped

Unity, Metabolism, Stability, informative, and

shape a new understanding of the emergence of

control. (Ganti, 2003) These characteristics show

life- growth in particular. A German architect and

the synthetic behaviour that any complex system

researcher, Michael Hensel, examined the field

possesses, and it is highly recommended to

of “performance-oriented architecture” (Hensel,

understand and test these responses on a more

2006) through the work of Professor Przemyslaw

sophisticated level of computation.

Prusinkiewicz. In his studies, Hensel argued that the term ‘growth’ can be calculated by extracting inputs driven by forces of nature. Environmental modelling challenges architects to design active materials that are sensitive to their surroundings.

He stated that “every change in the input yields different growth result,” which gives designers the advanced toolsets to create and control their products.

Hensel further explores the possibility of

the interlink between biology and form in his article Emergence: Morphogenetic Design Strategies.

The complexity of the natural form that emerges and self-assembles to reproduce ought to be experimented by crossing the magnitude beyond its molecular level. The emergent properties that are inhabited from the environmental context within the self-organizing system form a unified complex behaviour that acts as a single entity.

(Hensel, 2015)

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Fig 7 Ganti Chamoton reflect the origin of life in three different subsystems, Membrane that act as a protection boundry, the self-sustaining cycle, which can also act as a metabolic factor that keeps the system running, and the information carrying the replicating polymer that generates another cellular growth similar to the parent. https://www.researchgate.net/figure/275101380_fig1_ Fig-1-Ganti%27s-chemoton-is-made-up-of-threetightly-coupled-subsystems

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+ Fig 8 Conceptual Illustrtation of branching growth ElJamal, S. (2017). Proliferate - Permeable Plateau.

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RC7 -MARCHDesign -The Bartlett School of Architecture - UCL

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Fig 9 Conceptual Render of Growth 3D represtentation of growth using DLA - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http:// www.entagma.com/vex-in-houdini-diffusion-limitedaggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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2.0 The Universe

In a “New Kind of Science1”,

Sara El Jamal

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Stephan Wolfram breaks the limitation of traditional mathematical approach that was used to define nature and its complexity. His interest extended beyond how structures arise in the universe. As a result, Wolfram curiously questioned how something with deep complexity could be produced and exist in nature. Being a physicist who specialised in particle science, he assumed that it was easier to predict growth metaphorically. However, using

the

traditional

mathematical

equation, to decipher the most complex systems, did not work.

A new paradigm thinking then

emerged. A systematic approach using computer intelligence can implement simple, arbitrary, general rules. “If one can assume nature has to have a particular science one can start to think that perhaps life follows some definite rules.”

(Wolfram, 2002)

1 A New Kind of Science: A book written

by Stephan Wolfram in 2002, which has a systematic approach towards achieving cellular automata

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Fig 10 Conceptual Illustrtation of Cellular Automata Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/book/chapter-7-cellularautomata/ [Accessed 10 Jul. 2017].

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2.0.1 Cellular Automata

A World full of self-replicating

Conway developed an illustrative example titled

robots, and science that can reprogram how

“The Game of Life.” The algorithmic model

cells crystallise were some of the reasons that

uses simple rules to create complex phenomena

pushed scientists such as John Von Neumann

through the interactions between the agents as

and Stanislaw Ulam to propose a new concept

they evolve. The system captures certain essential

that might shape the future of new science.

features of the given prototype in a parallel system

When the first computers were introduced,

and idealises everything else. “This change from

Neumann recognised their potential to change

serial to parallel systems was significant because it

the norm. “The heuristic use of computers, as

is widely recognised that many natural systems

viewed by Von Neumann and Ulam, resembles

are parallel” (Burks & Nuemann, 1966)

the traditional scientific method except that

the computer replaces or supplements the

distinguished into two entities; black and white.

experimentation process.” (Burks & Nuemann,

The ‘black’ cell represents a living cell whereas

1966) He presented a highly complicated

the ‘white’ cell accounts for a non-living cell.

machine that can self-produce and generate a

“The basic rule can specify a cell should become

blueprint of information.

black if any of its neighbours are already ‘black’.”

(Wolfram, 2002). This basic rule generates the

However, Neumann failed to grasp

The

emergence

of

the

cells

is

the main idea behind the complexity of natural

same pattern over a period of time, however, if

systems, that complex forms can emerge

one decides to tweak the rule used to produce

from simple rules (Gleick,1987). The notion of

the previous example, one can realise the pattern

complexity during that time remained on the

created from the pixilated black and white cells

borderline between reductionism and chaos.

turns into a checkerboard pattern.

Thus, resulting in a paradigm where systems are segmented in a top-down approach. On the other hand, Ulam had proposed the idea of deriving complex systems from simple rules called Cellular Automata.

The intricate patterns attained by

the behaviour of a prototype in nature can be imitated in a symbolic programming. The core of the underlying structure in nature can be explained

Fig 11 Snow Flakes Cellular Automaton simulation.

Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/book/chapter-7cellular-automata/ [Accessed 10 Jul. 2017].

in a simple approach using one of the most basic computational rules called Cellular Automata.

Cellular Automata which was first introduced by Von Neumann in the 1940s, then later developed

by Ulam. It gained its popularity when John

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Fig 12 3D Cellular Automaton simulation.

Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/book/chapter-7-cellular-automata/ [Accessed 10 Jul. 2017].

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Fig 13: Rule 222

Fig 14: Rule 90 Fractal Resolution

Fig 15: Rule 90 Repetitive Pattern with a higher resolution Your Bibliography: Natureofcode.com. (2017). The Nature of Code. [online] Available at: http:// natureofcode.com/book/chapter-7-cellular-automata/ [Accessed 10 Jul. 2017].

Another rule was tested similarly to

The centre of the column cells here was

the previous setup. However, it’s not a simple

random. A simple rule was implemented, but the

repetitive pattern, but an intricate fracted

outcome was incredibly complicated in its patterns.

pattern is generated from identical nested pieces.

Put so little in and get so much out. Simple rules

Simple patterns generated from simple rules

can produce complex behaviours. Wolfram

is what was believed to be the only method to

stated that a snowflake, for instance, is a complex

achieve a particular behaviour in 1982. (Wolfram,

crystal form that can be derived from a single cell.

2002). A systematic experiment was conducted

However, when a piece of ice solidifies from a water

where Wolfram initiated every single one of the

vapour, there is some latent heat released which

256 possible simplest cellular automata rules.

inhibits more ice. “The simplest way to capture

Random patterns were generated. The research

that effect by having cellular automation in which

was believed to be a fatal error in the system,

cells become black when they have exactly one

however, after running different tests and analytical

black neighbour but stay white when they have

procedure, a discovery was made in regards to

more than one black neighbour� (Wolfram, 2002).

the generated patterns.

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0 ticks

40 ticks

200 ticks

Fig 16 Cellular Automata Model on Netlogo - Using ABM Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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being instigated and not before. They “act after a set of counters has come to exist, in numbers and configurations that originate from a decision process that is outside Conway’s rules.” (Frontiers In, 2016) Noting that the decision process varies from one individual to another, one must keep in mind that the counters will then transform as a reaction to each decision process respectively.

Conway first based his theory on a board game making the decision maker a player. However, as computer scientists adapted his approach, the decisions are then made by algorithms. As a decision maker, the rules are clear, the cells and

Fig 17 Game of Life illustration

counters are known, but the outcome can never

Your Bibliography: Natureofcode.com. (2017).

The Nature of Code. [online] Available at: http:// natureofcode.com/book/chapter-7-cellular-automata/ [Accessed 10 Jul. 2017].

be seen beforehand. One cannot predict the evolution or transformation of the cells after the process is complete. With Conway’s set of rules,

2.0.2 Game of life Theory

we come to learn the first step in creating forms

John Conway, the developer of Game

that are unrecognised by us.

According to Frontiers the unpredictable

of Life, pushed forward a new medium of exploring

results, come in three distinct emergent forms;

with his theory. Conway’s interest in biology

still life, oscillators, and movers. (Frontiers In,

resulted in creating “analogies with the rise, fall

2016) Still, life, similar to its name, describes

and alternations of a society of living organisms”

the stable state of cells or configurations. They

(Gardner, 1970). The Game of Life became

are unaffected by any conditions or variables.

an attractive tool for biologists and computer

Oscillators are configurations that are “stable

scientists. For computer scientists, it was a start of

over a cycle, returning to an initial state.” (Frontiers

a new era for gaming and computation; on the

In, 2016) Lastly, indicated by (author of the article)

other hand, for biologists, it was the beginning

Movers is a rare occurrence where configurations

of the discovery of a new hypothesis. Conway’s

move across the grid.

primary intention was to remodel a biological

process that can result in a chaotic behaviour. The

became associated with John Von Neumann’s

rules were programmed to be simple to achieve

theory of cellular automata as his work was a

the chaos intended.

“Conway described the

contribution to it. Conway becoming the reason

game of life as a board game (1970) for zero or

behind the popularity of cellular automatons

one player, but from the beginning, it was played

pushed him to be seen with negativity. Moreover,

out in a computer format, in a program written by

“Conway’s

Michael Guy and Stephen Bourne. Conway said

automata) is often viewed as a decisive vindication

that without this format some discoveries about

of it, making the approach simpler and easier to

the game would have been difficult to make”

apply.” (Frontier, 2016) However, the contribution

(Gardner, 1970)

of all scientists to the theory of cellular automata

Conway aimed to maintain three

has only escalated the number of ways in which

primary rules within his theory; survivals, deaths,

modern scientists can adapt the approach to

and births. Each rule reflects the name it was given;

current conditions. The theory is evident in the

meaning that cells adapt, emerge and transform

transformation of understanding how each cell is

along with the conditions they are exposed to.

distinctively significant.

Ultimately, Conway’s Game of Life theory

version

of

this

theory

(cellular

Moreover, these rules come into effect only after

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3D represtentation of growth using DLA - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http:// www.entagma.com/vex-in-houdini-diffusion-limitedaggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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3.0 Agent Based Modelling

In order to define ‘Emergence’, scientist needed a more sophisticated tool that

can interpolate its properties in relation to external outputs. Through the invention of computation, one can deduct that emergent structures are a result of interactions and behaviours between several composed elements. For instance, the transition between the micro-level to the macro-level leads to the assembly of aggregate patterns. To yield the aggregate pattern of an unknown behaviour, an ‘Integrative Understanding’ is required. However, to understand the behaviour of a particular pattern that is known, a ‘Differential Understanding’ should be taken into account. (Wilensky & Rand, 2015) An integrative understanding requires a system composed of multiple elements sharing the same characteristics to follow one simple rule.

Fig 18 Life Model on Netlogo - Using ABM Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

In the figure above, the elements are represented as arrows. Each arrow follows

one another to behave like a clock ticking. At the end of each tick, the arrow will follow a particular rule. The system is composed to represent arrows on a circle with a radius of 20 units clockwise. After each tick, the behaviour is initiated to make each arrow move forward 0.35 units and then turn to the right by one degree. The rule will be repeated several times as the clock continues to tick. To change the pattern of the aggregate, one can slightly alter the rules and see how the aggregates will behave. For example, instead of making the arrow move forward by 0.35 unit, the distance can be increased by 0.5 unit. The pattern that will emerge from just changing one small factor of the rule is unpredictable. The circle will expand then contract in a repetitive motion as a result of this change.

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On the other hand, ‘Differential

Understanding’ helps in interpreting the behaviour of a pattern that has emerged as a result of the interactions of the discrete elements. For instance, the pattern generated from the flocking of birds is often perceived as a consequence of two theories “(1) Most subjects did not see any role for randomness in creating these structures, randomness was considered to be destructive of pattern, not a force for creating pattern (the D component); and (2) Most subjects described these patterns as arising from the actions of a centralized controller or orchestrator (the C component).” (Wilensky & Rand, 2015)

Fig 19 Life Model on Netlogo - Using ABM Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Fig 20 Flock of Birds on Netlogo - Using ABM Nathan, A. & Barbosa, V. C. (2008). V-like formations in flocks of artificial birds. Artificial Life, 14(2), pp. 179-188. (available at https://arxiv.org/pdf/cs/0611032.pdf)

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3.0.1 Precedent Studies

Models have been used to help

1971. The applications that ABM can be attributed

individuals better understand and simplify a

to varying significantly. These can take the form

certain real world system. They come in various

of predicting areas such as the stock market, the

types and sizes depending on the problem being

spread of diseases, natural ecosystems, customer

analysed. All models require certain information

buying habits, warfare tactics, and even the

as an input to be processed in order to come up

human immune system, to name just a few (Macal

with a useful output (results). Models are split into

and North, 2010).

two categories; static and dynamic. Static models are models in which the input and the output corresponding to the same point in time. While dynamic models represent models that attempt to the asset to represent the future impact of a particular activity.

Certain rules must be embedded in the

model for its elements to follow to represent the real-world system being studied accurately. For example, to model the solar system, the path of each planet must be defined. Hypothetically this is possible; however, the real-world system can

Fig 21 Ant Model - Netlogo

be represented to a certain degree of accuracy.

Therefore, Continuous-field models are used to in the system with continuously varying estimates

Wilensky, U. (1997). NetLogo Ants model. http://ccl. northwestern.edu/netlogo/models/Ants. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

of the entire model’s property. For instance, instead

of specifying the individual properties of cars in a

computational element in the model with a

traffic jam, the model will take into consideration

set number,rules, properties and actions. For

the time-varying density of cars.

example, if one is to model the behaviour of an

With the inherent advancement of

ant in an ant colony, the ant itself is considered

computing capabilities, the potential of simulating

an agent. The ant has properties such as speed

complex data and patterns is becoming more

and height and can perform different tasks such

apparent. This is due to the fact that computer

as moving, eating, and carrying food. Therefore,

processing power and abilities have been

ABM is used to study how different agents

drastically improved in the past decades. Agent-

interact with one another and how different

Based Modelling (ABM) are computational models

agents will behave. ABM requires simple rules to

that aid individuals understand and describe how

be set in order to be able to simulate the various

any agent is expected to behave. One major

behaviours of the computational agents. This kind

approach that is gaining immense traction is

of systems is also known as complex systems due

agent-based modelling or ABM. “Emerging

to the sophisticated interactions between its parts.

from Cellular Automata, Cybernetics, Chaos,

Furthermore, agent-based modelling models

Complexity, and Complex Adaptive Systems, ABM

these agents individually and is seen as a ‘ground-

is used today to understand and explore complex,

up’ modelling approach. The effect is then seen

nonlinear systems” (Taieh & Evon, 2010). Thomas

on the system as a whole, where interactions,

Schelling introduced the earliest Agent-based

patterns, structures, and behaviours are observed

model concept through his segregation model in

(Macal and North, 2010).

replace the individual behaviour of each element

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Fig 22 Ant Model - Netlogo Wilensky, U. (1997). NetLogo Ants model. http://ccl.northwestern.edu/netlogo/models/Ants. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. An ant gets attracted to a source of food. As the ant carries the food to its nest, the ant leaves a trail behind.

This trail is a chemical reaction that attracts other ants. Each ant will sniff the trail and get attracted to the source of food. As they all gather the food they form a colony and transporthe food from the source to the nest.

+

Fig 23 Ant Model - Netlogo Netlogo allows the user to control the population density, the diffusion-rate of the chemical and the evaporation rate of the chemical. The table shows the amount of food stored in the pile in relation to the time the food is Wilensky, U. (1997). NetLogo Ants model. http://ccl.northwestern.edu/netlogo/models/Ants. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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3.0.2 Agents and Patches All agents have essential standard

rather than a simplified homogeneous population

features that make ABM a unique modelling

used in conventional models- they can represent

process. Agents are autonomous elements

the randomness of the real-world system better.

that are able to absorb, process, and exchange

Randomness is easily merged into the model.

data and information with other agents in the

Agents do not have a predetermined behaviour

model, which makes them an active, rather than

that is set by the user; instead, all of the decisions

a passive, element in the model. They have an

are made by the agent randomly depending

aim that they try to achieve with respect to their

on the agent’s situation and the surrounding

behaviours. Individuals interested in ABM are

environment. Moreover, individual modelling

able to implement awareness into the agents

results in a discrete, and not continuous, results.

used in the model. Agents can be programmed

Continuous models do not represent the real-

to have a sense of direction and to be aware of

world system well. One main advantage of

their surroundings. They can also be supplied

using ABM over conventional models is that

with knowledge about their environment which

ABM does not require any knowledge regarding

provides them with an identity. Agents are

the phenomena that is about to take place. To

assumed to be programmed based on a rational-

demonstrate, an individual modelling a jungle

choice paradigm in which they are able to perform

does not need to have knowledge about the

flawless rational decisions with the information

relationships between different kinds of animals in

they have gathered about their surroundings.

the jungle- for example, which animal is stronger

complex

than others. However, in conventional models,

They

mathematical

are

able

equations

to

solve

optimisation

the individual must have a good understanding

problems using their tremendous analytical

and

of the total behaviour of each animal with respect

abilities. Individuals may also configure their

to other animals and test their assumptions

agents to have the limited rationality to avoid

with the output of the model. ABM models also

the previously mentioned assumptions and

provide more detailed and accurate results since it

avoid unneeded errors. Lastly, all agents can

operates by modelling each individual agent and

communicate with one another, ask questions

based on their aggregated decisions.

about their environment, move around freely and as desired (within the model space) and adapt and learn from their experiences and others’.

ABM is different than other models because they do not solve differential and numerical equations in order to predict the behaviour of the model.

ABM is able to create various elements (agents),

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One

such

application

that utilises

agent-based modelling is NetLogo, which is a multi-agent programming language that models systems by natural and social occurrences. The program is used to model systems that continually undergo changes and evolve by giving the user the ability to individually program various agents that operate and interact with each other simultaneously (Tisue and Wilensky, 2004). In more practical terms, the species, in this case, are the agents that are given a set of rules that define their interactions with the environment. NetLogo would serve to stimulate their growth until a point where the pocket is filled, upon which the species falls out. The key here is to understand the growth patterns of the species so that it can be modelled to resemble a real-life scenario.

To computationally simulate and predict the movements of the agents or species in the overall system, as well as the result of their interactions with the defined boundaries of the pockets in which they operate.

Fig 24 Combined Analysis Jasss.soc.surrey.ac.uk. (2017). Cite a Website - Cite This For Me. [online] Available at: http://jasss.soc.surrey.ac.uk/14/3/7/ rebaudo_et_al_figure1_600px.png [Accessed 10 Jul. 2017].

The above figure illustrates a study to control pest invasion in an agricultural ecology. ABM is used in this context after several studies that were conducted using GIS (Geographic Information System). GIS extracts information of the environmental context, whereas cellular automaton is used to generate the pest population. Both studies is then combined to generate a data driven analysis using ABM.

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3.0.3 Diffusion Limited Aggregation

With each ABM model, it can be observed that the simplest of rules applied

can create some of the most intricate patterns. Hence, it is encouraged to divert our interest from “the complexity of the rules, but instead, on the interaction those rules produce.” The Diffusion-Limited Aggregation model is one that emphasises such patterns. The DLA model is widely known as “a classic example of simplicity at a micro level creating complexity at a macro level.” (Wilensky & Rand, 2015)

Diffusion Limited Aggregation starts off with two simple rules, a ‘static’ agent,

and an ‘active’ agent. The active agent is allowed to move one step at a time. However, this movement can be done randomly. The static agent remains in place until the active agent hits it and turns static. The active agent will be reborn once again, and the process will be repeated multiple times. The rule can be modified to alter the speed of the active agent and increase the chance of it sticking to the static agent. This can be achieved by adjusting the boundary of the static agent, thus, making the active agent move towards the static agent rather than wandering around. The active agent does not move towards the static agent in a linear manner but instead tries to find all possible directions to reach the static agent.

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+ Type: Point Attractor

+

Fig 25 DLA Point Attractor

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Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http:// paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

Figure above shows a point attractor DLA. The Alive Agents bounces back and forth randomly until it hits a static point attractor and dies.

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Type: Line Attractor

Fig 26 DLA Line Attractor Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http:// paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

Figure above shows a line attractor DLA. This model illustrates the agents falling from top to bottom as they get attracted to the surface.

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Type: Rectangle Attractor

Fig 27 DLA Rectangle Attractor

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Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http:// paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

Figure above shows a rectangle attractor DLA. The agents are born in the origin of the model and gets attracted towards the boundary.

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Type: Circular Attractor

Fig 28 DLA Positive Circle Attractor Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http:// paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

Figure above shows a circular attractor DLA. The agents move randomly in this model however it gets attracted towards the circle boundary.

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Type: Boundary Circular Attractor

Fig 29 DLA Negative Circle Attractor

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Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http:// paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

Figure above shows an inner circular boundary attractor DLA. The agents are born on the origin of the circle and gets attracted towards the boundary of the circle. In this model, a color attribute has been added to differentiate the multiplegenerations that were born.

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+

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Probability of sticking: 0.2 Through using Netlogo, Growth simulation is easy to achieve by changing variables of the existing model.

Fig 30 DLA Point Attractor

+

Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/ models/DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL

In previous studies, the DLA model was able to create complex patterns that are highly similar

to those found in nature including “social patterns such as growth of cities.” It uses three related extensions to observe the generation of these varying patterns. The first two extensions “modified how the particles decide when to stop moving, and the final extension added the idea of starting from multiple seeds (multiple agents).” (Wilensky & Rand, 2015)

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Number of ticks: 1 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 50 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 250 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 500 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 1000 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 1500 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 2500 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 2500 unit Density: 4000 ‘Alive’ agents Angle: 60 degrees

Fig 31 DLA Point Attractor Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/ netlogo/models/DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL

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+

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Probability of sticking: 0.05

Fig 32 DLA Point Attractor

+

Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/ models/DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL

The first extension allows the DLA user to be in control of the probability of stopping the variable agents. As stated by the study “one way to think of this system is that if a particle comes into contact with a stationary object, then it becomes stationary itself with 100 percent probability.� With this extension, a change in the resulting patterns can be observed by manipulating the probability by either increasing or decreasing the number of variable agents generated.

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Number of ticks: 1 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 50 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 250 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 500 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 1000 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 1500 unit Density: 2500 ‘Alive’ agents Angle: 60 degrees

Number of ticks: 2500 unit Number of ticks: 2500 unit Density: 2500 ‘Alive’ agents Density: 4000 ‘Alive’ agents Angle: 60 degrees Angle: 60 degrees Fig 33 DLA Point Attractor Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/ netlogo/models/DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL

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+

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Probability of sticking: 0.01

Fig 34 DLA Point Attractor

+

Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/ models/DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL

The second extension further manipulates the probability of a variable agent coming to a stop by

exploring “how the probability of sticking is related to the number of neighbours that are already stationary.�

These aforementioned neighbouring agents surrounding the variable agent play a very influential role in the resulting pattern. In this case, the probability of the variable agent to be surrounded by stationary agents is much higher than in the first extension, thus resulting in a pattern that can most likely resemble a blob or a clot.

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Number of ticks: 1 unit Probability of Sticking: 0.5 Neighbor influence: ON

Number of ticks: 50 unit Probability of Sticking: 0.5 Neighbor influence: ON

Number of ticks: 250 unit Probability of Sticking: 0.5 Neighbor influence: ON

Number of ticks: 500 unit Probability of Sticking: 0.5 Neighbor influence: ON

Number of ticks: 1000 unit Probability of Sticking: 0.5 Neighbor influence: ON

Number of ticks: 1500 unit Probability of Sticking: 0.5 Neighbor influence: ON

Number of ticks: 2500 unit Number of ticks: 4000 unit Probability of Sticking: 0.5 Probability of Sticking: 0.01 Neighbor influence: ON Neighbor influence: OFF Fig 35 DLA Point Attractor Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/ netlogo/models/DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL

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+

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Number of ticks: 1 unit Probability of Sticking: 1.0 Neighbor influence: OFF

Number of ticks: 50 unit Probability of Sticking: 1.0 Neighbor influence: OFF

Number of ticks: 250 unit Probability of Sticking: 1.0 Neighbor influence: OFF

Number of ticks: 500 unit Probability of Sticking: 1.0 Neighbor influence: OFF

Number of ticks: 500 unit Probability of Sticking: 0.5 Neighbor influence: OFF

Number of ticks: 500 unit Probability of Sticking: 0.5 Neighbor influence: ON

Number of ticks: 500 unit Probability of Sticking: 1.0 Neighbor influence: ON

Number of ticks: 500 unit Probability of Sticking: 0.01 Neighbor influence: ON

Fig 36 DLA Point Attractor

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Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3model. http://ccl.northwestern.edu/netlogo/ models/DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL

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Moreover, even though with DLA model it is best to keep

previous results when extending the model in order to compare with new achievements, the original results are almost of no use. Due to

+

the three extensions of the DLA model, particularly the third, it can sometimes be “simpler to deviate from the original version, and not much is lost by doing so.” That is because the final extension allows the user of the model to be in control of the placement of the variable agent from the very beginning of the study. By placing the agent at random and not in the centre of the “world”, the user of the model gives room for further patterns that are unpredictable and more exciting.

An analogy of the application can be made through a real-life

situation such as the ability to understand the behaviour of intoxicated individuals after they exit the taverns. For instance, people exiting the bar one at a time stagger and move randomly around the plaza until they hit a drunk asleep person. The drunk person who was moving randomly will be lulled by the individual who has already fallen asleep and falls asleep as well. This situation has repeated a multitude of times

+

until an entire group of drunk people follow the same behaviour as the previous. The difference is that some individuals might take longer than the other to get lulled by the same habit.

+

Number of seed: 10

Probability of Sticking: 1.0

Probability of Sticking: 0.5

Probability of Sticking: 0.01

Fig 37 Network Diagram The above diagram shows that the probability of sticking results in affecting the overal volume of the growing seed.

ElJamal, S. (2017). Proliferate - Permeable Plateau.

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+ Fig 38 Permeable Plateau Top View ElJamal, S. (2017). Proliferate - Permeable Plateau.

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Implementation

The Permeable Plateau

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Fig 39 Permeable Plateau Top View ElJamal, S. (2017). Proliferate - Permeable Plateau.

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4.0.1 The Permeable Plateau The Plateau is a biological horizontal urban plaza that draws the boundaries between two main factors, growth and nongrowth. The urban plaza consists of multi-functional zones that include a pedestrian pathway, seating areas and growth components. The Permeable Plateau is a surface that captures different microbial species and promotes growth in a controlled state. The horizontality of the project is a key factor that aids in simplifying the complexity of the form that used to take part in the design process of any verticle element.

The Project is made up of three different geometrical components that multiply and assemble to form multi-functional zones. The horizontal transition between the elements is inspired by the Highline project in New York. The Highline is an elevated park that was refurbished from being a train line into an urban landscape.

The key features of the Highline focus on the transition between different elements. These factors include pathways, benches, seating areas, plantation and staircases. However, the focal point of this project underlines the importance of non-living materials and living species. The Highline draws a clear boundary between the materials used for pathways, seating areas and the organic plantations integrated within the design.

The permeable plateau breaks the limit of these two elements by introducing a multi-biorecptive material that can host multiple species without

the need of artificial plantations. Moreover, the Highline lacks a higher level of intelligence from a computational perspective. The components used in the project are perceived to be static rather than dynamic. Nevertheless, the permeable plateau aims to explore further the transitional elements introduced in the Highline project by adding more features that are computational, biologically and ecologically driven.

Humdity + Shadow Analysis Modelled on Houdini FX

Growth Analysis

Fig 40 Environmental Studies ElJamal, S. (2017). Proliferate - Permeable Plateau.

Fig 41 Component Typologies Dewi, M., Jeng, Y., Ding, H. Yucel, I., ElJamal, S. (2017). Proliferate - Permeable Plateau.

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Fig 42 Modified Representation - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (P Rendering in Mantra & Redshift) | Entagma.

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CHAPTER V: Macro and MicroColonies

Brophytes and Microbes

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5.0.1 Network of Interactions DLA can analyse different behaviours ranging from how humans interact with one another under certain circumstances to how microbes survive and affect other dominant species.

Terrestrial species are used in the realm of design to investigate the probabilities of creating a living bio-receptive materials.

The constant need

for adapting to the conditions posed by the surrounding environment, without the necessity of any mechanical equipment, resulted in a crosslink between the fields of biology, computer science and design. Understanding the synthetic behaviour between the species and its biological process is a distinct approach towards creating the right containment to host and cultivate them.

behavioural interaction between the species; Trophic Network and a Mutualistic Network (Karimi & Maron, 2017). “Trophic networks have been very widely studied and refer solely to the feeding relationships between the consumers and their resource(s)” (Karimi & Maron, 2017).

Whereas, “Mutualistic networks have been little studied at the microbial scale and those existing between plants and pollinating insects are better known” (Karimi & Maron, 2017).

After understanding the network of

interaction that can occur between the various

A network of interactions is established

as an emergent property of the interlink between the different kind of species throughout time (Bertrand et al. 2011). “Interactions can be beneficial, antagonistic or neutral regarding the impact they have on the species involved” (Lidicker, 1979). ‘Beneficial interactions’ is when the subject just benefits from one of the two allies. This act can be seen as commensalism, as the other partner who does not profit from the subject will not lose any characteristics. For instance, bio-degradation, “which corresponds to the consumption by commensal organisms of compounds produced by other members of the community.” (Karimi & Maron, 2017) Symbiosis is a sector of Mutualism, in which the interaction between the subject and the partner can affect the growth of that partner. On the other hand, ‘Antagonistic attraction’ is a predator and prey interaction. The interaction can influence the solemn existence of one of the partners. Two type of networks is used to distinguish that different

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Fig 43 A Mutualistic Network, Which focuses on the symbiotic behavior of the microbial species.

Link-springer-com.libproxy.ucl.ac.uk. (2017). Shibboleth Authentication Request. [online]

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Link-springer-com.libproxy.ucl.ac.uk. (2017). Shibboleth Authentication Request. [online] Fig 44 In the first scheme, the diagram illustrates the strengths and limitations of the microbial diversity. The ‘Taxa’ in this diagram is an abbreviation of taxonomy and functionility of the microbial species.The second scheme suggests a network hat connects the microbes with one another as a result of the environmental quality.

Fig 45 Link-springer-com.libproxy.ucl.ac.uk. (2017). Shibboleth Authentication Request. [online] A Trophic Network, which is also discribed as the food network, describes the predator and prey beahvior.

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The agents has different colors to categories them based on their classification. The agents compete to dominate one another, resulting into different colonies.

Fig 46

In this study, the dominent agents are the green species.

Fig 47

After few runs, the Red agents become more dominent.

Fig 48

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After few runs, the Grey Agents become more dominent. with 39 different species.

Fig 49

The Ticks has two elements, one that carries the information from the surrounding environment or the from the other creatures. The other is the chromosomes that carries the genetic material Fig 50

5.0.2 Computing Behaviours

Another method used to illustrates the

interactional behaviour between the different level of species is through John Holland’s model using ABM, Echo. In this model, Holland refers to the agents as creatures, and the agents with similar mating preference as species. The creatures are consumers who can exchange resources within one another. The model describes the idea of emergence and the evolution process. However, one can take this phenomenon and transform it into an evolution of different market that can contribute to the understanding of the economy.

Wilensky, U. (2005). NetLogo Echo model. http://ccl.northwestern.edu/netlogo/models/Echo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

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species, the symbiotic behaviour between

both starches.

Bryophytes (Mosses), Algae and Fungi will

7.

be explored to achieve a multi-dynamic

composition of the cell wall are

platform. Bryophytes, “a group of lower

identical in both bryophytes and

non-vascular plants that is composed of

algae. It consists of a wall layer made

Musci (mosses), Hepaticae (liverworts),

of cellulose surrounded by a pectic

and

wall

been

Anthrocerotae

(hornworts),

taxonomically

placed

have

between

The

containing

structure

and

galacturonic

the

acid.”

(Plantscience, 2016)

the algae and the pteridophytes, as first terrestrial plants.” (Kenrick and Crane, 1997; Edwards et al., 1995). Moss, in particular, is a small rootless plant, which is under the bryophytes category. Mosses grow as patches on wet surfaces. However, the difference between Moss and a natural plant is that Moss does not absorb water from the ground but instead from rain or surrounding surfaces. The symbiotic relationship between Moss, Fungi and Algae, is that all three species require the need to live in damp areas. Moss and Algae are similar in many ways; “ 1.

Absent Vascular Tissue.

2.

Autotrophic mode of nutrition

3.

Conspicuous plant in the life

cycle being the gametophyte 4.

Absence of roots

5.

Retention

of

the

swimming

habits by the sperms which indicates the

Fig 50 : Bryophyte Growth Diagram

algal ancestry of the bryophytes.

6.

The carbohydrate food reserve

material in both bryophytes and algae is

64

Link-springer-com.libproxy.ucl.ac.uk. (2017).

Shibboleth Authentication Request. [online]

Dissertation: 2017

Proliferated

Fig 51 Jill Harrison, C. (2017). Development and genetics in the evolution of land plant body plans.

Simulating Moss based on DLA.

Fig 52 Jill Harrison, C. (2017). Development and genetics in the evolution of land plant body plans.

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Algae - Macro Scale

Algae - Micro Scale - Probability of sticking 0.01

Fig 53 Ak3.picdn.net. (2017). MicrobialAlgae.

Fig 54 Ak3.picdn.net. (2017). MicrobialAlgae.

Fungi - Macro Scale

Fungi - Micro Scale - Probability of sticking 0.5

Fig 55 E., 2017. Saprophyte -. Biology Dictionary. Available at: https://biologydictionary.net/saprophyte/ [Accessed July 14, 2017].

Fig 56 Anon, Image Gallery: Mycelium Microscope. Image Gallery Mycelium Microscope. Available at: http://keywordsuggest. org/gallery/954248.html [Accessed July 14, 2017].

Bryophytes - Macro Scale

Bryophytes - Micro Scale - Probability of sticking 1.0

Anon, Moss and Stone Gardens. Moss and Stone Gardens. [Accessed July 13, 2017].

Anon, HEXAGON. Patternity

Fig 57 Algae

Fungi + Algae

Lichens

On the other hand, Fungi and algae

growth mechanism, in which the cell expansion

have a stronger symbiosis with each other. The

is restricted to the boundary at the tip of the

relationship between the two produces another

cell. (Knight, 2009) Moss growth in a controlled

type of species known by Lichen. Algae as

environment was recorded in a time-lapse manner

microorganism have the ability to dry in a short

to track the growth process of the cell structure.

amount of time, and because it’s a small organism

The video showed that the diffused growth of the

in comparison with fungi and moss, it needs

body of the cell did not affect the growth form

protection. Fungi protect Algae in an exchange of

of the cell. Instead, the main changes occurred

nutrients that are provided by Algae.

elongated along the tip of the cell (Knight, 2009).

Growth patterns illustrated in Bryophytes and Fungal species led to the formation of antistrophic

66

Anon, Novel Fungal Pelletization-Assisted Technology for Algae Harvesting and Wastewater Treatment. the process for fungi algae pallets formation. Available at: researchgate.net .

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Time - Lapse - Species Experiment - Controlled Bryophytes

Fungi

Algae

Day 01

Day 01

Day 01

Day 05

Day 05

Day 05

Day 10

Day 10

Day 10

Day 15

Day 15

Day 15

Day 20

Day 20

Day 20

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Fig 58 - Time-Lapse Growth Study conducted by Marisa Dewi - ElJamal, S. (2017). Proliferate - Permeable Plateau.

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+ Fig 59 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/vexin-houdini-diffusion-limited-aggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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Netlogo- DLA - Algae

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Probability of Sticking: 0.1 Density: 4000 Time : 1

Probability of Sticking: 0.1 Density: 4000 Time : 50

Probability of Sticking: 0.1 Density: 4000 Time : 100

Probability of Sticking: 0.1 Density: 4000 Time : 250

Probability of Sticking: 0.1 Density: 4000 Time : 500

Probability of Sticking: 0.1 Density: 4000 Time : 1000

Probability of Sticking: 0.1 Density: 4000 Time : 1500 Fig 60

Probability of Sticking: 0.1 Density: 4000 Time : 2000

Probability of Sticking: 0.1 Density: 4000 Time : 2500

Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension3.

The components in the permeable plateau are divided into two categories, biological and non-biological. The geometry of the elements is driven computationally to tessellate a higher level of complexity. The materials act as a host to different kinds of species that can attract one another to colonise and promote a new form of growth. The layering of the project is an important key feature that can construct a design strategy towards the future of bio-receptive materials.

The components are further developed by using the external output to affect the final result. The biological components are influenced by the environmental context and the typology of the

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microbial species it inhabits. The second level of computation will be implemented to drive the force of growth in the direction needed.

DLA equation: the level of stickiness = Amount of shade (min 0.1 max 1.0) Density of the ‘moving’ particles = level of humidity If level of stickiness = 0.1 Density of particles = 4000 The growth level increases and escalates.

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Dissertation: 2017

Houdini- DLA - Algae

Proliferated

Probability of Sticking: 0.1 Density: 400 Time : 1

Probability of Sticking: 0.1 Density: 400 Time : 50

Probability of Sticking: 0.1 Density: 400 Time : 100

Probability of Sticking: 0.1 Density: 400 Time : 250

Probability of Sticking: 0.1 Density: 400 Time : 500

Probability of Sticking: 0.1 Density: 400 Time : 1000

Probability of Sticking: 0.1 Density: 4000 Time : 1

Probability of Sticking: 0.1 Density: 4000 Time : 50

Probability of Sticking: 0.1 Density: 4000 Time : 100

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Probability of Sticking: 0.1 Probability of Sticking: 0.1 Probability of Sticking: 0.1 Density: 4000 Density: 4000 Density: 4000 Time : 250 Time : 500 Time : 1000 Fig 61 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/vexin-houdini-diffusion-limited-aggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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Netlogo- DLA - Fungi

Probability of Sticking: 0.5 Density: 4000 Time : 1

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Probability of Sticking: 0.5 Density: 4000 Time : 50

Probability of Sticking: 0.5 Density: 4000 Time : 250

Probability of Sticking: 0.5 Density: 4000 Time : 500

Probability of Sticking: 0.5 Density: 4000 Time : 1500 Fig 62

Probability of Sticking: 0.5 Density: 4000 Time : 2000

Sara El Jamal

Probability of Sticking: 0.5 Density: 4000 Time : 100

Probability of Sticking: 0.5 Density: 4000 Time : 1000

Probability of Sticking: 0.5 Density: 4000 Time : 2500

Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension3.

Fig 63 ElJamal, S. (2017). Proliferate - Permeable Plateau.

The effect of data-driven factors, such as the sticking probability, and the density of the attractors can contribute to the possibility of controlling growth in relation to the required design.

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DLA equation: the level of stickiness = Amount of shade Density of the ‘moving’ particles = level of humidity If level of stickiness = 0.5 Density of particles = 4000 The growth level increases and escalates.

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Houdini- DLA - Fungi

Proliferated

Probability of Sticking: 0.01 Density: 400 Time : 1

Probability of Sticking: 0.01 Density: 400 Time : 50

Probability of Sticking: 0.01 Density: 400 Time : 100

Probability of Sticking: 0.01 Density: 400 Time : 1000

Probability of Sticking: 0.01 Density: 400 Time : 2000

Probability of Sticking: 0.01 Density: 400 Time : 2500

Probability of Sticking: 0.5 Density: 400 Time : 1

Probability of Sticking: 0.5 Density: 400 Time : 50

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Probability of Sticking: 0.5 Density: 400 Time : 100

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Probability of Sticking: 0.5 Probability of Sticking: 0.5 Probability of Sticking: 0.5 Density: 400 Density: 400 Density: 400 Time : 1000 Time : 2000 Time : 2500 Fig 64 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/vexin-houdini-diffusion-limited-aggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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Probability of Sticking: 1.0 Density: 4000 Time : 1

Probability of Sticking: 1.0 Density: 4000 Time : 50

Probability of Sticking: 1.0 Density: 4000 Time : 100

Probability of Sticking: 1.0 Density: 4000 Time : 250

Probability of Sticking: 1.0 Density: 4000 Time : 500

Probability of Sticking: 1.0 Density: 4000 Time : 1000

Probability of Sticking: 1.0 Density: 4000 Time : 1500 Fig 65

Probability of Sticking: 1.0 Density: 4000 Time : 2000

Probability of Sticking: 1.0 Density: 4000 Time : 2500

Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension3.

Fig 66 ElJamal, S. (2017). Proliferate - Permeable Plateau.

In the Figure above, one can start investigating the possible growth factor by inputting the forces gathered from the surrounding environment. For instance, to control the growth of the bryophyte moss category, an understanding of the environmental conditions required for this entity to survive can result in knowing the right factors to control and sustain its growth behaviour. Moss requires a damp surface

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with a minimum amount of sunlight.

DLA1 equation: the level of stickiness = Amount of shade Density of the ‘moving’ particles = level of humidity If level of stickiness = 1.0 Density of particles = 4000 The growth level increases and escalates.

1

Differential Limited Aggregation

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Fig 67 ElJamal, S. (2017). Proliferate - Permeable Plateau.

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Fig 68 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/vexin-houdini-diffusion-limited-aggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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+ Fig 69 Multi Cultural Diversity - ElJamal, S. (2017). Proliferate - Permeable Plateau.

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CHAPTER VII: Taxonomy Cultivating Species

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Relationship Matrix Fig 70

The figure shows the

Multi-Species

Connectivity

Behaviour

+

relationship between different species and their behaviour based on negative and positive

interaction with one another.

Clusters of species

The ‘A’ is dominent,

Search Radius: 2.7 A & B has a negative Max Search Radius: 36 interaction, A & D has a units positive interaction.

Symbiosis

Core

Dominence

the interaction

between A and

the other species is determined by the symbiotic behavior of each category.

D: Algae

D: two positive interactions

A, D, C has the most D has the most connections within each centrality which makes other.

A lose dominence.

Complex Interactions Powerful Interactions Positive Int.

A: Moss

C: Fungi As mentioned in the previous chapter, C

and D have a positive interaction with each other and produce

Possibility of other 7 links with ratio of 4:3 interaction: 42 Postive in control Negative Int.

Intersections

D: has the most positive interactions Excluded

another form of species, Lichens.

The Relationship

matrix can provide a taxonomy of possible interactions.

+

D, C, F, and E each has one negative interaction.

Close proximity between D, F, E, C

B has the least level of dominence

Logic Modified by ElJamal, S. (2017). Proliferate - Permeable Plateau. Metrics developed by: Link-springer-com. libproxy.ucl.ac.uk. (2017). Shibboleth Authentication Request. [online] Component Morphology

+

Points Scattered

Fig 71

Connected Adjacent Points

SDF Volume is created

ElJamal, S. (2017). Proliferate - Permeable Plateau.

Three primary inputs are used to generate these geometrical elements: The Voxel to fracture,

the points to build around each cell, and the SDF volume to produce the depth of the cells. The interior on the surface is designed for each piece within the volume. The materiality of the project is an essential element that is diverted by the species that inhabits it.

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Proliferated

Dissertation: 2017 Taxonomy Matrix

Static Points

Growth Limitation

Attracting Network

A: Moss Growth attractors

D: Algae Growth attractors

E: Hybrid symbiosis

C: Fungi Growth attractors

Moss Growth expanding within the network

Algae Growth expanding within the network

Hybrid allocated between Algae and Fungi

Fungi Growth expanding within the network

Voronoi - DLA symbiosis

Fig 72

ElJamal, S. (2017). Proliferate - Permeable Plateau.

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+ 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/vex-in-houdini-diffusion-limited-aggregation-plus-renderingin-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

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Proliferated

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No longer are we limited to only looking at the surface of matters and materials.

With the theories explored in ‘A New Kind of Science,’ ‘Game of Life,’ and ‘DLA,’ we arrive at the knowledge that we have only touched the tip of the iceberg when learning about the complexity of the production of these matters and materials. We now have in the palm of our hands the tools and skills to further study in minute details the intricate patterns that form our everyday surroundings.

With what we have now from agent based models to rigorous studies, we can analyse the behaviour of complex systems through integrating elements from nature, surroundings and the species requirements to survive in the world that we are in now. To further our understanding of the materials that are continuously advancing and demanding, we require the aid of technology that also advances in line with our demands for it.

Ultimately, we should not be satisfied with the results before us. We cannot be idle in the face of constant development from life, nature to technology and other factors that directly and indirectly affect our surroundings.

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+ Bibliography 1.

+

Abstract: Nature reviews Molecular cell biology’, Nature Reviews Molecular Cell Biology, 9(8), pp.

2. 593–602. doi: 10.1038/nrm2460.

3. Anon, Plant Science 4 U. Similarities between Algae and Bryophytes. Available at: http://www. plantscience4u.com/2013/07/similarities-between-algae-and.html#.WWiOnYjyt3g [Accessed July 14, 2017].

4. Anon, (2017). [online] Available at: http://www2.hawaii.edu/~nreed/ics606/papers/Macal06model.pdf [Accessed 31 May 2017] 5. Caballero, L., Hodge, B. & Hernandez, S., 2016. Conway’s “Game of Life” and the Epigenetic Principle.

Frontiers in Cellular and Infection Microbiology. Available at: https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC4905947/ [Accessed July 14, 2017].

6. Coursehero.com. (2017). As many more individuals of each species are born than can possibly survive [online] Available at https://www.coursehero.com/file/p66o1po/As-many-more-individuals-ofeach-species... [Accessed 31 May 2017].

7.

DE QUEIROZ, K. (2017). Branches in the lines of descent: Charles Darwin and the evolution of the species concept.

8. Darwin C. On the origin of species. John Murray; London: 1859.

9. Gánti, T. (2003). The principles of life. Oxford: Oxford University Press 10. Gardner, M., 1984. Codes, ciphers and secret writing, New York: Dover.

11. GLOBAL4CAST.ORG. (2017). Entropy of the (International) System. [online] Available at: https:// global4cast.org/2017/02/08/entropy-of-the-system/ [Accessed 31 May 2017].

12. Ghose, A., 1940. The life divine, Calcutta: Arya Pub. House.

13. Hensel, M. (2006). (Synthetic) life architectures: ramifications and potentials of a 14. literal biological paradigm for architectural design. Architectural Design, 76(2), 18-25.

15. doi:10.1002/ad.236 16. Hensel, M. (2006). Computing self-organisation: environmentally sensitive growth modelling.

Architectural Design, 76(2), pp.12-17.

17. Holland, J.H., 1996. Hidden order: how adaptation builds complexity, Reading, MA: Perseus Books 18. Holland, John H. (1998), Emergence from Chaos to Order, Oxford University Pres 19. Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/book/ chapter-7-cellular-automata/ [Accessed 31 May 2017].

20. Ias.ac.in. (2017).

+

21. Johnson, S. (2001). Emergence. New York: Scribner.

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+ 22. Kenrick, P. & Crane, P. R. Water-conducting cells in early fossil land plants: implications for the early evolution of tracheophytes. Bot. Gaz. 152, 335–356 (1991).

23. Lewes, G.H., 1877. Problems of life and mind, London: Trübner. Mathematics of Computation, 21(100), p.745.

24. Lidicker, W.Z., 1979. Clarification of Interactions in Ecological Systems | BioScience | Oxford Academic. OUP Academic. Available at: https://academic.oup.com/bioscience/articleabstract/29/8/475/306030/A-Clarification-of-Interactions-in-Ecological?redirectedFrom=fulltext [Accessed July 14, 2017 25. Macal, C. & North, M., 2014. Introductory tutorial: Agent-based modeling and simulation.

Proceedings of the Winter Simulation Conference 2014.

26. NetLogo, Cambridge (MA): MIT Press.

27. Oxman, N. (2015) Design at the intersection of technology and biology.

28. Rafelski, S.M. and Marshall, W.F. (2008) ‘Building the cell: Design principles of cellular architecture: 29. Rocha, Luis M. [1999].”Syntactic Autonomy: or why there is no autonomy without symbols and how self-organizing systems might evolve them.” New York Academy of Sciences. In Press.

30. Simon H.A. (1962): The Architecture of Complexity, Proceedings of the American Philosophical Society 106, p. 467-482, reprinted in: 31. Simon H.A. (1981) : The Sciences of the Artificial (2nd ed.), (MIT Press, Cambridge MA).

32. Schwartz, J.T., Neumann, J.V. & Burks, A.W., 1967. Theory of Self-Reproducing Automata.

33. Thackara, J. (2005) In the bubble. Cambridge, Mass.: MIT Press.

34. Wolframscience.com. (2017). An Outline of Basic Ideas: A New Kind of Science | Online by Stephen Wolfram [Page 1]. [online] Available at: http://www.wolframscience.com/nks/p1--an-outline-of-basicideas/ [Accessed 31 May 2017].

35. Wilensky, U. & Rand, W., 2015. Introduction to agent-based modeling: modeling natural, social, and engineered complex systems with

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List of Illustrations

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List of Figure ElJamal, S. Dewi, M. Ding, H. Yucel, I. Jeng, Y. (2017). Proliferate - Permeable Plateau.

11 Fig 1 11 Permeable Plateau - Conceptual Render 11 Fig 2 14 The Life Cycle of Moss Growth (Physcomitrella Patens) 14 Frontiersin.org. (2017). development of psychomitrella. [online] Available at: http://www.frontiersin.org/files/ Articles/25695/fpls-03-00166-HTML/image_m/fpls-03-00166-g001.jpg [Accessed 10 Jul. 2017].

14 ElJamal, S. (2017). Proliferate - Permeable Plateau.

17 Fig 3 17 Conceptual Illustrtation of Reductionism - 17 QUEIROZ, K.E.V.I.N.D.E., 2011. Branches in the lines of descent: Charles Darwin and the evolution of the species concept | Biological Journal of the Linnean Society | Oxford Academic. OUP Academic. Available at: https://academic.oup.com/ biolinnean/article/103/1/19/2452442/Branches-in-the-lines-of-descent-Charles-Darwin [Accessed July 14, 2017].

19 Fig 5 Ancestor Diagram 19 Fig 6 21 Complex systems interact with the environment along other kinds of interactions which include “emergent systems properties - the higher level features; interdependencies between these elements and emergent properties; the multiple nested levels and hierarchies that can form; and if the elements and properties are included in other systems.� (Lucas, 2016). Web. Photofugue.com.au. (2017). processPhotography - Introducing Complexity. [online] Available at: http:// photofugue.com.au/Pages/Introducing%2520Complexity.html [Accessed 10 Jul. 2017]. 21 Fig 7 22 Ganti Chamoton reflect the origin of life in three different subsystems, Membrane that act as a protection boundry, the self-sustaining cycle, which can also act as a metabolic factor that keeps the system running, and the information carrying the replicating polymer that generates another cellular growth similar to the parent.

22 https://www.researchgate.net/figure/275101380_fig1_Fig-1-Ganti%27s-chemoton-is-made-up-of-three-tightly-coupledsubsystems 22 ElJamal, S. (2017). Proliferate - Permeable Plateau.

23 Fig 8 23 Conceptual Illustrtation of branching growth 23 Fig 9 24 Conceptual Render of the Permeable Pleateau.

24 ElJamal, S. (2017). Proliferate - Permeable Plateau. 24 Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/book/chapter-7-cellularautomata/ [Accessed 10 Jul. 2017].

27 Fig 10 27 Conceptual Illustrtation of Cellular Automata 27 Fig 11 28 Snow Flakes Cellular Automaton simulation.

28 Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/book/chapter-7-cellularautomata/ [Accessed 10 Jul. 2017].

28 Fig 12 28 3D Cellular Automaton simulation.

28 Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/book/chapter-7-cellularautomata/ [Accessed 10 Jul. 2017].

28 Fig 13: Rule 222 29 Fig 14: Rule 90 Fractal Resolution 29 Fig 15: Rule 90 Repetitive Pattern with a higher resolution 29 Your Bibliography: Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/ book/chapter-7-cellular-automata/ [Accessed 10 Jul. 2017].

29 Fig 16 30 Cellular Automata Model on Netlogo - Using ABM 30 Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. 30 Fig 17 31 Game of Life illustration 31 Your Bibliography: Natureofcode.com. (2017). The Nature of Code. [online] Available at: http://natureofcode.com/ book/chapter-7-cellular-automata/ [Accessed 10 Jul. 2017].

31 Fig 18 34 Life Model on Netlogo - Using ABM 34 Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for Connected Learning

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and Computer-Based Modeling, Northwestern University, Evanston, IL. 34 Fig 19 35 Life Model on Netlogo - Using ABM 35 Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. 35 Fig 20 35 Flock of Birds on Netlogo - Using ABM 35 Nathan, A. & Barbosa, V. C. (2008). V-like formations in flocks of artificial birds. Artificial Life, 14(2), pp. 179-188. (available at https://arxiv.org/pdf/cs/0611032.pdf) 35 Fig 21 36 Ant Model - Netlogo 36 Wilensky, U. (1997). NetLogo Ants model. http://ccl.northwestern.edu/netlogo/models/Ants. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. 36 Fig 22 37 Ant Model - Netlogo 37 Wilensky, U. (1997). NetLogo Ants model. http://ccl.northwestern.edu/netlogo/models/Ants. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. 37 Fig 23 37 Ant Model - Netlogo 37 Wilensky, U. (1997). NetLogo Ants model. http://ccl.northwestern.edu/netlogo/models/Ants. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. 37 Fig 24 39 Combined Analysis 39 Jasss.soc.surrey.ac.uk. (2017). Cite a Website - Cite This For Me. [online] Available at: http://jasss.soc.surrey.ac.uk/14/3/7/ rebaudo_et_al_figure1_600px.png [Accessed 10 Jul. 2017].

39 Fig 25 41 DLA Point Attractor 41 Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http://paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

41 Fig 26 42 DLA Line Attractor 42 Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http://paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

42 Fig 27 43 DLA Rectangle Attractor 43 Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http://paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

43 Fig 28 44 DLA Positive Circle Attractor 44 Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http://paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

44 Fig 29 45 DLA Negative Circle Attractor 45 Paulbourke.net. (2017). DLA - Diffusion Limited Aggregation. [online] Available at: http://paulbourke.net/fractals/dla/ [Accessed 10 Jul. 2017].

45 Fig 30 46 DLA Point Attractor 46 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 46 Fig 31 47 DLA Point Attractor 47 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 47 Fig 32 48 DLA Point Attractor 48 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 48 Fig 33 49 DLA Point Attractor 49 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/models/

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+ DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 49 Fig 34 50 DLA Point Attractor 50 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 50 Fig 35 51 DLA Point Attractor 51 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 1 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 51 Fig 36 52 DLA Point Attractor 52 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension1. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 52 Fig 37 53 Network Diagram 53 ElJamal, S. (2017). Proliferate - Permeable Plateau.

53 Fig 38 54 Permeable Plateau Top View 54 ElJamal, S. (2017). Proliferate - Permeable Plateau.

54 Fig 39 56 Permeable Plateau Top View 56 ElJamal, S. (2017). Proliferate - Permeable Plateau.

56 Fig 48 62 Fig 49 63 Fig 50 63 Wilensky, U. (2005). NetLogo Echo model. http://ccl.northwestern.edu/netlogo/models/Echo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. 63 Fig 50 : 64 Bryophyte Growth Diagram 64 Link-springer-com.libproxy.ucl.ac.uk. (2017). Shibboleth Authentication Request. [online] 64 Fig 51 65 Jill Harrison, C. (2017). Development and genetics in the evolution of land plant body plans.

65 Fig 52 65 Jill Harrison, C. (2017). Development and genetics in the evolution of land plant body plans.

65 Fig 53 66 Ak3.picdn.net. (2017). MicrobialAlgae.

66 Fig 55 66 E., 2017. Saprophyte -. Biology Dictionary. Available at: https://biologydictionary.net/saprophyte/ [Accessed July 14, 2017].

66 Anon, Moss and Stone Gardens. Moss and Stone Gardens. [Accessed July 13, 2017].

66 Fig 54 66 Ak3.picdn.net. (2017). MicrobialAlgae.

66 Fig 56 66 Anon, Image Gallery: Mycelium Microscope. Image Gallery Mycelium Microscope. Available at: http://keywordsuggest. org/gallery/954248.html [Accessed July 14, 2017].

66 Anon, HEXAGON. Patternity 66 Anon, Novel Fungal Pelletization-Assisted Technology for Algae Harvesting and Wastewater Treatment. the process for fungi algae pallets formation. Available at: researchgate.net .

66 Fig 57 66 Fig 59 68 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/ vex-in-houdini-diffusion-limited-aggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

68 Fig 60 70 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension3.

70 Fig 61 71 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion

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71 Fig 62 72 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension3.

72 Fig 63 72 ElJamal, S. (2017). Proliferate - Permeable Plateau.

72 Fig 64 73 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/ vex-in-houdini-diffusion-limited-aggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

73 Fig 65 74 Wilensky, U., Rand, W. (2006). NetLogo DLA Simple Extension 3 model. http://ccl.northwestern.edu/netlogo/models/ DLASimpleExtension3.

74 Fig 66 74 ElJamal, S. (2017). Proliferate - Permeable Plateau.

75 3D represtentation of Moss growth - Houdini FX Model - Script from Entagma.com. (2017). VEX in Houdini: Diffusion Limited Aggregation (Plus Rendering in Mantra & Redshift) | Entagma. [online] Available at: http://www.entagma.com/ vex-in-houdini-diffusion-limited-aggregation-plus-rendering-in-mantra-redshift/#more-879 [Accessed 14 Jul. 2017].

75 Fig 67 75 Fig 68 75 Multi Cultural Diversity - ElJamal, S. (2017). Proliferate - Permeable Plateau. 76 Fig 69 76 Fig 71 78 Fig 70 78 Logic Modified by ElJamal, S. (2017). Proliferate - Permeable Plateau. Metrics developed by: Link-springer-com.libproxy. ucl.ac.uk. (2017). Shibboleth Authentication Request. [online] 78 Fig 72 79 ElJamal, S. (2017). Proliferate - Permeable Plateau.

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