RUNWAYS OF INNOVATION

Universities are the hubs of transformative innovation, where every year, countless new ideas take flight. The last 50 years have seen an explosion of technologies borne out of the complex interchange between basic university research and commercial partnerships that have gone on to transform the world. Here are two new totemic technologies that will disrupt the future, and how they came to be.

1953

DOUBLE HELIX STRUCTURE OF DNA DISCOVERED

University of Cambridge

James Watson, Francis Crick, Rosalind Franklin, Maurice Wilkins

Synthetic Biology

Mixing biology, engineering, and computer science, synthetic biology is the design and assembly of artificial biological systems or the re-engineering of existing ones. It is accelerating biotechnology in industrial and medical fields and creating new sustainable methods of manufacturing and energy production. Applications include biofuels, novel vaccines and therapies, engineering agriculture to improve yields, and resistance to pests and climate change.

The global synthetic biology market was worth an estimated $14.15 billion in 2021, and growing rapidly: it is expected to reach $45.7 billion by 2026. Australia’s Synthetic Biology Roadmap estimates that, by 2040, synthetic biology could generate $27 billion in annual revenue for Australia and create 44,000 new jobs. Notable Australian companies are Samsara Eco, an Australian start-up that uses enzyme-based technology to break down plastic into its core molecules; and Starpharma, which uses nanoscale polymers for drug targeting. Leading universities are the University of Melbourne, Monash University, the University of Sydney, Macquarie University, the University of Queensland, and UNSW.

1977

FIRST COMPLETE GENOME SEQUENCED University of Cambridge Frederick Sanger

1972

RECOMBINANT DNA TECHNOLOGY DEVELOPED

Stanford University; University of California San Francisco

Stanley Cohen, Herbert Boyer (Boyer co-founds Genentech Inc in 1976; first gene patent issued 1980)

1972

SYNTHESIS OF THE FIRST COMPLETE ARTIFICIAL GENE University of Wisconsin Madison Har Gobind Khorana

2010

CREATION OF THE FIRST SYNTHETIC CELL

J. Craig Venter Institute; U.S. National Institute of Standards and Technology; Massachusetts Institute of Technology

Elizabeth Strychalski, John Glass, Lijie Sun, James Pelletier, Andreas Mershin, Neil Gershenfeld, Kim Wise, Nacyra Assad-Garcia, Bogumil Karas, Thomas Deerinck, Mark Ellisman, Ray-YuanChuang

2022

FIRST FULLY SYNTHETIC YEAST GENOME FOR WINE Macquarie University; University of Adelaide; Australian Wine Research Institute; NSW

Department of Primary Industries

Thomas Williams, Hugh Goold, Ian Paulsen, Isak S. Pretorius, Anthony Borneman, Dariusz Kutyna, Cristobal Onetto, Daniel Johnson

1980

PROPOSES USING SUPERPOSITION AND ENTANGLEMENT IN COMPUTATION

Moscow State University, Russia Yuri Manin

1980

DEVELOPS A THEORETICAL MATHEMATICAL MODEL FOR QUANTUM COMPUTATION

Argonne National Laboratory, USA

Paul Benioff

Quantum Computing

Creating computers that operate on the principles of quantum mechanics — the physics of how matter and energy behave at subatomic level — has long been recognised as potentially powerful, if impossibly complex. Unlike today’s ‘classical’ computers, which process information in binary bits (0s and 1s), quantum computers rely on ‘quantum bits’, or qubits, which can exist in multiple states at once. Known as superposition, this allows a multitude of computation strategies — some exponentially faster, some simultaneous — that are far beyond modern computers. Another key property, entanglement, allows quantum computers to process data in parallel. Although a nascent technology that is delicate and unstable, quantum computers are already used to simulate chemical and molecular interactions to help discover new drugs or novel materials. As they scale up over the next decade, quantum computers will revolutionise cryptography, financial modelling, and chemical engineering, solve complex optimisation problems such as scheduling and routing, and accelerate types of machine learning algorithms.

The global quantum computing market is valued at an estimated $15.3 billion, and growing fast, due to enormous expenditure by governments and commercial companies. The market for quantum computing is predicted to reach $186 billion by 2030. It’s predicted quantum computing will generate $2.2 billion in Australian revenue by 2030 and nearly $6 billion by 2045, creating 8700 new jobs by 2030 and 19,400 by 2045. Notable Australian companies are PsiQuantum, which is developing a quantum computer based on silicon photonics; Silicon Quantum Computing, focusing on single-atom qubits for information processing; and Diraq, which relies on spin qubits and existing technology used by today’s classical computers. Leading universities are UNSW, the University of Sydney, the University of Melbourne, Monash University, and the University of Queensland.

— Wilson da Silva

1982

DETAILS HOW QUANTUM MECHANICS COULD BE USED TO PERFORM CALCULATIONS IMPRACTICAL OR IMPOSSIBLE FOR CLASSICAL COMPUTERS

California Institute of Technology, USA

1994

DEVISES A QUANTUM ALGORITHM CAPABLE OF FACTORING LARGE NUMBERS QUICKLY, BREAKING SECURE CRYPTOGRAPHIC SYSTEMS

Massachusetts Institute of Technology, USA

Richard Feynman

1998

CONCEPT AND DEVELOPMENT OF A SCALABLE QUANTUM COMPUTER IN SILICON

University of New South Wales

Bruce Kane, Michelle Simmons

2001

FIRST USE OF SHOR’S FACTORING ALGORITHM IN A QUANTUM SYSTEM

Stanford University; IBM Almaden Research Centre, USA

Isaac Chuang, Lieven Vandersypen, Matthias Steffen, Gregory Breyta, Costantino Yannoni, Mark Sherwood

2012

FIRST QUANTUM ALGORITHMS RUN ON SILICON QUANTUM COMPUTER

University of New South Wales

Jarryd Pla, Andrew Dzurak, Andrea Morello, Kuan Tan, Juan Dehollain, Wee Lim, John Morton, Floris Zwanenburg, David Jamieson,