The discipline of Synthetic Biology, views cells as machines that can be built, from parts, in a manner similar to electronic circuits or airplanes and has thus sought to co-opt biological cells to perform tasks of a predefined function and utility, e.g. nano-computation or nano-manufacturing. This research area aims at making biological cells much easier to program and hence harnessed for useful purposes. In order to achieve this, we use the theories, tools, methodologies and resources that computer science created for writing computer programs and find ways of making them practical and useful in the biological laboratory.
Three main sub-areas underpining our research in synthetic biology are: in vivo computation, computational biodesign and biodesign automation. Our group leads the Synthetic Biology Network for modelling and programming cell-chell interactions.
In vivo computation
A living cell, e.g. a bacterium, can be conceptualised as a machine that processes matter, energy and, crucially, information. It is composed of a series of sub-systems that work in concert by sensing external stimuli, assessing its own internal states and making decisions through a network of complex and interlinked biological regulatory networks (BRN) that act as the cell’s brain. Furthermore, it has been shown that cells not only react to their environment but that they can even predict environmental changes. By pursuing advances in the in vivo computation, i.e. programmable information processing by biological cells, we are creating a brand new, unconventional, computing platform that allows us to push the boundaries of computer science and software engineering. Thus we are fundamentally probing the very nature of what "computation" means.
We research and develop computational tools and approaches to aid the biologist in designing synthetic bioconstructs, especially for bacterial systems. For example we investigate into new Computer Aided Design (CAD) tools and languages for specifying biological systems, how to automatically bring together data from a wide variety of sources as to inform the design process, etc. In particular, we are building state of the art simulators and modelling engines as well as model repositories and data warehouses that can be mined and integrated as to accelerate the process of biological design of complex phenotypes.
We are also carrying out research on biodesign automation for synthetic biology. One promising approach to the design of novel, non-intuitive genetic circuits is the use of machine intelligence (MI). MI techniques were developed for use in complex areas, where the desired outcome is known, but how to achieve this outcome is less obvious. Many MI approaches are inspired by biology and include:
- evolutionary algorithms, based upon biological evolution
- artificial neural networks, based loosely upon the way in which brains learn
- swarm intelligence, based upon the way in which social insects interact to solve problems
- fuzzy systems
Our machine intelligence strategy for design automation is augmented by in-house new technologies for DNA sequencing, synthesis, 3D printing, robot liquid handling and microfluidics devices.