About Synthetic Biology
In this topic we develop model-based computational designs for new or modified biological systems.
Computational design of whole genome metabolic networks
We modify bacterial GEMS (whole genome metabolic models) in the context of target-driven optimisation, where we wish to improve the behaviour of a single celled organism - E.coli - in terms of the production of target biochemicals under the availability of specific carbon sources and aerobic or anaerobic conditions. Specifically, we use a population based technique (genetic algorithm) and for efficiency purposes Flux Balance Analysis (FBA), coupled with a database of FBA GEMs for a variety of E.coli strains, and a mapping between the genome and the reactome. This enables us to both modify GEM network topology by adding or removing genes [and hence reactions] as well as modifying reaction rates themselves.
Computational design of DNA circuits
We model localised DNA computation, where a DNA strand walks along a binary decision graph to compute a binary function. We automatically identify leakage transitions, which allows for a detailed qualitative and quantitative assessment of circuit designs, design comparison, and design optimisation. The ability to identify leakage transitions is an important step in the process of optimising DNA circuit layouts where the aim is to
minimise the computational error inherent in a circuit while minimising the area of the circuit
Alternative design solutions in synthetic biology
This approach uses a data analytics driven methodology which enables the identification of alternative solutions of computational design templates by analysing the generational history of the population based heuristic search used to generate the templates. While optimisation is focused on obtaining the optimal solution this methodology focuses on alternative solutions which are sub optimal by fitness or solutions with similar fitness but different structures. When the optimal design solution is less robust, an alternative solution will have a sufficiently good accuracy and an achievable resource requirement. The main advantage of the methodology is that it focuses on the solution space explored by a single run which usually gets neglected in the search for an optimal solution. This data analytics driven methodology can be applied more generally to the optimisation of the computational design of complex systems.