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Synthetic Systems Biology

No gene-encoded function exists outside of its system context, and the selection and fitness are processes and outcomes that occur at the level of the system rather than the gene. The ‘system’ in this context may be as focussed as a pathway, local metabolic network, or cell; or it could be a population, a complex culture vessel, or some wider system. Also, while single functions might be abstracted, addressed, and constructed in laboratory research settings, ultimately synthetic biology solutions have to address multiple system properties that might be synergistic, competing, linked, or frequently unlinked.

To take one example relevant to our work, in the design of a bacterial system to convert sugars from hemicellulose to ethanol, one would have to consider multiple design requirements including:

  • The ability to use C5 sugars, predominantly xylose
  • The ability to efficiently metabolise C5 in the presence of low concentrations of C6 / glucose
  • The ability to efficiently produce ethanol
  • The ability to tolerate ethanol at concentrations that were desirable pre-extraction
  • The ability to tolerate inhibitors that are present in industrial hydrolysates of hemicellulose, specifically furfural
  • The safety of the bacterium being used
  • The ‘engineerability’ of the species and strains being engineered

A solution-focussed engineering approach has address this as a whole, rather than taking a traditional experimental approach focussed upon each behaviour separately. Nobody would design a car without consideration to issues of the different but interdependent aspects of steering, suspension, acceleration, and braking – we believe that the same philosophy is necessary to design and engineer living systems. There is no point generating an efficient ethanol producing strain that is inhibited by low concentrations of either ethanol or furfural. It is inefficient to work with strains that are very difficult to engineer, or one in which its ability to produce the product will be compromised if removal of factors that potentially make it dangerous are removed. The whole problem must be defined and addressed as such.

A particular strength of the comparative behavioural genomics approach is that enables us to determine the behavioural determinants for multiple system design requirements and to incorporate this into all stages of strain engineering and development.