Design automation in synthetic biology : a dual evolutionary strategy

Abstract

Synthetic biology o ers a new horizon in designing complex systems. However, unprecedented complexity hinders the development of biological systems to its full potential. Mitigating complexity via adopting design principles from engineering and computer science elds has resulted in some success. For example, modularisation to foster reuse of design elements, and using computer assisted design tools have helped contain complexity to an extent. Nevertheless, these design practices are still limited, due to their heavy dependence on rational decision making by human designers. The issue with rational design approaches here arises from the challenging nature of dealing with highly complex biological systems of which we currently do not have complete understanding. Systematic processes that can algorithmically nd design solutions would be better able to cope with uncertainties posed by high levels of design complexity. A new framework for enabling design automation in synthetic biology was investigated. The framework works by projecting design problems into search problems, and by searching for design solutions based on the dual-evolutionary approach to combine the respective power of design domains in vivo and in silico. Proof-of-concept ideas, software, and hardware were developed to exemplify key technologies necessary in realising the dual evolutionary approach. Some of the areas investigated as part of this research included single-cell-level micro uidics, programmatic data collection, processing and analysis, molecular devices supporting solution search in vivo, and mathematical modelling. These somewhat eclectic collection of research themes were shown to work together to provide necessary means with which to design and characterise biological systems in a systematic fashion.