Parameter identification with optimal experimental design for engineering biology

Engineering biology has the potential to generate new disruptive products and to revolutionise our approach to many societal problems. However, at present, the engineering of these products is complicated by interactions between the host microbe's (chassis') natural biology and the engineered genetic system or metabolic pathway. This results in poor performance which can range from differences in expected behaviour, poor host growth or even complete failure of the engineered function. Recently, mathematical models and computer aided design tools have been produced which enable these interactions to be accounted for during the project design phase. These approaches are termed "host-aware" and enable synthetic biologists and biotechnologists to produce "host-friendly" designs. To date, these approaches have only been developed for the academic lab workhorse E. coli which limits their application in industry who use a variety of microbes due to other beneficial biological properties. Extending these frameworks to other industrially relevant organisms is challenging due to the lack of available data and the lack of experimental protocols which enable efficient data generation. This pilot project establishes an interdisciplinary global research team to solve this problem. First the team will carry out a rigorous analysis of the host-aware design framework which is composed of complex nonlinear ordinary differential equation models and establish new accurate and efficient tools to enable robust parametrisation of complex microbial growth models from sparse data. Working closely with industrial partners, the team will then develop an optimal experimental design approach which enables scientists to determine what experiments need to be conducted to enable these host-aware design frameworks to be developed for industrially relevant microbes. This project will enable the future generation of new industrially relevant "host-aware" computer aided design tools needed for fast and efficient engineering of microbial cells factories.