Multi-objective optimisation of metabolic productivity and thermodynamic performance

Novel multi-objective optimisation methodologies, including a two-step sequential optimisation approach and multi-objective optimisation approaches using non-dominated sorting genetic algorithms (NSGAs) and MATLAB based linear programming integrated with genetic algorithms have been developed for the first time to engineer the cellular metabolic productivity and process performance simultaneously. The simultaneous optimisation of cellular metabolic productivity and thermodynamic performance deduces a unique set of enzyme catalysed pathways and flux distributions for a given metabolic product of importance. It has been demonstrated that the energy generating pathways associated to drive a desired productivity are prioritised effectively by multi-objective optimisation approach. A case study on the pentose phosphate pathway (PPP) and glycolysis of in silico Escherichia coli has been used to illustrate the effectiveness of the methodologies.