Implementation of a Bi-Level Programming Framework for Proposing Gene Transcription Control Strategies for Metabolite Synthesis Optimization Based On Genetic Algorithms

In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are biological reactions based. Here, we propose an evolutionary approach aiming to propose different mutant for continuously regulation of gene expression. We found that this algorithm, even though is far from providing the global optimum is able to uncover genes that a global optimizer would be incapable of. These results were experimentally tested by over-expressing adhE and gldA which transcribe for acetaldehyde-CoA dehydrogenase and glyceraldehyde-3-phosphate dehydrogenase A respectively. gldA over-expression allows a two-fold increase in the ethanol concentration. Then this approach demonstrate that rigorous global optimizers although providing the theoretical optimum need to be complemented with solutions capable of supply with more than a few options due to the gap between experimental and theoretical results.