Optimization of the bioconversion of glycerol to ethanol using <i>Escherichia coli</i> by implementing a bi-level programming framework for proposing gene transcription control strategies based on genetic algorithms

Abstract

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 based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. 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. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.

Transcription of the Alginate Operon in Pseudomonas aeruginosa Is Regulated by c-di-GMP

“…If c-di-GMP binds to the tested protein, instead of diffusing, c-di-GMP will be retained at the protein, resulting in a black dot in the center of the spot ( 34 ). Whole-cell lysates containing the E. coli IlvH protein overexpressed from the plasmid pCA24N-ilvH were used as the positive control, since the IlvH protein is known to bind c-di-GMP ( 35 ), and a whole-cell lysate with an empty vector, pCA24N ( 36 ), was used as the negative control. Unlike the positive control, the tested proteins showed no significant black dot in the spot center but showed instead a uniform spot that resulted from freely diffusing c-di-GMP ( Fig.…”

“…The DRaCALA c-di-GMP binding assay of purified AlgB, AmrZ, and AlgR protein and the preparation of the whole-cell lysates were performed as previously described by Schicketanz et al ( 34 ). The whole-cell lysates containing the E. coli IlvH protein overexpressed from the plasmid pCA24N-ilvH were used as the positive control, since the IlvH protein is known to bind c-di-GMP ( 35 ), and a whole-cell lysate with an empty vector pCA24N ( 36 ) was used as the negative control. The radioactive c-di-GMP was synthesized from 32 P-α-GTP (Perkin Elmer) via the purified His-YdeH protein ( 53 ).…”

From industrial by‐products to value‐added compounds: the design of efficient microbial cell factories by coupling systems metabolic engineering and bioprocesses

“…Next, a series of mutations on this population and a generation of new populations are carried out so its performance is evaluated to finally obtain individuals with the phenotype desired. 34 Another approach based on elementary modes is CASOP (Computational Approach for Strain Optimization), which was developed to suggest gene overexpression and deletion. 35 This approach is based on minimal functional units (pathways) of metabolic networks, which allow a balanced operation of the network under steady-state conditions.…”

“…Then, OptGene uses genetic algorithms to explore the genotypic space by first randomly generating an initial population. Next, a series of mutations on this population and a generation of new populations are carried out so its performance is evaluated to finally obtain individuals with the phenotype desired 34 …”