Improved differential search algorithms for metabolic network optimization

The capabilities of Escherichia coli and Zymomonas mobilis to efficiently converting substrate into valuable metabolites have caught the attention of many industries. However, the production rates of these metabolites are still below the maximum threshold. Over the years, the organism strain design was improvised through the development of metabolic network that eases the process of exploiting and manipulating organism to maximize its growth rate and to maximize metabolites production. Due to the complexity of metabolic networks and multiple objectives, it is difficult to identify near-optimal knockout reactions that can maximize both objectives. This research has developed two improved modelling-optimization methods. The first method introduces a Differential Search Algorithm and Flux Balance Analysis (DSAFBA) to identify knockout reactions that maximize the production rate of desired metabolites. The latter method develops a non-dominated searching DSAFBA (ndsDSAFBA) to investigate the trade-off relationship between production rate and its growth rate by identifying knockout reactions that maximize both objectives. These methods were assessed against three metabolic networks – E.coli core model, iAF1260 and iEM439 for production of succinic acid, acetic acid and ethanol. The results revealed that the improved methods are superior to the other state-of-the-art methods in terms of production rate, growth rate and computation time. The study has demonstrated that the two improved modelling-optimization methods could be used to identify near-optimal knockout reactions that maximize production of desired metabolites as well as the organism’s growth rate within a shorter computation time

A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate

A non-dominated sorting differential search algorithm flux balance analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate