Non-Dominated Sorting Differential Search Algorithm for Optimizing Regulatory-Metabolic Networks by Using Probabilistic Approach

Modeling in biology has given valuable insights and can be a guide for wet labs. The optimal prediction of a particular metabolite can be obtained by analyzing the genome-scale metabolic model of an organism. The technique that can be implemented is in silico gene or reaction knockout. Many algorithms were proposed to study the effects of genetic perturbations. One of them is a non-dominated sorting differential search algorithm and flux balance analysis (ndsDSAFBA). This algorithm can simultaneously predict growth and production rates based on reaction knockout. It also provides non-dominated solutions that are useful. However, the limitation of that algorithm is the huge combinatorial possibility and the lack of gene regulatory networks. To overcome it, this study proposed a strategy to integrate the regulatory network into the metabolic network for improving the ndsDSAFBA algorithm, called ndsDSAFBA-TFs knockout. The conditional probability approach is used in this study. The proposed improvement has been compared to the regular ndsDSAFBA. The results show that the ndsDSAFBA-TFs knockout outperformed the regular ndsDSAFBA for optimal prediction of the production rate of the succinic acid, namely 16.429 mmol gDW
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hr
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. Moreover, the combinatorial possibility is significantly lower than the regular ndsDSAFBA. Based on the results, the ndsDSAFBA-TFs knockout can be suggested becoming the development of the ndsDSAFBA algorithm.