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

Kauthar Mohd Daud¹, Mohd Saberi Mohamad², Zalmiyah Zakaria¹

¹Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.

Computers in Biology and Medicine

|August 27, 2019

Summary

This study introduces a new metabolic engineering method, ndsDSAFBA, for optimizing cellular production. It identifies reaction knockouts to maximize both growth and production rates, outperforming traditional single-objective approaches.

Keywords:

Artificial intelligence Bioinformatics Flux balance analysis Metabolic engineering Multi-objective evolutionary algorithms Pareto dominance Reaction knockout

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Area of Science:

Metabolic Engineering
Systems Biology
Computational Biology

Background:

Metabolic engineering aims to enhance cellular functions by altering metabolic networks.
In silico reaction knockout simulations are used to predict genetic perturbation effects on metabolite production.
Existing methods often prioritize growth coupling, but the ultimate goal is increased production, and they typically yield single solutions, neglecting cellular multi-objective behavior.

Purpose of the Study:

To develop a novel multi-objective optimization method for metabolic engineering.
To identify reaction knockouts that simultaneously maximize metabolite production and growth rates.
To address the limitations of single-objective optimization in metabolic engineering.

Main Methods:

Developed a new method termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis).
Incorporated Pareto dominance concepts to handle competing objectives (production and growth rates).
Validated the method using three genome-scale metabolic models.

Main Results:

Obtained a set of non-dominated solutions, each representing a distinct mutant strain with trade-offs between production and growth.
Demonstrated that ndsDSAFBA outperforms single-objective optimization (SOO) and other multi-objective optimization (MOO) methods.
Achieved superior results in terms of both production rate and growth rate.

Conclusions:

The ndsDSAFBA method effectively identifies reaction knockouts for enhanced cellular production and growth.
Multi-objective optimization using Pareto dominance is crucial for realistic metabolic engineering strategies.
This approach provides a more comprehensive set of solutions compared to traditional methods.