Single-Objective Constrained Optimization for Gene Regulatory Networks Modeling

In the course of apprehending of biological system, the modeling process of gene interactions plays a crucial rocketing role. Unfortunately, the main bottleneck of the modeling process is always the determination of the model parameters. This study focuses on the problem of identifying Gene Regulatory Network (GRN) variables in a discrete framework, represented by gene product concentration thresholds, that separate discrete states of genes contained in the GRN. We propose to compute thresholds from graphs representing interactions between biological genes, gene product concentrations experimentally measured by biologists, and observable behaviors. In this setting, we have developed some adaptations of bio-inspired methods to assist modelers and to propose compatible models to biologists. Since the parameter identification problem is constrained, in this article we focus on adapting and comparing three different heuristics of the CEC’2020 competition for solving single-objective constrained problems using the aforementioned bio-inspired methods by defining a dedicated fitness. To validate our approach, we used an abstract model of the cell cycle and found the performances of the three heuristics consistent with the results of the CEC’2020 competition. This serves as a proof of concept for the development of methods to identify parameters of GRN models using available data and relying less on biological expertise.