Gene regulatory network inference using Michaelis-Menten kinetics

A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.