  Inference of gene regulatory networks (GRNs) based on experimental data is a
challenging task in bioinformatics. In this paper, we present a bi-objective
minimization model (BoMM) for inference of GRNs, where one objective is the
fitting error of derivatives, and the other is the number of connections in the
network. To solve the BoMM efficiently, we propose a multi-objective
evolutionary algorithm (MOEA), and utilize the separable parameter estimation
method (SPEM) decoupling the ordinary differential equation (ODE) system. Then,
the Akaike Information Criterion (AIC) is employed to select one inference
result from the obtained Pareto set. Taking the S-system as the investigated
GRN model, our method can properly identify the topologies and parameter values
of benchmark systems. There is no need to preset problem-dependent parameter
values to obtain appropriate results, and thus, our method could be applicable
to inference of various GRNs models.
