Modelling gene regulatory network by fractional order differential equations

Choosing a suitable mathematical model is essential for modelling genetic regulatory mechanism. This paper proposes a new approach based on fractional order differential equations to build the gene regulatory network from time series data. Particle swarm optimization technique is employed for identifying the model´s order and parameters. The experiments on real life gene expression data show that the fractional order differential model leads better data fit, and the proposed scheme is effective to infer gene regulatory model.