Connectionist Modelling of Dynamics of Gene Expression and Reverse Engineering Gene Regulatory Networks

In this article we develop two connectionist models describing the dynamics of gene expression incorporating protein concentration. The models are based on the theoretical study of Goutsias and Kim [37]. We calculate the concentration of mRNAs and proteins at different time steps, and the concentrations of mRNAs and proteins are calculated as a function of step n. Here we consider concentration of mRNA in a cell at step as depending on the concentration of mRNA and proteins at step (n — 1) in that particular cell. Similarly the protein concentration in a cell at step n depends on the concentration of protein and mRNA at step (n — 1) in that particular cell. Here we develop two neural network models, and estimate the parameters using neural network model through learning. Finally, gene regulatory networks are determined as network parameters. The performance of the models have effectively been tested on a real life fruit fly time series gene expression data containing various stages of development of fruit fly.