An Efficient Data Assimilation Schema for Restoration and Extension of Gene Regulatory Networks Using Time-Course Observation Data

Gene regulatory networks (GRNs) play a central role in sustaining complex biological systems in cells. Although we can construct GRNs by integrating biological interactions that have been recorded in literature, they can include suspicious data and a lack of information. Therefore, there has been an urgent need for an approach by which the validity of constructed networks can be evaluated; simulation-based methods have been applied in which biological observational data are assimilated. However, these methods apply nonlinear models that require high computational power to evaluate even one network consisting of only several genes. Therefore, to explore candidate networks whose simulation models can better predict the data by modifying and extending literature-based GRNs, an efficient and versatile method is urgently required. We applied a combinatorial transcription model, which can represent combinatorial regulatory effects of genes, as a biological simulation model, to reproduce the dynamic behavior of gene expressions within a state space model. Under the model, we applied the unscented Kalman filter to obtain the approximate posterior probability distribution of the hidden state to efficiently estimate parameter values maximizing prediction ability for observational data by the EM-algorithm. Utilizing the method, we propose a novel algorithm to modify GRNs reported in the literature so that their simulation models become consistent with observed data. The effectiveness of our approach was validated through comparison analysis to the previous methods using synthetic networks. Finally, as an application example, a Kyoto Encyclopedia of Genes and Genomes (KEGG)-based yeast cell cycle network was extended with additional candidate genes to better predict the real mRNA expressions data using the proposed method.