Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks

Abstract

As a result of recent advances in biotechnology, many findings related to intracellular systems have been published, e.g., transcription factor (TF) information. Although we can reproduce biological systems by incorporating such findings and describing their dynamics as mathematical equations, simulation results can be inconsistent with data from biological observations if there are inaccurate or unknown parts in the constructed system. For the completion of such systems, relationships among genes have been inferred through several computational approaches, which typically apply several abstractions, e.g., linearization, to handle the heavy computational cost in evaluating biological systems. However, since these approximations can generate false regulations, computational methods that can infer regulatory relationships based on less abstract models incorporating existing knowledge have been strongly required. Results: We propose a new data assimilation algorithm that utilizes a simple nonlinear regulatory model and a state space representation to infer gene regulatory networks (GRNs) using time-course observation data. For the estimation of the hidden state variables and the parameter values, we developed a novel method termed a higher moment ensemble particle filter (HMEnPF) that can retain first four moments of the conditional distributions through filtering steps. Starting from the original model, e.g., derived from the literature, the proposed algorithm can sequentially evaluate candidate models, which are generated by partially changing the current best model, to find the model that can best predict the data. For the performance evaluation, we generated six synthetic data based on two real biological networks and evaluated effectiveness of the proposed algorithm by improving the networks inferred by previous methods. We then applied time-course observation data of rat skeletal muscle stimulated with corticosteroid. Since a corticosteroid pharmacogenomic pathway, its kinetic/dynamics and TF candidate genes have been partially elucidated, we incorporated these findings and inferred an extended pathway of rat pharmacogenomics. Conclusions: Through the simulation study, the proposed algorithm outperformed previous methods and successfully improved the regulatory structure inferred by the previous methods. Furthermore, the proposed algorithm could extend a corticosteroid related pathway, which has been partially elucidated, with incorporating several information sources.