A non-linear reverse-engineering method for inferring genetic regulatory networks

Siyuan Wu; Tiangang Cui; Xinan Zhang; Tianhai Tian

doi:10.7717/peerj.9065

A non-linear reverse-engineering method for inferring genetic regulatory networks

PeerJ. 2020 Apr 29:8:e9065. doi: 10.7717/peerj.9065. eCollection 2020.

Authors

Siyuan Wu¹, Tiangang Cui¹, Xinan Zhang², Tianhai Tian¹

Affiliations

¹ School of Mathematics, Monash University, Clayton, VIC, Australia.
² School of Mathematics and Statistics, Central China Normal University, Wuhan, PR China.

Abstract

Hematopoiesis is a highly complex developmental process that produces various types of blood cells. This process is regulated by different genetic networks that control the proliferation, differentiation, and maturation of hematopoietic stem cells (HSCs). Although substantial progress has been made for understanding hematopoiesis, the detailed regulatory mechanisms for the fate determination of HSCs are still unraveled. In this study, we propose a novel approach to infer the detailed regulatory mechanisms. This work is designed to develop a mathematical framework that is able to realize nonlinear gene expression dynamics accurately. In particular, we intended to investigate the effect of possible protein heterodimers and/or synergistic effect in genetic regulation. This approach includes the Extended Forward Search Algorithm to infer network structure (top-down approach) and a non-linear mathematical model to infer dynamical property (bottom-up approach). Based on the published experimental data, we study two regulatory networks of 11 genes for regulating the erythrocyte differentiation pathway and the neutrophil differentiation pathway. The proposed algorithm is first applied to predict the network topologies among 11 genes and 55 non-linear terms which may be for heterodimers and/or synergistic effect. Then, the unknown model parameters are estimated by fitting simulations to the expression data of two different differentiation pathways. In addition, the edge deletion test is conducted to remove possible insignificant regulations from the inferred networks. Furthermore, the robustness property of the mathematical model is employed as an additional criterion to choose better network reconstruction results. Our simulation results successfully realized experimental data for two different differentiation pathways, which suggests that the proposed approach is an effective method to infer the topological structure and dynamic property of genetic regulations.

Keywords: Differential equation; Genetic regulatory network; Hematopoiesis; Network inference; Probabilistic graphic model.

Grants and funding

This work was supported by the National Natural Science Foundation of China (Nos.11871238 and 11931019). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.