Wu Siyuan, Cui Tiangang, Zhang Xinan, Tian Tianhai
School of Mathematics, Monash University, Clayton, VIC, Australia.
School of Mathematics and Statistics, Central China Normal University, Wuhan, PR China.
PeerJ. 2020 Apr 29;8:e9065. doi: 10.7717/peerj.9065. eCollection 2020.
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.
造血作用是一个高度复杂的发育过程，可产生各种类型的血细胞。这个过程由不同的基因网络调控，这些基因网络控制造血干细胞（HSC）的增殖、分化和成熟。尽管在理解造血作用方面已经取得了重大进展，但HSC命运决定的详细调控机制仍未完全阐明。在本研究中，我们提出了一种新方法来推断详细的调控机制。这项工作旨在开发一个能够准确实现非线性基因表达动态的数学框架。特别是，我们打算研究遗传调控中可能的蛋白质异二聚体和/或协同效应的影响。这种方法包括用于推断网络结构的扩展前向搜索算法（自上而下方法）和用于推断动态特性的非线性数学模型（自下而上方法）。基于已发表的实验数据，我们研究了两个由11个基因组成的调控网络，分别用于调控红细胞分化途径和中性粒细胞分化途径。所提出的算法首先应用于预测11个基因和55个可能用于异二聚体和/或协同效应的非线性项之间的网络拓扑结构。然后，通过将模拟结果与两种不同分化途径的表达数据进行拟合来估计未知的模型参数。此外，进行边缘删除测试以从推断的网络中去除可能不重要的调控。此外，数学模型的稳健性被用作选择更好的网络重建结果的附加标准。我们的模拟结果成功实现了两种不同分化途径的实验数据，这表明所提出的方法是推断遗传调控的拓扑结构和动态特性的有效方法。
