Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible and strong, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

中文翻译：

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基于微分方程模型的基因调控网络推论概述。

基因调控网络（GRN）的重建在理解生物系统的复杂性，功能和途径方面起着重要作用，这可以支持疾病新药的设计。由于微分方程模型灵活而强大，因此这些模型已被用于识别生化反应和基因调控网络。本文研究了逆向工程基因调控网络的微分方程模型。介绍了三种微分方程模型，包括常微分方程（ODE），时滞微分方程（TDDE）和随机微分方程（SDE）。ODE模型包括线性ODE，非线性ODE和S系统模型。我们还讨论了进化算法，用于搜索微分方程模型的最佳结构和参数。这项研究可以提供对微分方程模型的全面理解，并导致发现新型微分方程模型。