Department of Biological Sciences, Virginia Tech, 5088 Derring Hall, Blacksburg VA 24061, USA; Division of Systems Biology, Academy of Integrated Science, Virginia Tech, Blacksburg VA 24061, USA.
Bioinformatics and Systems Biology Program, King Mongkut's University of Technology Thonburi, Bang Khun Thian, Bangkok 10150, Thailand.
J Theor Biol. 2019 Feb 7;462:514-527. doi: 10.1016/j.jtbi.2018.11.034. Epub 2018 Nov 28.
Strategies for modeling the complex dynamical behavior of gene/protein regulatory networks have evolved over the last 50 years as both the knowledge of these molecular control systems and the power of computing resources have increased. Here, we review a number of common modeling approaches, including Boolean (logical) models, systems of piecewise-linear or fully non-linear ordinary differential equations, and stochastic models (including hybrid deterministic/stochastic approaches). We discuss the pro's and con's of each approach, to help novice modelers choose a modeling strategy suitable to their problem, based on the type and bounty of available experimental information. We illustrate different modeling strategies in terms of some abstract network motifs, and in the specific context of cell cycle regulation.
作为分子控制系统知识和计算资源能力的提升，过去 50 年来，对基因/蛋白质调控网络的复杂动态行为建模的策略也得到了发展。在这里，我们回顾了一些常见的建模方法，包括布尔（逻辑）模型、分段线性或全非线性常微分方程组系统，以及随机模型（包括混合确定性/随机方法）。我们讨论了每种方法的优缺点，以帮助新手建模者根据可用实验信息的类型和丰富程度，选择适合其问题的建模策略。我们根据一些抽象的网络模式以及细胞周期调控的具体情况来说明不同的建模策略。
