Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA.
Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA.
Biochim Biophys Acta Gene Regul Mech. 2017 Jan;1860(1):64-74. doi: 10.1016/j.bbagrm.2016.07.017. Epub 2016 Jul 30.
Uncovering and mathematically modeling Transcription Factor Networks (TFNs) are the first steps in engineering plants with traits that are better equipped to respond to changing environments. Although several plant TFNs are well known, the framework for systematically modeling complex characteristics such as switch-like behavior, oscillations, and homeostasis that emerge from them remain elusive. This review highlights literature that provides, in part, experimental and computational techniques for characterizing TFNs. This review also outlines methodologies that have been used to mathematically model the dynamic characteristics of TFNs. We present several examples of TFNs in plants that are involved in developmental and stress response. In several cases, advanced algorithms capture or quantify emergent properties that serve as the basis for robustness and adaptability in plant responses. Increasing the use of mathematical approaches will shed new light on these regulatory properties that control plant growth and development, leading to mathematical models that predict plant behavior. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.
揭示和数学建模转录因子网络（TFNs）是工程植物具有更好应对环境变化的特征的第一步。尽管已经有几个植物 TFN 被广泛研究，但系统建模复杂特征（如开关行为、振荡和动态平衡）的框架仍然难以捉摸。本综述强调了部分提供用于表征 TFN 的实验和计算技术的文献。本综述还概述了用于数学建模 TFN 动态特性的方法。我们提出了几个涉及发育和应激反应的植物 TFN 的例子。在几种情况下，高级算法捕捉或量化了作为植物反应稳健性和适应性基础的新兴特性。增加对数学方法的使用将为控制植物生长和发育的这些调节特性提供新的认识，从而导致可以预测植物行为的数学模型。本文是由 Erich Grotewold 博士和 Nathan Springer 博士编辑的特刊题为“植物基因调控机制和网络”的一部分。
