Deterministic and stochastic models of genetic regulatory networks.
Deterministic and stochastic models of genetic regulatory networks.
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DOI：
10.1016/s0076-6879(09)67013-0
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发表时间：
2009
影响因子：
--
通讯作者：
Aitchison, John D.
中科院分区：
文献类型：
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作者：
Shmulevich, Ilya;Aitchison, John D.
传统的分子生物学研究倾向于将生物学途径简化为作为细胞系统的孤立部分进行研究的复合单元。随着可以捕获数千个数据点的高通量方法和强大的计算方法的出现，在系统层面研究细胞过程的现实已经摆在我们面前。由于这些方法产生了大量的数据集，系统级分析借鉴了其他领域，如工程和数学，采用计算和统计方法来破译分子之间的关系。在高质量数据集和分析的指导下，人们可以开始预测建模的过程。这些方法的结果往往令人惊讶，超出了正常的直觉。我们讨论了四类动力系统用于建模基因调控网络。讨论分为连续和离散模型，以及确定性和随机模型类。对于这些类别的每种组合，都会在酵母细胞周期的背景下介绍和讨论一个模型，说明如何通过不同的模型类解决不同类型的问题。
Traditionally molecular biology research has tended to reduce biological pathways to composite units studied as isolated parts of the cellular system. With the advent of high throughput methodologies that can capture thousands of data points, and powerful computational approaches, the reality of studying cellular processes at a systems level is upon us. As these approaches yield massive datasets, systems level analyses have drawn upon other fields such as engineering and mathematics, adapting computational and statistical approaches to decipher relationships between molecules. Guided by high quality datasets and analyses, one can begin the process of predictive modeling. The findings from such approaches are often surprising and beyond normal intuition. We discuss four classes of dynamical systems used to model genetic regulatory networks. The discussion is divided into continuous and discrete models, as well as deterministic and stochastic model classes. For each combination of these categories, a model is presented and discussed in the context of the yeast cell cycle, illustrating how different types of questions can be addressed by different model classes.