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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Survey on Methods for Modeling and Analyzing Integrated Biological Networks
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A Survey on Methods for Modeling and Analyzing Integrated Biological Networks

机译：综合生物网络建模与分析方法的综述

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摘要

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omicsȁD; technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.
机译：了解蜂窝系统如何建立对其动态变化的环境的综合响应是系统生物学中的未解决问题之一。尽管它们相互交织，但信号网络，基因调控和新陈代谢经常在定义明确的子系统中独立建模。为了这个目的，根据所研究的每个特定网络的特征，已经开发了几种数学形式主义。尽管如此，对细胞行为的更深入了解要求将这些各种系统集成到一个模型中，该模型能够捕获它们如何作为整体工作。随着“组学；技术”的最新进展，越来越多的数据变得可用，因此，最近的努力已经朝着这种集成建模方法发展。我们在这里回顾和讨论当前可用于建模和分析集成生物网络的方法框架，特别是代谢，基因调控和信号网络，包括基于网络的方法和化学组织理论，通量平衡分析及其扩展，逻辑离散模型，Petri网，传统动力学模型，混合系统和随机模型，还对数据进行了比较需求，可扩展性以及网络规模和计算负担，并在大规模基因组模型，尤其是各种生物的子系统中，以成功的案例研究说明了这些方法。

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