Joyce Andrew R, Palsson Bernhard Ø
Bioinformatics Program, University of California-San Diego, La Jolla, CA, USA.
Methods Mol Biol. 2008;416:433-57. doi: 10.1007/978-1-59745-321-9_30.
Genome-scale metabolic models of organisms can be reconstructed using annotated genome sequence information, well-curated databases, and primary research literature. The metabolic reaction stoichiometry and other physicochemical factors are incorporated into the model, thus imposing constraints that represent restrictions on phenotypic behavior. Based on this premise, the theoretical capabilities of the metabolic network can be assessed by using a mathematical technique known as flux balance analysis (FBA). This modeling framework, also known as the constraint-based reconstruction and analysis approach, differs from other modeling strategies because it does not attempt to predict exact network behavior. Instead, this approach uses known constraints to separate the states that a system can achieve from those that it cannot. In recent years, this strategy has been employed to probe the metabolic capabilities of a number of organisms, to generate and test experimental hypotheses, and to predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the constraint-based modeling approach and focuses on its application to computationally predicting gene essentiality.
可以利用注释的基因组序列信息、精心整理的数据库和原始研究文献来重建生物体的基因组规模代谢模型。代谢反应化学计量学和其他物理化学因素被纳入模型，从而施加代表对表型行为限制的约束。基于这一前提，可以通过使用一种称为通量平衡分析（FBA）的数学技术来评估代谢网络的理论能力。这个建模框架，也称为基于约束的重建和分析方法，与其他建模策略不同，因为它不试图预测确切的网络行为。相反，这种方法使用已知的约束来区分系统可以实现的状态和不能实现的状态。近年来，这种策略已被用于探究许多生物体的代谢能力、生成和测试实验假设以及准确预测代谢表型和进化结果。本章介绍基于约束的建模方法，并重点介绍其在计算预测基因必需性方面的应用。
