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首页> 外文期刊>Progress in drug research. >Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach.
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Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach.

机译：走向全细胞建模和仿真：通过基于约束的方法获得全面的功能基因组学。

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The increasing availability of various system-level, or so-called 'omics', datasets, in concert with existing data from the primary research literature, is facilitating the development of genome-scale metabolic models for many organisms. By incorporating the metabolic reaction stoichiometry as well as other physicochemical properties into systemic network reconstructions, these models account for the constraints that restrict an organism's phenotypic behavior. Accordingly, unlike many contemporary modeling strategies, this constraint-based modeling approach does not attempt to predict network behavior exactly; rather, it seeks to clearly distinguish those network states that a system can achieve from those that it cannot. A variety of analytical tools have been designed and developed to probe these models, thus enabling studies that investigate the metabolic capabilities of a number of organisms, that generate and test experimental hypotheses, and that predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the concepts that underlie the constraint-based modeling approach, and describes several of its applications with an emphasis on those potentially relevant to the drug development field. In addition, while this chapter focuses on the primary application of the constraint-based approach to date, namely in modeling metabolic networks, the latter sections of the chapter discuss its relatively recent application to modeling other cellular systems. Finally, the chapter concludes with an assessment of future directions focusing on the efforts that will be required to utilize the constraint-based approach in generating a holistic model of a viable organism.
机译：与主要研究文献中的现有数据相结合，各种系统级数据集（即所谓的“组学”）数据集的可用性日益提高，正在促进许多生物的基因组规模代谢模型的开发。通过将代谢反应化学计量以及其他理化性质纳入系统网络重构中，这些模型解决了限制生物体表型行为的限制。因此，与许多当代建模策略不同，这种基于约束的建模方法不会尝试准确地预测网络行为。相反，它试图清楚地区分系统可以实现的网络状态和系统无法实现的网络状态。已经设计和开发了多种分析工具来探查这些模型，从而使研究能够调查许多生物的代谢能力，生成和测试实验假设，并准确预测代谢表型和进化结果。本章介绍了基于约束的建模方法背后的概念，并描述了其一些应用，重点介绍了与药物开发领域潜在相关的那些应用。此外，尽管本章重点介绍了迄今为止基于约束的方法的主要应用，即在代谢网络的建模中，但本章的后面各节讨论了它在建模其他细胞系统方面的相对较新的应用。最后，本章以对未来方向的评估作为结尾，重点放在利用基于约束的方法生成可行生物的整体模型所需的努力上。

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