Computational strategies for a system-level understanding of metabolism.
Computational strategies for a system-level understanding of metabolism.
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DOI：
10.3390/metabo4041034
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发表时间：
2014-11-24
期刊：
影响因子：
4.1
通讯作者：
Vanoni M
中科院分区：
文献类型：
--
作者：
Cazzaniga P;Damiani C;Besozzi D;Colombo R;Nobile MS;Gaglio D;Pescini D;Molinari S;Mauri G;Alberghina L;Vanoni M
细胞新陈代谢是提供能量和维持生命的基础的生化机制。了解其精细调节对于从代谢工程应用到代谢紊乱和癌症的治疗等多个领域都至关重要。需要复杂的计算方法来阐明新陈代谢的复杂性。为此，人们开发了多种方法，但通常很难确定哪种计算策略最适合代谢特定方面的研究。这篇综述提供了可用于分析代谢途径的计算方法的最新描述，讨论了它们的主要优点和缺点。 特别是，注意力集中在代谢网络重建中适当规模和准确水平的识别，以及模型结构和参数的推断，特别是在处理实验测量不足时。然后讨论选择正确的计算方法来导出计算机数据，包括拓扑分析、基于约束的建模和系统动力学模拟。最后提供了一些获得新生物学知识或提出假设的计算方法的描述。
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.