Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.
PLoS Comput Biol. 2012;8(8):e1002662. doi: 10.1371/journal.pcbi.1002662. Epub 2012 Aug 30.
Constraint-based models of metabolism have been used in a variety of studies on drug discovery, metabolic engineering, evolution, and multi-species interactions. These genome-scale models can be generated for any sequenced organism since their main parameters (i.e., reaction stoichiometry) are highly conserved. Their relatively low parameter requirement makes these models easy to develop; however, these models often result in a solution space with multiple possible flux distributions, making it difficult to determine the precise flux state in the cell. Recent research efforts in this modeling field have investigated how additional experimental data, including gene expression, protein expression, metabolite concentrations, and kinetic parameters, can be used to reduce the solution space. This mini-review provides a summary of the data-driven computational approaches that are available for reducing the solution space and thereby improve predictions of intracellular fluxes by constraint-based models.
基于约束的代谢模型已被用于药物发现、代谢工程、进化和多物种相互作用的各种研究中。这些基因组规模的模型可以为任何测序的生物体生成，因为它们的主要参数（即反应计量）高度保守。由于这些模型的参数要求相对较低，因此很容易开发；但是，这些模型通常会导致具有多个可能通量分布的解决方案空间，从而难以确定细胞中的精确通量状态。最近在该建模领域的研究工作探讨了如何使用包括基因表达、蛋白质表达、代谢物浓度和动力学参数在内的其他实验数据来缩小解决方案空间。这篇迷你综述总结了用于缩小解决方案空间的基于数据的计算方法，从而通过基于约束的模型提高对细胞内通量的预测。
