Chindelevitch Leonid, Trigg Jason, Regev Aviv, Berger Bonnie
1] Mathematics Department, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, USA [2] Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.
Mathematics Department, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, USA.
Nat Commun. 2014 Oct 7;5:4893. doi: 10.1038/ncomms5893.
Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations.
基于约束的模型是目前唯一能够在全基因组规模上研究新陈代谢的方法。通量平衡分析通常用于分析基于约束的模型。奇怪的是,这种分析的结果会因所运行的软件而异,我们发现这种情况可以通过使用精确算术而非浮点算术来补救。在此,我们介绍MONGOOSE,这是一个用于以精确算术分析基于约束的代谢模型结构的工具箱。我们将MONGOOSE应用于对98个现有代谢网络模型的分析,发现近一半的模型中生物量反应令人惊讶地受阻(无法维持非零通量)。我们提出了一种有原则的方法来解除这些反应的阻塞,并将其扩展到识别必需反应和合成致死反应以及最小培养基的问题。我们的结构见解能够对基于约束的代谢模型进行系统研究,从而更深入地理解它们的可能性和局限性。