GrowMatch: an automated method for reconciling in silico/in vivo growth predictions.
GrowMatch: an automated method for reconciling in silico/in vivo growth predictions.
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DOI：
10.1371/journal.pcbi.1000308
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发表时间：
2009-03
影响因子：
4.3
通讯作者：
Maranas CD
中科院分区：
文献类型：
--
作者：
Kumar VS;Maranas CD
基因组尺度的代谢重建通常通过比较利用不同碳源的不同突变体的硅生长预测和体内生长数据来验证。这种比较导致两种类型的模型预测不一致；要么模型在实验中未观察到生长时预测生长（与NGG不一致），要么模型在实验显示生长时预测不生长（与NGG不一致）。在这里，我们提出了一个基于优化的框架GrowMatch，以自动协调GNG预测（通过抑制模型中的功能）和NGG预测（通过向模型中添加功能）。我们使用GrowMatch解决了最新的硅芯片大肠杆菌（iAF1260）模型预测与Keio收集的体内数据之间的不一致性，并将硅芯片与体内预测的一致性从90.6%提高到96.7%。具体来说，我们能够对56/72个GNG突变体和13/38个NGG突变体提出一致性恢复假设。GrowMatch通过提出抑制突变体代谢网络解决了18个GNG不一致的问题。通过抑制代谢网络中的同工酶解决了15个不一致，通过适当修改iAF1260的生物量方程解决了其余23个与被阻断基因对应的GNG突变体。GrowMatch通过向模型中添加功能，提出了5个NGG突变体的一致性恢复假设，而其余8个不一致性则通过精确定位执行被删除基因功能的可能替代基因来解决。在许多情况下，GrowMatch识别了相当不直观的模型修改假设，这些假设很难单独通过检查来确定。此外，GrowMatch可以在新的基因组尺度代谢模型构建阶段使用，而不是现有的基因组尺度代谢模型，从而更方便和准确地重建。在过去的十年中，已经建立了细胞代谢的数学模型来描述现有的代谢过程。测试这些模型的准确性和完整性的黄金标准是将不同情景下的细胞生长预测（即细胞寿命/死亡）与现有的实验数据进行比较。尽管这些比较已被用于建议模型修改，但识别这些修改的关键步骤通常是手动执行的。在这里，我们描述一个自动化过程GrowMatch来解决这个问题。当模型通过与实验数据对比预测生长而过度预测生物体的代谢能力时，我们使用GrowMatch通过抑制生长来恢复模型中生物转化的一致性。或者，当模型通过预测没有生长（即细胞死亡）而低于现有数据来预测生物体的代谢能力时，我们使用GrowMatch通过向模型中添加促进生长的生物转化来恢复一致性。我们通过调和最新大肠杆菌模型与庆应义塾数据库中可用数据的生长预测不一致性来演示GrowMatch的使用。尽管大肠杆菌模型具有高度精心策划的性质，GrowMatch通过利用现有的实验数据汇编，确定并解决了大量模型预测的不一致。
Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%. Specifically, we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants. GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks. Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network, and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260. GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene. For many cases, GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, GrowMatch can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions. Over the past decade, mathematical models of cellular metabolism have been constructed for describing existing metabolic processes. The gold standard for testing the accuracy and completeness of these models is to compare their cellular growth predictions (i.e., cell life/death) across different scenarios with available experimental data. Although these comparisons have been used to suggest model modifications, the key step of identifying these modifications has often been performed manually. Here, we describe an automated procedure GrowMatch that addresses this challenge. When the model overpredicts the metabolic capabilities of the organism by predicting growth in contrast with experimental data, we use GrowMatch to restore consistency by suppressing growth enabling biotransformations in the model. Alternatively, when the model underpredicts the metabolic capabilities of the organism by predicting no growth (i.e., cell death) in contrast with available data, we use GrowMatch to restore consistency by adding growth-enabling biotransformations to the model. We demonstrate the use of GrowMatch by reconciling growth prediction inconsistencies of the latest Escherichia coli model with data available at the Keio database. Despite the highly curated nature of the Escherichia coli model, GrowMatch identified and resolved a large number of model prediction inconsistencies by taking advantage of available compilations of experimental data.
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DOI：
10.1016/s0928-4869(00)00006-9
发表时间：
2000-04-15
期刊：
SIMULATION PRACTICE AND THEORY
影响因子：
--
作者：
Arita, M
通讯作者：
Arita, M
影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
影响因子：
12.3
作者：
Chen L;Vitkup D
通讯作者：
Vitkup D
大肠杆菌K-12的构造框架，单基因敲除突变体：Keio Collection。
影响因子：
9.9
作者：
通讯作者：
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影响因子：
9.9
作者：
通讯作者：
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