A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks.
A scalable algorithm to explore the Gibbs energy landscape of genome-scale metabolic networks.
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DOI：
10.1371/journal.pcbi.1002562
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发表时间：
2012
影响因子：
4.3
通讯作者：
Marinari E
中科院分区：
文献类型：
--
作者：
De Martino D;Figliuzzi M;De Martino A;Marinari E
将各种类型的基因组数据整合到生物网络的预测模型中是计算生物学目前面临的主要挑战之一。基于约束的模型，特别是发挥关键作用，试图在基因组规模获得定量的细胞代谢的理解。从本质上讲，他们的目标是基于最小假设来构建生物体的代谢能力，这些假设通过合适的化学计量约束来描述底层反应网络的稳态，特别是质量平衡和能量平衡（即热力学可行性）。然而，这些要求的实现以产生反应通量的可行配置和/或测试给定通量分布的热力学可行性可以证明是计算密集型的。我们在这里提出了一个快速和可扩展的基于化学计量的方法来探索吉布斯能量景观的生化网络在稳态。该方法适用于重建吉布斯能量景观的问题，在人类红细胞的代谢活动，并确定和删除在大肠杆菌代谢网络（iAF 1260）中的不可行的反应循环。在前一种情况下，我们产生一致的细胞内代谢物的化学势（或对数浓度）的预测;在后者中，我们确定了一组有限的循环（共23个）在周质和细胞质核心的起源热力学不可行性在一个大样本（）的通量配置随机产生的兼容与反应可逆性的先验信息。生物系统的运作在任何情况下都受到物理定律的约束。特别是热力学，决定了生化反应在稳态下的优先方向。当应用于细胞反应系统（如代谢网络）时，它有利于出现一些（物理上可行的）组织物质流动的方法，同时禁止其他方法。细胞生化活性的详细预测模型的开发依赖于在细胞代谢网络的基因组规模重建中整合热力学定律的可能性。在这项工作中，我们已经设计了一个有效的松弛算法来实现基因组规模的模型中的热力学约束。除了允许检查反应流配置的热力学可行性之外，它还能够提供关于其他相关物理化学量的信息。我们已经将其应用于两个不同复杂性的细胞代谢网络，即人类红细胞和大肠杆菌。在前一种情况下，我们已经获得了细胞内的化学状态（代谢物浓度和反应自由能）与经验知识相一致的预测;在后者中，我们有效地纠正了化学不可行的通量配置。
The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample () of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility. The operation of biological systems is constrained under all circumstances by the laws of physics. Thermodynamics, in particular, dictates preferential directions in which biochemical reactions should flow at stationarity. When applied to cellular reaction systems (like metabolic networks), it favors the emergence of some (thermodynamically feasible) ways to organize the flow of matter while prohibiting others. The development of detailed predictive models for the biochemical activity of a cell relies on the possibility to integrate the laws of thermodynamics in genome-scale reconstructions of cellular metabolic networks. In this work we have devised an efficient relaxation algorithm to implement thermodynamic constraints in genome-scale models. Besides allowing to check for thermodynamic feasibility of reaction flow configurations, it is also capable of providing information on other relevant physico-chemical quantities. We have applied it to two cellular metabolic networks of different complexity, namely that of human red blood cells and that of the bacterium Escherichia coli. In the former case, we have obtained predictions for the intracellular chemical state (in terms of metabolite concentrations and reaction free energies) consistent with empirical knowledge; in the latter, we have effectively corrected thermodynamically infeasible flux configurations.
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影响因子：
3
作者：
Braunstein, Alfredo;Mulet, Roberto;Pagnani, Andrea
通讯作者：
Pagnani, Andrea
将代谢物浓度包括到通量平衡分析中：热力学可实现性作为对代谢网络中通量分布的限制。
影响因子：
--
作者：
Hoppe, Andreas;Hoffmann, Sabrina;Holzhutter, Hermann-Georg
通讯作者：
Holzhutter, Hermann-Georg
影响因子：
5.4
作者：
Feist, Adam M.;Palsson, Bernhard O.
通讯作者：
Palsson, Bernhard O.
影响因子：
56.9
作者：
Mézard, M;Parisi, G;Zecchina, R
通讯作者：
Zecchina, R
DOI：
10.1073/pnas.0813229106
发表时间：
2009-02-24
影响因子：
11.1
作者：
Martelli, Carlotta;De Martino, Andrea;Castillo, Isaac Perez
通讯作者：
Castillo, Isaac Perez