利用化学计量冗余计算效率和网络减少。

Q2 Medicine In Silico Biology Pub Date : 2015-01-01 DOI:10.3233/ISB-140464
Brian P Ingalls, Eric Bembenek
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引用次数: 2

摘要

代谢网络的分析通常从化学计量矩阵的构建开始,它表征了网络的拓扑结构。该矩阵通过平衡方程描述了潜在的稳态流量分布。本文首先观察到平衡方程仅取决于网络中线性冗余的结构,因此可以以简洁的方式陈述,从而提高稳态分析的计算效率。这种对稳态行为的替代描述随后被用于提供一种新的网络缩减方法,它补充了现有的从输入-输出宏观反应角度描述细胞内网络的算法(以促进生物过程优化和控制)。最后,证明了这种新颖的约简方法可以用于解决大型网络的基本模式分析:由约简网络支持的模式可以捕获代谢模块的输入-输出模式,大大减少了计算工作量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Exploiting stoichiometric redundancies for computational efficiency and network reduction.

Analysis of metabolic networks typically begins with construction of the stoichiometry matrix, which characterizes the network topology. This matrix provides, via the balance equation, a description of the potential steady-state flow distribution. This paper begins with the observation that the balance equation depends only on the structure of linear redundancies in the network, and so can be stated in a succinct manner, leading to computational efficiencies in steady-state analysis. This alternative description of steady-state behaviour is then used to provide a novel method for network reduction, which complements existing algorithms for describing intracellular networks in terms of input-output macro-reactions (to facilitate bioprocess optimization and control). Finally, it is demonstrated that this novel reduction method can be used to address elementary mode analysis of large networks: the modes supported by a reduced network can capture the input-output modes of a metabolic module with significantly reduced computational effort.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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