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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >PreProPath: An Uncertainty-Aware Algorithm for Identifying Predictable Profitable Pathways in Biochemical Networks
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PreProPath: An Uncertainty-Aware Algorithm for Identifying Predictable Profitable Pathways in Biochemical Networks

机译： PreProPath ：一种不确定性识别算法，用于识别生化网络中可预测的获利途径

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摘要

Pathway analysis is a powerful approach to enable rational design or redesign of biochemical networks for optimizing metabolic engineering and synthetic biology objectives such as production of desired chemicals or biomolecules from specific nutrients. While experimental methods can be quite successful, computational approaches can enhance discovery and guide experimentation by efficiently exploring very large design spaces. We present a computational algorithm, Predictably Profitable Path (), to identify target pathways best suited for engineering modifications. The algorithm utilizes uncertainties about the metabolic networks operating state inherent in the underdetermined linear equations representing the stoichiometric model. Flux Variability Analysis is used to determine the operational flux range. identifies a path that is predictable in behavior, exhibiting small flux ranges, and profitable, containing the least restrictive flux-limiting reaction in the network. The algorithm is computationally efficient because it does not require enumeration of pathways. The results of case studies show that can efficiently analyze variances in metabolic states and model uncertainties to suggest pathway engineering strategies that have been previously supported by experimental data.
机译：途径分析是一种强大的方法，可以合理设计或重新设计生化网络，以优化代谢工程和合成生物学目标，例如从特定营养素生产所需的化学药品或生物分子。尽管实验方法可以相当成功，但计算方法可以通过有效地探索非常大的设计空间来增强发现并指导实验。我们提出了一种计算算法，可预测的获利路径（），以识别最适合工程修改的目标路径。该算法利用了不确定的代表化学计量模型的线性方程中固有的代谢网络运行状态的不确定性。磁通变异性分析用于确定工作磁通范围。标识行为可预测，通量范围较小且有利可图的路径，其中包含网络中限制最小的通量限制反应。该算法计算效率高，因为它不需要枚举路径。案例研究的结果表明，它可以有效地分析代谢状态的变化并建立模型不确定性，以提示先前已得到实验数据支持的途径工程策略。

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