Heath Allison P, Bennett George N, Kavraki Lydia E
Department of Computer Science, Rice University, Houston, TX 77005, USA.
J Comput Biol. 2011 Nov;18(11):1575-97. doi: 10.1089/cmb.2011.0165. Epub 2011 Oct 14.
This article presents a new graph-based algorithm for identifying branched metabolic pathways in multi-genome scale metabolic data. The term branched is used to refer to metabolic pathways between compounds that consist of multiple pathways that interact biochemically. A branched pathway may produce a target compound through a combination of linear pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While branched metabolic pathways predominate in metabolic networks, most previous work has focused on identifying linear metabolic pathways. The ability to automatically identify branched pathways is important in applications that require a deeper understanding of metabolism, such as metabolic engineering and drug target identification. The algorithm presented in this article utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on several well-characterized metabolic pathways that demonstrate that the new merging approach can efficiently find biologically relevant branched metabolic pathways.
本文提出了一种基于图形的新算法，用于在多基因组规模代谢数据中识别分支代谢途径。术语“分支”用于指代化合物之间的代谢途径，这些途径由多个在生物化学上相互作用的途径组成。分支途径可能通过线性途径的组合产生目标化合物，这些线性途径将化合物分解为较小的化合物，与许多化合物并行作用，并将化合物合并为较大的化合物。虽然分支代谢途径在代谢网络中占主导地位，但大多数先前的工作都集中在识别线性代谢途径上。自动识别分支途径的能力在需要更深入理解代谢的应用中很重要，例如代谢工程和药物靶点识别。本文提出的算法利用显式原子跟踪来识别线性代谢途径，然后将它们合并为分支代谢途径。我们在几个特征明确的代谢途径上给出了结果，证明了这种新的合并方法能够有效地找到与生物学相关的分支代谢途径。
