A New Approach to Obtain EFMs Using Graph Methods Based on the Shortest Path between End Nodes

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• 作者：Jose Francisco Hidalgo C茅spedes (20)
  Francisco De As铆s Guil Asensio (20)
  Jose Manuel Garc铆a Carrasco (20)
  
  20. Grupo de Arquitectura y Computaci贸n Paralela ; Universidad de Murcia ; Spain
  
• 关键词：Metabolic networks ; graph theory ; EFM ; flux modes ; pathways ; systems biology
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9043
• 期：1
• 页码：641-649
• 全文大小：171 KB
• 参考文献：1. Croes, D., Couche, F., Wodak, S.J., et al.: Metabolic PathFinding: Inferring relevant pathways in biochemical networks. Nucleic Acids Res. 2005(33), W326鈥揥330
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• 作者单位：Bioinformatics and Biomedical Engineering
• 丛书名：978-3-319-16482-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Genome-scale metabolic networks let us to understand the behavior of the metabolism in the cells of live organisms. The availability of great amounts of such data gives scientific community the opportunity to infer in silico new metabolic knowledge. Elementary Flux Modes (EFM) are minimal contained pathways or subsets of a metabolic network that are very useful to achieve the comprehension of a very specific metabolic function (as well as dis-functions), and to get the knowledge to develop new drugs. Metabolic networks can have large connectivity and, therefore, EFMs resolution faces a combinational explosion challenge to be solved. In this paper we propose a new approach to obtain EFMs based on graph methods and the shortest path between end nodes. Our method finds all the pathways in the metabolic network and it is able to prioritize the pathway search accounting the biological mean pursued. Our technique has two phases, the exploration one and the characterization one, and we show how it works in a well-known case study.