A Graph Theory Augmented Math Programming Approach to Identify Genetic Targets for Strain Improvement

Sudhakar Jonnalagadda, Balaji Balagurunathan, Lee Dong-Yup, Rajagopalan Srinivasan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Improvement of biological strains through targeted modification of metabolism is essential for successful development of bioprocesses. The computational complexity of optimization procedures routinely used for identifying genetic targets limits their application to genome-scale metabolic networks. In this study, we combined graph theoretic approaches with mixed-integer liner programming (MILP) to reduce the search space and thus reducing computational time. Specifically, we used cut-sets (minimal set of reactions that cuts metabolic networks) as additional constraints to reduce the search space. The efficacy of proposed approach is illustrated by identifying minimal reaction set for Saccharomyces Cerevisiae.

Original languageEnglish
Title of host publication19th European Symposium on Computer Aided Process Engineering
EditorsJacek Jezowski, Jan Thullie
Pages1051-1055
Number of pages5
DOIs
StatePublished - 2009
Externally publishedYes

Publication series

NameComputer Aided Chemical Engineering
Volume26
ISSN (Print)1570-7946

Keywords

  • Cut-sets
  • MILP
  • Strain improvement

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