Please use this identifier to cite or link to this item: https://doi.org/10.1016/S1570-7946(09)70175-0
Title: A Graph Theory Augmented Math Programming Approach to Identify Genetic Targets for Strain Improvement
Authors: Jonnalagadda, S.
Balagurunathan, B.
Dong-Yup, L. 
Srinivasan, R. 
Keywords: Cut-sets
MILP
Strain improvement
Issue Date: 2009
Source: Jonnalagadda, S.,Balagurunathan, B.,Dong-Yup, L.,Srinivasan, R. (2009). A Graph Theory Augmented Math Programming Approach to Identify Genetic Targets for Strain Improvement. Computer Aided Chemical Engineering 26 : 1051-1055. ScholarBank@NUS Repository. https://doi.org/10.1016/S1570-7946(09)70175-0
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. © 2009 Elsevier B.V. All rights reserved.
Source Title: Computer Aided Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/54227
ISBN: 9780444534330
ISSN: 15707946
DOI: 10.1016/S1570-7946(09)70175-0
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