Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-09-19

AUTHORS

Wu-Hsiung Wu, Feng-Sheng Wang, Maw-Shang Chang

ABSTRACT

BACKGROUND: Improving the synthesis rate of desired metabolites in metabolic systems is one of the main tasks in metabolic engineering. In the last decade, metabolic engineering approaches based on the mathematical optimization have been used extensively for the analysis and manipulation of metabolic networks. Experimental evidence shows that mutants reflect resilience phenomena against gene alterations. Although researchers have published many studies on the design of metabolic systems based on kinetic models and optimization strategies, almost no studies discuss the multi-objective optimization problem for enzyme manipulations in metabolic networks considering resilience phenomenon. RESULTS: This study proposes a generalized fuzzy multi-objective optimization approach to formulate the enzyme intervention problem for metabolic networks considering resilience phenomena and cell viability. This approach is a general framework that can be applied to any metabolic networks to investigate the influence of resilience phenomena on gene intervention strategies and maximum target synthesis rates. This study evaluates the performance of the proposed approach by applying it to two metabolic systems: S. cerevisiae and E. coli. Results show that the maximum synthesis rates of target products by genetic interventions are always over-estimated in metabolic networks that do not consider the resilience effects. CONCLUSIONS: Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. The proposed generalized fuzzy multi-objective optimization approach has the potential to be a good and practical framework in the design of metabolic networks. More... »

PAGES

145-145

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1752-0509-5-145

DOI

http://dx.doi.org/10.1186/1752-0509-5-145

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025493971

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21929795


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