ArticleBMC systems biology2011
Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change.Frontiers in endocrinology · 2022Article
- Tryptophan Production Maximization in a Fed-Batch Bioreactor with ModifiedBioengineering (Basel, Switzerland) · 2021Article
- MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering.BMC bioinformatics · 2020Article
- Fuzzy Decision Making Approach to Identify Optimum Enzyme Targets and Drug Dosage for Remedying Presynaptic Dopamine Deficiency.PloS one · 2016Article
- Fuzzy optimization for detecting enzyme targets of human uric acid metabolism.Bioinformatics (Oxford, England) · 2013Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundImproving 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.
resultsThis 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.
conclusionsConsidering 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.
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