ArticleBMC systems biology2015
Flux-sum analysis identifies metabolite targets for strain improvement.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 20 citations in OpenAlex.
- Exploring CHO cell stability during prolonged passaging via eXplainable AI driven flux balance analysis.NPJ systems biology and applications · 2026Article
- Protein-constrained models pinpoints the role of underground metabolism in robustness of metabolic phenotypes.iScience · 2025Article
- Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data.Genome biology · 2024Article
- Systems-level analysis of the plasticity of the maize metabolic network reveals novel hypotheses in the nitrogen-use efficiency of maize roots.Journal of experimental botany · 2022Article
- In silico model-guided identification of transcriptional regulator targets for efficient strain design.Microbial cell factories · 2018Article
- Metabolic engineering of Klebsiella pneumoniae based on in silico analysis and its pilot-scale application for 1,3-propanediol and 2,3-butanediol co-production.Journal of industrial microbiology & biotechnology · 2017Article
- In silico profiling of cell growth and succinate production inBioresources and bioprocessing · 2016Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets.
resultsIn this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology.
conclusionsThe application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering.
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