ReviewComputational and structural biotechnology journal2021
Advances in flux balance analysis by integrating machine learning and mechanism-based models.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning.Scientific reports · 2026Article
- Screening of exogenous nutrients for pathogenic bacteria and development of highly active bactericides.mSystems · 2026Article
- Cancer-associated fibroblasts drive metabolic heterogeneity in colorectal cancer cells: predictions from metabolic modeling.NPJ systems biology and applications · 2026Article
- Exploring CHO cell stability during prolonged passaging via eXplainable AI driven flux balance analysis.NPJ systems biology and applications · 2026Article
- Quantifying and comparing causal patterns in stochastic chemical reaction networks.NPJ systems biology and applications · 2026Article
- Computational and multi-omics systems biology for precision microbiome therapeutics.Frontiers in microbiomes · 2026Review
- EngineeringFrontiers in microbiology · 2026Review
- The Need for a Systems Biology Approach in Cancer Explained.International journal of molecular sciences · 2025Review
- Non-canonical plant metabolism.Nature plants · 2025Review
- A Guide to Metabolic Network Modeling for Plant Biology.Plants (Basel, Switzerland) · 2025Review
- Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study.mSystems · 2025Article
- In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.FEMS microbiology reviews · 2025Review
- Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers.Endocrine oncology (Bristol, England) · 2025Review
- From single cells to communities: Mathematical perspectives on bacterial quorum sensing.Computational and structural biotechnology journal · 2025Review
- Article
- MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism.Bioinformatics advances · 2025Article
- MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling.Computational and structural biotechnology journal · 2025Article
- Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models.Machine learning · 2025Article
- Deciphering and designing microbial communities by genome-scale metabolic modelling.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.