What is flux balance analysis?
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Later methods needing fewer parameters include Boolean network and constraint-based modeling.
“Subsequently, alternative approaches were developed that require fewer parameters, including Boolean network modeling (Davidson et al., 2002) and constraint-based modeling (Orth et al., 2010; Thiele et al., 2009).”
From A Whole-Cell Computational Model Predicts Phenotype from Genotype (2012) · cites this paper as Orth et al., 2010
Flux Balance Analysis models have been used to predict and understand the behavior of microbes given their environment.
“For instance, Flux Balance Analysis (FBA) models have been used to predict and understand the behavior of microbes given their environment (Orth et al., 2010;Thiele et al., 2013;Zelezniak et al., 2015).”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Orth et al., 2010
ME models have only been developed for Thermotoga maritima and Escherichia coli because they require detailed knowledge of all steps of protein synthesis that is not readily available for all organisms, particularly eukaryal cells.
“ME models have only been developed for Thermotoga maritima (Lerman et al, 2012) and E. coli (Orth et al, 2010) because they require detailed knowledge of all the steps of protein synthesis (protein maturation, protein folding, metal binding, etc.), which are not readily available for all organisms, and in particular for eukaryal cells.”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Orth et al, 2010
GECKO uses the flux balance analysis (FBA) approach.
“GECKO uses the flux balance analysis (FBA) approach (Orth et al, 2010).”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Orth et al, 2010
Flux balance analysis allows metabolic fluxes to be compared with transcriptome and proteome measurements and yields genome-scale information on flux control.
“This approach, called flux balance analysis (Orth et al., 2010), enables comparison of metabolic fluxes with transcriptome and proteomedataandprovidesinformationaboutthe control of fluxes at the genome scale (Bordel et al., 2010).”
From Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast (2017) · cites this paper as Orth et al., 2010
Heckmann et al.'s kcat prediction model for E. coli relies on detailed expert-crafted input features and achieved R2 approximately 0.34 on an independent test set.
“Heckmann et al. 14 developed a k cat prediction model for enzymes in E. coli . The model relies on detailed, expert-crafted input features such as enzyme active site properties, metabolite concentrations, experimental conditions, and reaction fluxes calculated through flux balance analysis (FBA) 15 . It achieved a coefficient of determination R 2 ≈ 0.34 on an independent test set.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [15]
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- Authors
- Orth, J. D., Thiele, I., & Palsson, B. Ø.
- Affiliations
- University of California San Diego (US)
- University of Iceland (IS)
- Corresponding author
- Palsson, Bernhard Ø
- Venue
- Nature Biotechnology
- Published
- 2010-03
- Volume
- 28
- Issue
- 3
- Pages
- 245-248
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3108565
- OpenAlex
- W2078355959
- Cited by
- 3,903
- References
- 44
- Citations by year
- 2026: 262 · 2025: 292 · 2024: 304 · 2023: 323 · 2022: 277 · 2021: 308 · 2020: 290 · 2019: 301 · 2018: 290 · 2017: 255 · 2016: 217 · 2015: 197 · 2014: 179 · 2013: 179 · 2012: 131
- FWCI
- 52.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM057089)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- flux balance analysis, metabolic networks, metabolic modeling, stoichiometric modeling, software toolbox
- MeSH terms
- Escherichia coli, Models, Biological, Oxygen, Phenotype, Biomass, Systems Biology, Metabolic Networks and Pathways
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