The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Flux balance analysis of biological systems: applications and challenges
Open accessAbstract
Systems level modelling and simulations of biological processes are proving to be invaluable in obtaining a quantitative and dynamic perspective of various aspects of cellular function. In particular, constraint-based analyses of metabolic networks have gained considerable popularity for simulating cellular metabolism, of which flux balance analysis (FBA), is most widely used. Unlike mechanistic simulations that depend on accurate kinetic data, which are scarcely available, FBA is based on the principle of conservation of mass in a network, which utilizes the stoichiometric matrix and a biologically relevant objective function to identify optimal reaction flux distributions. FBA has been used to analyse genome-scale reconstructions of several organisms; it has also been used to analyse the effect of perturbations, such as gene deletions or drug inhibitions in silico. This article reviews the usefulness of FBA as a tool for gaining biological insights, advances in methodology enabling integration of regulatory information and thermodynamic constraints, and finally addresses the challenges that lie ahead. Various use scenarios and biological insights obtained from FBA, and applications in fields such metabolic engineering and drug target identification, are also discussed. Genome-scale constraint-based models have an immense potential for building and testing hypotheses, as well as to guide experimentation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0
Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835
Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Hatzimanikatis, V., & Palsson, B. Ø. (2007) · Molecular Systems Biology · doi:10.1038/msb4100155 · Cited by 1,383
Global reconstruction of the human metabolic network based on genomic and bibliomic data
Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610772104 · Cited by 1,374
Analysis of optimality in natural and perturbed metabolic networks
Segrè, D., Vitkup, D., & Church, G. M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.232349399 · Cited by 1,362
Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003) · Biotechnology and Bioengineering · doi:10.1002/bit.10803 · Cited by 1,256
Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network
Förster, J., Famili, I., Fu, P., Palsson, B. Ø., & Nielsen, J. (2003) · Genome Research · doi:10.1101/gr.234503 · Cited by 1,069
Genome-scale models of microbial cells: evaluating the consequences of constraints
Price, N. D., Reed, J. L., & Palsson, B. Ø. (2004) · Nature Reviews Microbiology · doi:10.1038/nrmicro1023 · Cited by 1,023
An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR)
Reed, J. L., Vo, T. D., Schilling, C. H., & Palsson, B. O. (2003) · Genome Biology · doi:10.1186/gb-2003-4-9-r54 · Cited by 999
Edwards, J. S., Ibarra, R. U., & Palsson, B. O. (2001) · Nature Biotechnology · doi:10.1038/84379 · Cited by 939
Edwards, J. S., & Palsson, B. O. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5528 · Cited by 875
Cited by
Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology
Karp, P. D., Paley, S. M., Krummenacker, M., Latendresse, M., Dale, J. M., Lee, T. J., Kaipa, P., Gilham, F., Spaulding, A., Popescu, L., et al. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbp043 · Cited by 694
Karp, P. D., Paley, S., & Romero, P. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s225 · Cited by 661
Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways
Jia, D., Lu, M., Jung, K. H., Park, J. H., Yu, L., Onuchic, J. N., Kaipparettu, B. A., & Levine, H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816391116 · Cited by 336
A review of network-based approaches to drug repositioning
Lotfi Shahreza, M., Ghadiri, N., Mousavi, S. R., Varshosaz, J., & Green, J. R. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx017 · Cited by 330
New applications of synthetic biology tools for cyanobacterial metabolic engineering
Santos-Merino, M., Singh, A. K., & Ducat, D. C. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00033 · Cited by 222
Metadata
- Authors
- Raman, K., & Chandra, N.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-03-15
- Volume
- 10
- Issue
- 4
- Pages
- 435-449
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2078775934
- Cited by
- 437
- References
- 80
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Flux balance analysis of biological systems: applications and challenges. https://app.cochat.ai/papers/10.1093/bib/bbp011
- The paper
- Raman, K., & Chandra, N. (2009). Flux balance analysis of biological systems: applications and challenges. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbp011
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
