Sequencing efforts have provided us with detailed information about the genetic content of various organisms across all three domains of life. As genomic sciences continue to evolve we can anticipate that multiple dimensions in genome annotation will emerge as we characterize genome-scale functions. The expansion in dimensionality of genome annotation allows for the formalization of our knowledge about genomes, their attributes and functions.
A one-dimensional annotation provides information on the location of genes and any information on the known or putative function of gene products. A two-dimensional annotation uses information about the functional networks in a cell to specify the cellular components and their interactions.
Two-dimensional annotations of many cellular processes can be represented as biochemical transformations. These two-dimensional annotations serve as biochemically and genetically structured databases through which data can be analysed and from which computational models can be generated.
Manual, automated and iterative methods for generating two-dimensional reconstructions of cellular metabolism from one-dimensional annotations have been developed and can be applied to studying other cellular processes, such as signalling, transcription and translation.
Higher dimensions in genome annotation are beginning to appear, where the three-dimensional structural arrangement of genomes within the confines of a cell are accounted for and where changes in genome sequence over evolutionary time are tracked.
We currently have the methods and information needed to generate one-dimensional and two-dimensional annotations; as we learn more about the structural arrangement of genomes within the cell and how these genomes adaptively evolve we can begin to generate higher levels of annotation.
Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Drug transporters OAT1 and OAT3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions
Scientific Reports Open Access 31 October 2022
Cellular & Molecular Immunology Open Access 04 February 2022
Scientific Reports Open Access 06 August 2020
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Thiele, I., Price, N. D., Vo, T. D. & Palsson, B. O. Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J. Biol. Chem. 280, 11683–11695 (2005).
Jamshidi, N., Wiback, S. J. & Palsson, B. O. In silico model-driven assessment of the effects of single nucleotide polymorphisms (SNPs) on human red blood cell metabolism. Genome Res. 12, 1687–1692 (2002).
Yeh, I., Hanekamp, T., Tsoka, S., Karp, P. D. & Altman, R. B. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res. 14, 917–924 (2004).
Becker, S. A. & Palsson, B. O. Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol. 5, 8 (2005).
Burgard, A. P., Pharkya, P. & Maranas, C. D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003).
Alper, H., Jin, Y. S., Moxley, J. F. & Stephanopoulos, G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 7, 155–164 (2005).
Alper, H., Miyaoku, K. & Stephanopoulos, G. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nature Biotechnol. 23, 612–616 (2005).
Fong, S. S. et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 91, 743–748 (2005).
Carlson, R., Fell, D. & Srienc, F. Metabolic pathway analysis of a recombinant yeast for rational strain development. Biotechnol. Bioeng. 79, 121–134 (2002).
Pharkya, P., Burgard, A. P. & Maranas, C. D. OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 14, 2367–2376 (2004).
Liao, J. C., Hou, S. Y. & Chao, Y. P. Pathway analysis, engineering and physiological considerations for redirecting central metabolism. Biotechnol. Bioeng. 52, 129–140 (1996).
Janssen, P., Goldovsky, L., Kunin, V., Darzentas, N. & Ouzounis, C. A. Genome coverage, literally speaking. The challenge of annotating 200 genomes with 4 million publications. EMBO Rep. 6, 397–399 (2005).
Stein, L. Genome annotation: from sequence to biology. Nature Rev. Genet. 2, 493–503 (2001). This article provides a thorough review of one-dimensional annotation methods that involve gene finding and gene-functional assignment, as well as placing genes in the context of biological processes.
Salzberg, S. L., Delcher, A. L., Kasif, S. & White, O. Microbial gene identification using interpolated Markov models. Nucleic Acids Res. 26, 544–548 (1998).
Salzberg, S. L., Pertea, M., Delcher, A. L., Gardner, M. J. & Tettelin, H. Interpolated Markov models for eukaryotic gene finding. Genomics 59, 24–31 (1999).
Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Pearson, W. R. & Lipman, D. J. Improved tools for biological sequence comparison. Proc. Natl Acad. Sci. USA 85, 2444–2448 (1988).
Eddy, S. HMMER: profile HMMs for protein sequence analysis. HMMER: sequence analysis using pofile hidden Markov Models web site [online], <http://hmmer.wustl.edu> (2003).
Bowers, P. M. et al. Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol. 5, R35 (2004). This article describes several context-based methods for identifying genes that are functionally related. The article also announces the creation of the Prolinks database that includes results for several genomes.
Overbeek, R., Fonstein, M., D'Souza, M., Pusch, G. D. & Maltsev, N. Use of contiguity on the chromosome to predict functional coupling. In Silico Biol. 1, 93–108 (1999).
Overbeek, R., Fonstein, M., D'Souza, M., Pusch, G. D. & Maltsev, N. The use of gene clusters to infer functional coupling. Proc. Natl Acad. Sci. USA 96, 2896–2901 (1999).
Enright, A. J., Iliopoulos, I., Kyrpides, N. C. & Ouzounis, C. A. Protein interaction maps for complete genomes based on gene fusion events. Nature 402, 86–90 (1999).
Marcotte, E. M. et al. Detecting protein function and protein–protein interactions from genome sequences. Science 285, 751–753 (1999).
Marcotte, C. J. & Marcotte, E. M. Predicting functional linkages from gene fusions with confidence. Appl. Bioinformatics 1, 93–100 (2002).
Wu, J., Kasif, S. & DeLisi, C. Identification of functional links between genes using phylogenetic profiles. Bioinformatics 19, 1524–1530 (2003).
Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D. & Yeates, T. O. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl Acad. Sci. USA 96, 4285–4288 (1999).
Kharchenko, P., Vitkup, D. & Church, G. M. Filling gaps in a metabolic network using expression information. Bioinformatics 20 (Suppl. 1), I178–I185 (2004).
Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).
Walker, M. G., Volkmuth, W., Sprinzak, E., Hodgson, D. & Klingler, T. Prediction of gene function by genome-scale expression analysis: prostate cancer-associated genes. Genome Res. 9, 1198–1203 (1999).
Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).
Zhang, W. et al. The functional landscape of mouse gene expression. J. Biol. 3, 21 (2004).
Kelley, R. & Ideker, T. Systematic interpretation of genetic interactions using protein networks. Nature Biotechnol. 23, 561–566 (2005).
Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J. & Palsson, B. O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92–96 (2004). This article describes an iterative model-building approach for identifying new regulatory interactions that is based on gene-expression data. The work also resulted in the identification of knowledge gaps in metabolism and regulation from analysis of mutant phenotyping data.
Borodina, I., Krabben, P. & Nielsen, J. Genome-scale analysis of Streptomyces coelicolor A3(2) metabolism. Genome Res. 15, 820–829 (2005). This article describes a metabolic reconstruction that is generated by automated methods followed by manual curation for Streptomyces coelicolor . It discusses problems that are associated with automated reconstructions and provides examples where two-dimensional annotation enhanced one-dimensional annotation by finding genes for missing metabolic enzymes.
Green, M. L. & Karp, P. D. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 5, 76 (2004). This article presents a method for identifying the genes responsible for encoding enzymes that are missing from pathways in current metabolic reconstructions. This method was applied to reconstructions from three different organisms and led to new putative assignments for about half the missing enzymes.
Karp, P. D., Krummenacker, M., Paley, S. & Wagg, J. Integrated pathway-genome databases and their role in drug discovery. Trends Biotechnol. 17, 275–281 (1999).
Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54 (2003).
Price, N. D., Reed, J. L. & Palsson, B. O. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature Rev. Microbiol. 2, 886–897 (2004). This review provides a comprehensive overview of developed methods for interrogating reconstructions using a constraint-based modelling approach.
Papin, J. A., Hunter, T., Palsson, B. O. & Subramaniam, S. Reconstruction of cellular signalling networks and analysis of their properties. Nature Rev. Mol. Cell Biol. 6, 99–111 (2005).
Papin, J. A. & Palsson, B. O. The JAK–STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys. J. 87, 37–46 (2004).
Ouzounis, C. A. & Karp, P. D. The past, present and future of genome-wide re-annotation. Genome Biol 3, COMMENT 2001 (2002).
Schomburg, I. et al. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 32, D431–D433 (2004).
Duarte, N. C., Herrgard, M. J. & Palsson, B. O. Reconstruction and validation of Saccharomyces cerevisiae i ND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 14, 1298–1309 (2004).
Gardy, J. L. et al. PSORTb v. 2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 21, 617–623 (2005).
Hua, S. & Sun, Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17, 721–728 (2001).
Schneider, G. & Fechner, U. Advances in the prediction of protein targeting signals. Proteomics 4, 1571–1580 (2004).
Ross-Macdonald, P. et al. Large-scale analysis of the yeast genome by transposon tagging and gene disruption. Nature 402, 413–418 (1999).
Huh, W. K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).
Gasteiger, E. et al. ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31, 3784–3788 (2003).
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
Keseler, I. M. et al. EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res. 33, D334–D337 (2005).
Overbeek, R. et al. WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Res. 28, 123–125 (2000).
Christie, K. R. et al. Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res. 32, D311–D314 (2004).
Krieger, C. J. et al. MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res. 32, D438–D442 (2004).
Vo, T. D., Greenberg, H. J. & Palsson, B. O. Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. J. Biol. Chem. 279, 39532–39540 (2004).
Neidhardt, F. C., Ingraham, J. L. & Schaechter, M. Physiology of the bacterial cell (Sinauer Associates, Sunderland, Massachusetts, 1990).
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).
Famili, I. & Palsson, B. O. Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices. J. Theor. Biol. 224, 87–96 (2003).
Thiele, I., Vo, T. D., Price, N. D. & Palsson, B. O. An expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): An in silico genome-scale characterization of single and double deletion mutants. J. Bacteriol. 187, 5818–5830 (2005).
Karp, P. D., Paley, S. & Romero, P. The Pathway Tools software. Bioinformatics 18 (Suppl. 1), S225–S232 (2002).
Paley, S. M. & Karp, P. D. Evaluation of computational metabolic-pathway predictions for Helicobacter pylori. Bioinformatics 18, 715–724 (2002).
Tsoka, S., Simon, D. & Ouzounis, C. A. Automated metabolic reconstruction for Methanococcus jannaschii. Archaea 1, 223–229 (2004).
Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6, R2 (2005).
Zhang, P. et al. MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol. 138, 27–37 (2005).
Romero, P. & Karp, P. PseudoCyc, a pathway-genome database for Pseudomonas aeruginosa. J. Mol. Microbiol. Biotechnol. 5, 230–239 (2003).
Larsson, P. et al. The complete genome sequence of Francisella tularensis, the causative agent of tularemia. Nature Genet. 37, 153–159 (2005).
Karp, P. D. et al. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res. 33, 6083–6089 (2005).
Serres, M. H. et al. A functional update of the Escherichia coli K-12 genome. Genome Biol. 2, RESEARCH 0035 (2001).
Burgard, A. P., Nikolaev, E. V., Schilling, C. H. & Maranas, C. D. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 14, 301–312 (2004).
Palsson, B. The challenges of in silico biology. Nature Biotechnol. 18, 1147–1150 (2000).
Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934 (2001). This article illustrates how the combination of experimental measurements and model predictions can be used to identify new network interactions. The experiments were carried out to better understand and generate new hypotheses concerning galactose utilization in yeast.
Thanbichler, M., Viollier, P. H. & Shapiro, L. The structure and function of the bacterial chromosome. Curr. Opin. Genet. Dev. 15, 153–162 (2005). This review discusses studies that relate to the topological (three-dimensional) structure of bacterial chromosomes. It describes recent evidence that the organization of bacterial chromosomes is non-random and that during replication the position of the genome within the cell is spatially arranged.
Chakalova, L. et al. Replication and transcription: shaping the landscape of the genome. Nature Rev. Genet. 6, 669–677 (2005).
Viollier, P. H. et al. Rapid and sequential movement of individual chromosomal loci to specific subcellular locations during bacterial DNA replication. Proc. Natl Acad. Sci. USA 101, 9257–9262 (2004).
Allen, T. E. et al. Genome-scale analysis of the uses of the Escherichia coli genome: model-driven analysis of heterogeneous data sets. J. Bacteriol. 185, 6392–6399 (2003).
Jeong, K. S., Ahn, J. & Khodursky, A. B. Spatial patterns of transcriptional activity in the chromosome of Escherichia coli. Genome Biol. 5, R86 (2004).
Gerdes, S. Y. et al. Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J. Bacteriol. 185, 5673–5684 (2003).
Rocha, E. P. & Danchin, A. Gene essentiality determines chromosome organisation in bacteria. Nucleic Acids Res. 31, 6570–6577 (2003).
Rocha, E. P. & Danchin, A. Essentiality, not expressiveness, drives gene-strand bias in bacteria. Nature Genet. 34, 377–378 (2003).
Hatfield, G. W. & Benham, C. J. DNA topology-mediated control of global gene expression in Escherichia coli. Annu. Rev. Genet. 36, 175–203 (2002).
Travers, A. & Muskhelishvili, G. DNA supercoiling — a global transcriptional regulator for enterobacterial growth? Nature Rev. Microbiol. 3, 157–169 (2005).
Flores, N. et al. Adaptation for fast growth on glucose by differential expression of central carbon metabolism and gal regulon genes in an Escherichia coli strain lacking the phosphoenolpyruvate:carbohydrate phosphotransferase system. Metab. Eng. 7, 70–87 (2005).
Raghunathan, A. & Palsson, B. O. Scalable method to determine mutations that occur during adaptive evolution of Escherichia coli. Biotechnol. Lett. 25, 435–441 (2003).
Notley-McRobb, L. & Ferenci, T. Adaptive mgl-regulatory mutations and genetic diversity evolving in glucose-limited Escherichia coli populations. Environ. Microbiol. 1, 33–43 (1999).
Anderson, J. B. et al. Mode of selection and experimental evolution of antifungal drug resistance in Saccharomyces cerevisiae. Genetics 163, 1287–1298 (2003).
Honisch, C., Raghunathan, A., Cantor, C. R., Palsson, B. O. & van den Boom, D. High-throughput mutation detection underlying adaptive evolution of Escherichia coli-K12. Genome Res. 14, 2495–2502 (2004).
Shendure, J. et al. Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309, 1728–1732 (2005). This article describes a new non-electrophoretic DNA-sequencing method for rapid whole-genome sequencing and provides results for the DNA sequence of an adaptively evolved strain of E. coli.
Palsson, B. O. Systems Biology: Properties of Reconstructed Networks (Cambridge Univ. Press, 2006).
Reed, J. L. & Palsson, B. O. Thirteen years of building constraint-based in silico models of Escherichia coli. J. Bacteriol. 185, 2692–2699 (2003).
Edwards, J. S. & Palsson, B. O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl Acad. Sci. USA 97, 5528–5533 (2000).
Forster, J., Famili, I., Fu, P., Palsson, B. O. & Nielsen, J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244–253 (2003).
Sheikh, K., Forster, J. & Nielsen, L. K. Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus. Biotechnol. Prog. 21, 112–121 (2005).
Park, S. M., Schilling, C. H. & Palsson, B. O. Compositions and methods for modeling Bacillus subtilis metabolism (US Patent and Trademark Office, 2003).
Schilling, C. H. & Palsson, B. O. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J. Theor. Biol. 203, 249–283 (2000).
Edwards, J. S. & Palsson, B. O. Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem. 274, 17410–17416 (1999).
Schilling, C. H. et al. Genome-scale metabolic model of Helicobacter pylori 26695. J. Bacteriol. 184, 4582–4593 (2002).
Oliveira, A. P., Nielsen, J. & Forster, J. Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol. 5, 39 (2005).
Hong, S. H. et al. The genome sequence of the capnophilic rumen bacterium Mannheimia succiniciproducens. Nature Biotechnol. 22, 1275–1281 (2004).
Eppig, J. T. et al. The Mouse Genome Database (MGD): from genes to mice — a community resource for mouse biology. Nucleic Acids Res. 33, D471–D475 (2005).
Palsson, B. O. Two-dimensional annotation of genomes. Nature Biotechnol. 22, 1218–1219 2004).
Woldringh, C. L. The role of co-transcriptional translation and protein translocation (transertion) in bacterial chromosome segregation. Mol. Microbiol. 45, 17–29 (2002).
Ibarra, R. U., Edwards, J. S. & Palsson, B. O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002).
Mahadevan, R. et al. Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl. Environ. Microbiol. (in the press).
Feist, A. M. et al. Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri. Mol. Systems Biol. (in the press).
The authors would like to thank T. Allen and S. Fong for useful comments on the manuscript. This work was funded in part by the US National Institutes of Health. B.O.P. serves on the scientific advisory board of Genomatica, Inc.
Jennifer L. Reed, Iman Famili, Ines Thiele & Bernhard O. Palsson Towards multidimensional genome annotation. Nature Reviews Genetics 7, 130–141 (2006); doi:10.1038/nrg1769 Bernhard O. Palsson serves on the Scientific Advisory Board of Genomatica, Inc.
- One-dimensional annotation
Details the position of genes within the genome and describes the cellullar function of gene products.
- Two-dimensional annotation
Accounts for the cellular components that are identified in a one-dimensional annotation as well as their chemical and physical interactions.
- Network reconstruction
A description of the network components and their interactions.
- Three-dimensional annotation
Details the spatial location of genes (rather than the gene products) within the cell as a result of genome packaging.
- Four-dimensional annotation
Details changes in genome sequence that result from adaptive evolution.
- Metabolite connectivity
The number of reactions a given metabolite participates in.
- Systemic reactions
Mathematically derived reactions which represent overall or dominant types of chemical transformation in a given network.
Proteins encoded by different genes that catalyse the same reaction.
- Computational model
A set of equations that mathematically represents network reconstruction and is used to predict the behaviour of a system.
- Precursor metabolites
Metabolites that are generated by catabolic pathways and used by anabolic pathways to generate biomass components.
- Biomass components
The macromolecules (proteins, carbohydrates, lipids and nucleotides), vitamins, cofactors, metals and minerals that make up a cell.
- Boolean rules
Logic statements that use Boolean operators (and, or, not) to evaluate the on/off state of a variable.
- P/O ratio
The number of ATP molecules (P) that are formed per oxygen atom (O) consumed during respiration.
- Network gap
One or more reaction that is missing from the network reconstruction owing to the lack of direct genetic or biochemical evidence.
- Blocked reactions
Reactions that, at steady state, can have no net flux (reactions that involve dead-end metabolites are blocked reactions).
- Pathway holes
Missing reactions from defined metabolic pathways such as glycolysis and amino-acid biosynthesis.
- Dead-end metabolites
A metabolite that is either only produced or only consumed by the metabolic network (pathway holes, network gaps and blocked reactions involve dead-end metabolites).
- Flux-coupling analysis
A computational method that determines how fluxes through a pair of reactions are related.
About this article
Cite this article
Reed, J., Famili, I., Thiele, I. et al. Towards multidimensional genome annotation. Nat Rev Genet 7, 130–141 (2006). https://doi.org/10.1038/nrg1769
This article is cited by
Cellular & Molecular Immunology (2022)
Drug transporters OAT1 and OAT3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions
Scientific Reports (2022)
A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes
Theoretical and Applied Genetics (2021)
Scientific Reports (2020)
Genome-scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation
Scientific Reports (2020)