Reader TTS

ABSTRACT A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as

biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the

optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that _Escherichia coli_ uses its metabolism to grow at a maximal rate using the

_E. coli_ MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or

succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the _E. coli_

metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of _in silico_ and experimental biology can

be used to obtain a quantitative genotype–phenotype relationship for metabolism in bacterial cells. Access through your institution Buy or subscribe This is a preview of subscription

content, access via your institution ACCESS OPTIONS Access through your institution Subscribe to this journal Receive 12 print issues and online access $209.00 per year only $17.42 per issue

Learn more Buy this article * Purchase on SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which are calculated during checkout ADDITIONAL

ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS A GENOME-SCALE METABOLIC MODEL OF

_SACCHAROMYCES CEREVISIAE_ THAT INTEGRATES EXPRESSION CONSTRAINTS AND REACTION THERMODYNAMICS Article Open access 09 August 2021 RECONSTRUCTING ORGANISMS IN SILICO: GENOME-SCALE MODELS AND

THEIR EMERGING APPLICATIONS Article 21 September 2020 RECONSTRUCTION OF A CATALOGUE OF GENOME-SCALE METABOLIC MODELS WITH ENZYMATIC CONSTRAINTS USING GECKO 2.0 Article Open access 30 June

2022 CHANGE HISTORY * _ 21 MARCH 2003 Supplementary Table 1 HTML format was replaced with an Excel version _ NOTES * * NOTE:The original Supplementary Table 1 (HTML format) has been replaced

with an Excel file. REFERENCES * TIGR Microbial database: a listing of published microbial genomes and chromosomes and those in progress. (The Institute for Genomic Research, Rockville, MD,

2000). http://www.tigr.org/tdb/mdb/mdbcomplete.html * Karp, P.D. et al. The EcoCyc and MetaCyc databases. _Nucleic Acids Res._ 28, 56–59 (2000). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Selkov, E. Jr.,, Grechkin, Y., Mikhailova, N. & Selkov, E. MPW: the Metabolic Pathways Database. _Nucleic Acids Res._ 26, 43–45 (1998). Article  CAS  PubMed  PubMed Central 

Google Scholar  * Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. _Nucleic Acids Res._ 27, 29–34 (1999). Article  CAS  PubMed  PubMed Central  Google Scholar  * Karp. P.D.,

Ouzounis, C. & Paley, S. HinCyc: a knowledge base of the complete genome and metabolic pathways of _H. influenzae_. _Proceedings of the ISMB-96 Conference_ 4, 116–124 (1996). CAS  Google

Scholar  * Overbeek, R. et al. WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. _Nucleic Acids Res._ 28, 123–125 (2000). Article  CAS 

PubMed  PubMed Central  Google Scholar  * Edwards, J.S. & Palsson, B.O. Systems Properties of the _Haemophilus influenzae_ Rd metabolic genotype. _J. Biol. Chem._ 274, 17410–17416

(1999). Article  CAS  PubMed  Google Scholar  * 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). Article  CAS  PubMed  PubMed Central  Google Scholar  * 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). Article  CAS  PubMed  Google Scholar  * Goryanin, I., Hodgman, T.C. & Selkov, E.

Mathematical simulation and analysis of cellular metabolism and regulation. _Bioinformatics_ 15, 749–758 (1999). Article  CAS  PubMed  Google Scholar  * Tomita, M. et al. E-CELL: software

environment for whole-cell simulation. _Bioinformatics_ 15, 72–84 (1999). Article  CAS  PubMed  Google Scholar  * Bailey, J.E. Mathematical modeling and analysis in biochemical engineering:

past accomplishments and future opportunities. _Biotechnol. Prog._ 14, 8–20 (1998). Article  CAS  PubMed  Google Scholar  * Fell, D. _Understanding the control of metabolism_. (Portland

Press, London; 1996). Google Scholar  * Domach, M.M., Leung, S.K., Cohn, R.E. & Shuler, M.M. Computer model for glucose-limited growth of a single cell of _Escherichia coli_ B/r-A.

_Biotechnol. Bioeng._ 26, 203–216 (1984). Article  CAS  PubMed  Google Scholar  * Palsson, B.O. & Lee, I.D. Model complexity has a significant effect on the numerical value and

interpretation of metabolic sensitivity coefficients. _J. Theor. Biol._ 161, 299–315 (1993). Article  CAS  PubMed  Google Scholar  * Barkai, N. & Leibler, S. Robustness in simple

biochemical networks. _Nature_ 387, 913–917 (1997). Article  CAS  PubMed  Google Scholar  * Liao, J.C. Modelling and analysis of metabolic pathways. _Curr. Opin. Biotechnol._ 4, 211–216

(1993). Article  CAS  PubMed  Google Scholar  * Novak, B. et al. Finishing the cell cycle. _J. Theor. Biol._ 199, 223–233 (1999). Article  CAS  PubMed  Google Scholar  * Kompala, D.S.,

Ramkrishna, D., Jansen, N.B. & Tsao, G.T. Investigation of bacterial growth on mixed substrates. Experimental evaluation of cybernetic models. _Biotechnol. Bioeng._ 28, 1044–1056 (1986).

Article  CAS  PubMed  Google Scholar  * McAdams, H.H. & Arkin, A. It's a noisy business! Genetic regulation at the nanomolar scale. _Trends Genet._ 15, 65–69 (1999). Article  CAS 

PubMed  Google Scholar  * Palsson, B.O., Joshi, A. & Ozturk, S.S. Reducing complexity in metabolic networks: making metabolic meshes manageable. _Fed. Proc._ 46, 2485–2489 (1987). CAS 

PubMed  Google Scholar  * Jamashidi, N., Edwards, J., Fahland, T., Church, G. & Palsson, B. A computer model of human red blood cell metabolism. _Bioinformatics_, in press (2001). * Lee,

I.D. & Palsson, B.O. A Macintosh software package for simulation of human red blood cell metabolism. _Comput. Methods Programs Biomed._ 38, 195–226 (1992). Article  CAS  PubMed  Google

Scholar  * Mulquiney, P.J., Bubb, W.A. & Kuchel, P.W. Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: in vivo kinetic

characterization of 2,3-bisphosphoglycerate synthase/phosphatase using 13C and 31P NMR. _Biochem. J._ 342, 567–580 (1999). Article  CAS  PubMed  PubMed Central  Google Scholar  * Fell, D.A.

& Small, J.A. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. _J. Biochem._ 238, 781–786 (1986). Article  CAS  Google Scholar  * Edwards, J.S.,

Ramakrishna, R., Schilling, C.H. & Palsson, B.O. In _Metabolic engineering_. (eds Lee, S.Y. & Papoutsakis, E.T.) 13–57 (Marcel Dekker, New York, NY; 1999). Google Scholar  *

Pramanik, J. & Keasling, J.D. Stoichiometric model of _Escherichia coli_ metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements.

_Biotechnol. Bioeng._ 56, 398–421 (1997). Article  CAS  PubMed  Google Scholar  * Sauer, U., Cameron, D.C. & Bailey, J.E. Metabolic capacity of _Bacillus subtilis_ for the production of

purine nucleosides, riboflavin, and folic acid. _Biotechnol. Bioeng._ 59, 227–238 (1998). Article  CAS  PubMed  Google Scholar  * Bonarius, H.P.J., Schmid, G. & Tramper, J. Flux analysis

of underdetermined metabolic networks: the quest for the missing constraints. _Trends Biotechnol._ 15, 308–314 (1997). Article  CAS  Google Scholar  * Varma, A. & Palsson, B.O.

Metabolic flux balancing: basic concepts, scientific and practical use. _Bio/Technology_ 12, 994–998 (1994). Article  CAS  Google Scholar  * Edwards, J.S. Functional genomics and the

computational analysis of bacterial metabolism (PhD Thesis). (Department of Bioengineering, University of California San Diego, La Jolla, CA; 1999). * Chvatal, V. _Linear programming_. (W.H.

Freeman, New York, NY; 1983). Google Scholar  * Varma, A. & Palsson, B.O. Metabolic capabilities of _Escherichia coli_: II. Optimal growth patterns. _J. Theor. Biol._ 165, 503–522

(1993). Article  CAS  Google Scholar  * 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, 249–83 (2000). Article  CAS  PubMed  Google Scholar  * Schilling, C.H., Schuster, S., Palsson, B.O. & Heinrich, R. Metabolic pathway analysis: basic concepts and

scientific applications in the post-genomic era. _Biotechnol. Prog._ 15, 296–303 (1999). Article  CAS  PubMed  Google Scholar  * Schilling, C.H., Edwards, J.S., Letscher, D. & Palsson,

B.O. Pathway analysis and flux balance analysis: a comprehensive study of metabolic systems. _Biotechnol. Bioeng._ in press (2001). * Sauer, U. et al. Metabolic flux ratio analysis of

genetic and environmental modulations of _Escherichia coli_ central carbon metabolism. _J. Bacteriol._ 181, 6679–6688 (1999). Article  CAS  PubMed  PubMed Central  Google Scholar  * Klapa,

M.I., Park, S.M., Sinskey, A.J. & Stephanopoulos, G. Metabolite and isotopomer balancing in the analysis of metabolic cycles: I. Theory. _Biotechnol. Bioeng._ 62, 375–391 (1999). Article

  CAS  PubMed  Google Scholar  * DeRisi, J.L., Iyer, V.R. & Brown, P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. _Science_ 278, 680–686 (1997).

Article  CAS  PubMed  Google Scholar  * Richmond, C.S., Glasner, J.D., Mau, R., Jin, H. & Blattner, F.R. Genome-wide expression profiling in _Escherichia coli_ K-12. _Nucleic Acids Res._

27, 3821–3835 (1999). Article  CAS  PubMed  PubMed Central  Google Scholar  * Hacia, J.G., Brody, L.C., Chee, M.S., Fodor, S.P.A. & Collins, F.S. Detection of heterozygous mutations in

BRCA1 using high-density oligonucleotide arrays and two-colour fluorescence analysis. _Nat. Genet._ 14, 441–447 (1996). Article  CAS  PubMed  Google Scholar  * Link, A.J., Robison, K. &

Church, G.M. Comparing the predicted and observed properties of proteins encoded in the genome of _Escherichia coli_ K-12. _Electrophoresis_ 18, 1259–1313 (1997). Article  CAS  PubMed 

Google Scholar  * Vanbogelen, R.A., Abshire, K.Z., Moldover, B., Olson, E.R. & Neidhardt, F.C. _Escherichia coli_ proteome analysis using the gene-protein database. _Electrophoresis_ 18,

1243–1251 (1997). Article  CAS  PubMed  Google Scholar  * Gygi, S.P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. _Nat. Biotechnol._ 17,

994–999 (1999). Article  CAS  PubMed  Google Scholar  * Schilling, C.H., Edwards, J.S. & Palsson, B.O. Towards metabolic phenomics: analysis of genomic data using flux balances.

_Biotechnol. Prog._ 15, 288–295 (1999). Article  CAS  PubMed  Google Scholar  * Neidhardt, F.C. (ed.) _Escherichia coli and Salmonella: cellular and molecular biology_. (ASM Press,

Washington, DC; 1996). Google Scholar  * Maniatis, T., Fritsch, E.F. & Sambrook, J. _Molecular cloning: a laboratory manual_. (Cold Spring Harbor Laboratory Press, Cold Spring Harbor,

NY; 1982). Google Scholar  * Neidhardt, F.C. & Umbarger, H.E. In _Escherichia coli and Salmonella: cellular and molecular biology_. (ed. Neidhardt, F.C.) 13–16 (ASM Press, Washington,

DC; 1996). Google Scholar  * Strang, G. _Linear algebra and its applications_. (Saunders, Fort Worth, TX; 1988). Google Scholar  * Varma, A., Boesch, B.W. & Palsson, B.O. Stoichiometric

interpretation of _Escherichia coli_ glucose catabolism under various oxygenation rates. _Appl. Environ. Microbiol._ 59, 2465–2473 (1993). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Covert, M.W. et al. Metabolic modeling of microbial strains _in silico_. _Trends Biochem. Sci._ in press (2001). Download references ACKNOWLEDGEMENTS This work was funded by the

NIH (GM57089) and the NSF (MCB 98-73384 and BES 98-14092). We would like to thank Christophe Schilling and George M. Church for insightful discussions during the preparation of this

manuscript, and Markus Covert and Iman Famili for technical assistance. AUTHOR INFORMATION Author notes * Jeremy S. Edwards Present address: Department of Chemical Engineering, University of

Delaware, Newark, DE, 19716 AUTHORS AND AFFILIATIONS * Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093-0412, CA Jeremy S. Edwards, 

Rafael U. Ibarra & Bernhard O. Palsson Authors * Jeremy S. Edwards View author publications You can also search for this author inPubMed Google Scholar * Rafael U. Ibarra View author

publications You can also search for this author inPubMed Google Scholar * Bernhard O. Palsson View author publications You can also search for this author inPubMed Google Scholar

CORRESPONDING AUTHOR Correspondence to Bernhard O. Palsson. SUPPLEMENTARY INFORMATION SUPPLEMENTARY TABLE 1 NOTE: The original Supplementary Table 1 (HTML format) has been replaced with an

Excel file. (XLS 145 kb) SUPPLEMENTARY TABLE 2 (HTM 109 KB) SUPPLEMENTARY TABLE 3 (HTM 37 KB) SUPPLEMENTARY TABLE 4 (HTM 31 KB) SUPPLEMENTARY TABLE 5 (HTM 55 KB) SUPPLEMENTARY APPENDIX 1

(ZIP 138 KB) RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Edwards, J., Ibarra, R. & Palsson, B. _In silico_ predictions of _Escherichia coli_

metabolic capabilities are consistent with experimental data. _Nat Biotechnol_ 19, 125–130 (2001). https://doi.org/10.1038/84379 Download citation * Received: 19 September 2000 * Accepted:

09 November 2000 * Issue Date: February 2001 * DOI: https://doi.org/10.1038/84379 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable

link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative