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Stochastic fluctuations in metabolic pathways
Open accessAbstract
Fluctuations in the abundance of molecules in the living cell may affect its growth and well being. For regulatory molecules (e.g., signaling proteins or transcription factors), fluctuations in their expression can affect the levels of downstream targets in a network. Here, we develop an analytic framework to investigate the phenomenon of noise correlation in molecular networks. Specifically, we focus on the metabolic network, which is highly interlinked, and noise properties may constrain its structure and function. Motivated by the analogy between the dynamics of a linear metabolic pathway and that of the exactly soluble linear queuing network or, alternatively, a mass transfer system, we derive a plethora of results concerning fluctuations in the abundance of intermediate metabolites in various common motifs of the metabolic network. For all but one case examined, we find the steady-state fluctuation in different nodes of the pathways to be effectively uncorrelated. Consequently, fluctuations in enzyme levels only affect local properties and do not propagate elsewhere into metabolic networks, and intermediate metabolites can be freely shared by different reactions. Our approach may be applicable to study metabolic networks with more complex topologies or protein signaling networks that are governed by similar biochemical reactions. Possible implications for bioinformatic analysis of metabolomic data are discussed.
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“Actually, stochasticity rules all living cells through fluctuations in gene expression, molecular interactions, and metabolic fluxes, even systems governed by deterministic laws, such as monogenic genetics [131,132,133,134].”
From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [134]
In the citing paper
“Beyond identifiable causes, a large part of phenotype variability across patients with the same ABCD1 genotype may “simply” be due to stochasticity, a factor hitherto considered “noise” by Cartesian researchers.”
From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [134]
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Citations
Cites
(1972) · Cited by 17,806
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 10,215
Escherichia coli and Salmonella :cellular and molecular biology
Neidhardt, F. C. (1996) · ASM Press eBooks · Cited by 8,197
Ross, S. M. (1982) · Cited by 3,820
House, D., & Keyser, J. C. (2016) · A K Peters/CRC Press eBooks · doi:10.1201/9781315373140-16 · Cited by 3,332
Liggett, T. M. (1985) · Grundlehren der mathematischen Wissenschaften · doi:10.1007/978-1-4613-8542-4 · Cited by 2,935
Stochasticity in gene expression: from theories to phenotypes
Kærn, M., Elston, T. C., Blake, W. J., & Collins, J. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1615 · Cited by 2,318
Stochastic mechanisms in gene expression.
McAdams, H., & Arkin, A. (1997) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.94.3.814 · Cited by 1,970
Noise in Gene Expression: Origins, Consequences, and Control
Raser, J. M., & O’Shea, E. K. (2005) · Science · doi:10.1126/science.1105891 · Cited by 1,756
Intrinsic and extrinsic contributions to stochasticity in gene expression
Swain, P. S., Elowitz, M. B., & Siggia, E. D. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.162041399 · Cited by 1,661
Escherichia coli and Salmonella.
Neidhart, F. C. (1996) · Cited by 1,577
Real-Time Kinetics of Gene Activity in Individual Bacteria
Golding, I., Paulsson, J., Zawilski, S. M., & Cox, E. C. (2005) · Cell · doi:10.1016/j.cell.2005.09.031 · Cited by 1,468
Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments
Kussell, E. L., & Leibler, S. (2005) · Science · doi:10.1126/science.1114383 · Cited by 1,357
Large-distance and long-time properties of a randomly stirred fluid
Forster, D., Nelson, D. R., & Stephen, M. J. (1977) · Physical Review A · doi:10.1103/physreva.16.732 · Cited by 1,355
An Introduction to Stochastic Modeling
Frey, M. R., Taylor, H. M., & Karlin, S. (1994) · Technometrics · doi:10.2307/1269970 · Cited by 1,278
Summing up the noise in gene networks
Paulsson, J. (2004) · Nature · doi:10.1038/nature02257 · Cited by 1,259
Understanding the control of metabolism
(1997) · Choice Reviews Online · doi:10.5860/choice.34-6252 · Cited by 1,230
Jackson, J. R. (1957) · Operations Research · doi:10.1287/opre.5.4.518 · Cited by 1,216
Interaction of Markov processes
Spitzer, F. (1970) · Advances in Mathematics · doi:10.1016/0001-8708(70)90034-4 · Cited by 962
Noise Propagation in Gene Networks
Pedraza, J. M., & van Oudenaarden, A. (2005) · Science · doi:10.1126/science.1109090 · Cited by 796
Ever-fluctuating single enzyme molecules: Michaelis-Menten equation revisited
English, B. P., Min, W., van Oijen, A. M., Lee, K. T., Luo, G., Sun, H., Cherayil, B. J., Kou, S. C., & Xie, X. S. (2005) · Nature Chemical Biology · doi:10.1038/nchembio759 · Cited by 790
An excitable gene regulatory circuit induces transient cellular differentiation
Süel, G. M., García‐Ojalvo, J., Liberman, L. M., & Elowitz, M. B. (2006) · Nature · doi:10.1038/nature04588 · Cited by 714
Nonequilibrium statistical mechanics of the zero-range process and related models
Evans, M. R., & Hanney, T. (2005) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/38/19/r01 · Cited by 567
Interaction of Markov Processes
Spitzer, F. (1991) · Birkhäuser Boston eBooks · doi:10.1007/978-1-4612-0459-6_5 · Cited by 525
Ultrasensitivity and noise propagation in a synthetic transcriptional cascade
Hooshangi, S., Thiberge, S. Y., & Weiss, R. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408507102 · Cited by 489
Regulated cell-to-cell variation in a cell-fate decision system
Colman‐Lerner, A., Gordon, A. H., Serra, E., Chin, T., Resnekov, O., Endy, D., Pesce, C. G., & Brent, R. (2005) · Nature · doi:10.1038/nature03998 · Cited by 428
Nonequilibrium statistical mechanics of the zero-range process and related modelsPreprint
Evans, M. R., & Hanney, T. (2005) · Cited by 395
Metabolic modelling of microbes: the flux‐balance approach
Edwards, J. S., Covert, M., & Palsson, B. (2002) · Environmental Microbiology · doi:10.1046/j.1462-2920.2002.00282.x · Cited by 365
Single-Molecule Michaelis−Menten Equations
Kou, S. C., Cherayil, B. J., Min, W., English, B. P., & Xie, X. S. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp051490q · Cited by 348
Observing and interpreting correlations in metabolomic networks
Steuer, R., Kurths, J., Fiehn, O., & Weckwerth, W. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg120 · Cited by 313
Stochastic gene expression as a many-body problem
Sasai, M., & Wolynes, P. G. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2627987100 · Cited by 296
A test case of correlation metric construction of a reaction pathway from measurements
Arkin, A., Shen, P., & Ross, J. (1997) · Science · doi:10.1126/science.277.5330.1275 · Cited by 291
Gene network shaping of inherent noise spectra
Austin, D. W., Allen, M. S., McCollum, J. M., Dar, R. D., Wilgus, J. R., Sayler, G. S., Samatova, N. F., Cox, C. D., & Simpson, M. L. (2006) · Nature · doi:10.1038/nature04194 · Cited by 288
Xie, X. S., & Lu, H. P. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.23.15967 · Cited by 266
Attenuation of Noise in Ultrasensitive Signaling Cascades
Thattai, M., & van Oudenaarden, A. (2002) · Biophysical Journal · doi:10.1016/s0006-3495(02)75635-x · Cited by 229
The branch point effect. Ultrasensitivity and subsensitivity to metabolic control.
LaPorte, D. C., Walsh, K., & Koshland, D. E. (1984) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)89857-x · Cited by 180
Can we discover novel pathways using metabolomic analysis?
Weckwerth, W., & Fiehn, O. (2002) · Current Opinion in Biotechnology · doi:10.1016/s0958-1669(02)00299-9 · Cited by 171
Noisy signal amplification in ultrasensitive signal transduction
Shibata, T., & Fujimoto, K. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403350102 · Cited by 160
Molecular Communication through Stochastic Synchronization Induced by Extracellular Fluctuations
Zhou, T., Chen, L., & Aihara, K. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.178103 · Cited by 156
Statistical construction of chemical reaction mechanisms from measured time-series
Arkin, A., & Ross, J. (1995) · The Journal of Physical Chemistry · doi:10.1021/j100003a020 · Cited by 143
Tănase‐Nicola, S., Warren, P. B., & ten Wolde, P. R. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.068102 · Cited by 142
Zero-Range Process with Open Boundaries
Levine, E., Mukamel, D., & Schutz, G. M. (2005) · Journal of Statistical Physics · doi:10.1007/s10955-005-7000-7 · Cited by 110
Self-citationDetermination of causal connectivities of species in reaction networks
Vance, W. N., Arkin, A. P., & Ross, J. R. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.022049699 · Cited by 106
Near-Critical Phenomena in Intracellular Metabolite Pools
Elf, J., Paulsson, J., Berg, O. G., & Ehrenberg, M. (2003) · Biophysical Journal · doi:10.1016/s0006-3495(03)74839-5 · Cited by 103
An introduction to stochastic modeling
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5510 · Cited by 99
Factorized steady states in mass transport models
Evans, M. R., Majumdar, S. N., & Zia, R. K. P. (2004) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/37/25/l02 · Cited by 98
The effect of negative feedback on noise propagation in transcriptional gene networks
Hooshangi, S., & Weiss, R. (2006) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2208927 · Cited by 86
Product measure steady states of generalized zero range processes
Greenblatt, R. L., & Lebowitz, J. L. (2006) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/39/7/003 · Cited by 19
Hayashi, K. (1957) · Pharmaceutical Bulletin · doi:10.1248/cpb1953.5.518 · Cited by 3
Eisenstein, M. S. (2005) · Nature Methods · doi:10.1038/nmeth0205-87 · Cited by 2
Research works printed but not linked: 3
- G Michal Biochemical Pathways (Wiley, New York, 1999).
- JM Berg, JL Tymoczko, L Stryer Biochemistry (Freeman, 6th Ed, New York, 2006).
- DC LaPorte, K Walsh, DE Koshland J Biochem 259, 14068–14075 (1984).
Cited by
Sorted by citations
Tsimring, L. S. (2014) · Reports on Progress in Physics · doi:10.1088/0034-4885/77/2/026601 · Cited by 455
Stochasticity of metabolism and growth at the single-cell level
Kiviet, D. J., Nghe, P., Walker, N., Boulineau, S., Šunderlíková, V., & Tans, S. J. (2014) · Nature · doi:10.1038/nature13582 · Cited by 419
Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis
Xiao, Y., Bowen, C. H., Liu, D., & Zhang, F. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2046 · Cited by 237
Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks
Anderson, D. F., Crăciun, G., & Kurtz, T. G. (2010) · Bulletin of Mathematical Biology · doi:10.1007/s11538-010-9517-4 · Cited by 220
From the stochasticity of molecular processes to the variability of synaptic transmission
Ribrault, C., Sekimoto, K., & Triller, A. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3025 · Cited by 198
Metadata
- Authors
- Levine, E., & Hwa, T.
- Affiliations
- University of California San Diego (US)
- Center for Theoretical Biological Physics and Department of Physics, University of California at San Diego, La Jolla, CA 92093-0374
- ORCID
- Levine, Erel
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2007-05-29
- Volume
- 104
- Issue
- 22
- Pages
- 9224-9229
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC1890476
- OpenAlex
- W1979682650
- Cited by
- 132
- References
- 51
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 7 · 2023: 7 · 2022: 6 · 2021: 7 · 2020: 9 · 2019: 5 · 2018: 6 · 2017: 8 · 2016: 9 · 2015: 4 · 2014: 10 · 2013: 4 · 2012: 10
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0211308, 0225630, 0216576)
- Division of Materials Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Keywords
- stochastic fluctuations, metabolic networks, noise correlation, metabolic intermediates, enzyme levels, steady-state fluctuations, metabolomics, protein signaling networks
- MeSH terms
- Probability, Proteins, Substrate Specificity, Metabolic Networks and Pathways
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