Mechanistic pathway modeling for industrial biotechnology: challenging but worthwhile
Abstract
Mechanistic (also called kinetic) models quantitatively describe dynamic and steady states of biochemical pathways. They are based on network structure (stoichiometry), regulatory information (enzyme inhibitors and activators) and the corresponding reaction kinetics. Although this approach to understand and predict the behavior of biochemical networks has now been in use for almost half a century, its experimental foundation has dramatically changed in the data-rich age of systems biology. Large mechanistic models, ranging up to the genome scale, are now being built and lots of data are available to validate and test them. From the broad scope of possible modeling applications, this survey focuses on the recent developments and central problems of metabolic network modeling in the field of bioprocess development for industrial biotechnology.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Yeast cells that were evolved for efficient galactose utilization subsequently showed trade-offs when grown on glucose as carbon source; this effect was due to antagonistic pleiotropy based on mutations of the regulatory protein RAS2.
“Yeast cells that were evolved for efficient galactose utilization [106] subsequently showed trade-offs when grown on glucose as carbon source. This effect was due to antagonistic pleiotropy that was based on mutations of the regulatory protein RAS2[162].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [162]
Cites
Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection
Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., Datsenko, K. A., Tomita, M., Wanner, B. L., & Mori, H. (2006) · Molecular Systems Biology · doi:10.1038/msb4100050 · Cited by 7,858 · see citing works
Automated design of synthetic ribosome binding sites to control protein expression
Salis, H. M., Mirsky, E. A., & Voigt, C. A. (2009) · Nature Biotechnology · doi:10.1038/nbt.1568 · Cited by 1,844 · see citing works
Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261 · see citing works
Teusink, B., Passarge, J., Reijenga, C. A., Esgalhado, E., van der Weijden, C. C., Schepper, M., Walsh, M. C., Bakker, B. M., van Dam, K., Westerhoff, H. V., et al. (2000) · European Journal of Biochemistry · doi:10.1046/j.1432-1327.2000.01527.x · Cited by 694 · see citing works
Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm
McGuffee, S. R., & Elcock, A. H. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000694 · Cited by 693 · see citing works
Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli
Schuetz, R. J., Kuepfer, L., & Sauer, U. (2007) · Molecular Systems Biology · doi:10.1038/msb4100162 · Cited by 684 · see citing works
Li, C., Donizelli, M., Rodriguez, N., Dharuri, H., Endler, L., Chelliah, V., Li, L., He, E., Henry, A., Stefan, M. I., et al. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-92 · Cited by 597 · see citing works
Dynamic modeling of the central carbon metabolism of Escherichia coli
Chassagnole, C., Noisommit‐Rizzi, N., Schmid, J. W., Mauch, K., & Reuß, M. (2002) · Biotechnology and Bioengineering · doi:10.1002/bit.10288 · Cited by 553 · see citing works
Gibon, Y., Blaesing, O. E., Hannemann, J., Carillo, P., Höhne, M., Hendriks, J. H. M., Palacios, N., Cross, J., Selbig, J., & Stitt, M. (2004) · The Plant Cell · doi:10.1105/tpc.104.025973 · Cited by 552 · see citing works
Bacterial adaptation through distributed sensing of metabolic fluxes
Kotte, O., Zaugg, J. B., & Heinemann, M. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.10 · Cited by 260 · see citing works
Systems Biology: Philosophical Foundations
Boogerd, F. C., Bruggeman, F. J., Hofmeyr, J. S., & Westerhoff, H. V. (2007) · Data Archiving and Networked Services (DANS) · Cited by 226 · see citing works
Comparing different ODE modelling approaches for gene regulatory networks
Polynikis, A., Hogan, S. J., & di Bernardo, M. (2009) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2009.07.040 · Cited by 201 · see citing works
Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems
Joshi, M., Seidel-Morgenstern, A., & Kremling, A. (2006) · Metabolic Engineering · doi:10.1016/j.ymben.2006.04.003 · Cited by 190 · see citing works
Thiele, I., Jamshidi, N., Fleming, R. M. T., & Palsson, B. Ø. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000312 · Cited by 182 · see citing works
Formulating genome‐scale kinetic models in the post‐genome era
Jamshidi, N., & Palsson, B. Ø. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.8 · Cited by 180 · see citing works
Measuring enzyme activities under standardized in vivo ‐like conditions for systems biology
van Eunen, K., Bouwman, J., Daran-Lapujade, P. A. S., Postmus, J., Canelas, A. B., Mensonides, F. I. C., Orij, R., Tüzün, I., van den Brink, J., Smits, G. J., et al. (2010) · FEBS Journal · doi:10.1111/j.1742-4658.2009.07524.x · Cited by 178 · see citing works
Towards a genome-scale kinetic model of cellular metabolism
B, K. S. A., Simeonidis, E., Swainston, N., & Mendes, P. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-6 · Cited by 171 · see citing works
Approximative kinetic formats used in metabolic network modeling
Heijnen, J. J. (2005) · Biotechnology and Bioengineering · doi:10.1002/bit.20558 · Cited by 169 · see citing works
A Quantitative Approach to Catabolite Repression in Escherichia coli
Bettenbrock, K., Fischer, S., Kremling, A., et al. (2005) · Journal of Biological Chemistry · doi:10.1074/jbc.m508090200 · Cited by 158 · see citing works
Chemical biophysics: quantitative analysis of cellular systems
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2643 · Cited by 148 · see citing works
Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation
Liebermeister, W., Uhlendorf, J., & Klipp, E. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq141 · Cited by 146 · see citing works
Sensitivity analysis for the reduction of complex metabolism models
Degenring, D., Froemel, C., Dikta, G., & Takors, R. (2004) · Journal of Process Control · doi:10.1016/j.jprocont.2003.12.008 · Cited by 133 · see citing works
Towards building the silicon cell: A modular approach
Snoep, J. L., Bruggeman, F. J., Olivier, B. G., & Westerhoff, H. V. (2005) · Biosystems · doi:10.1016/j.biosystems.2005.07.006 · Cited by 121 · see citing works
Whence cometh the allosterome?
Lindsley, J. E., & Rutter, J. P. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0604452103 · Cited by 113 · see citing works
Systems Biology and Biotechnology of Escherichia coli
Lee, S. Y. (2009) · Springer eBooks · doi:10.1007/978-1-4020-9394-4 · Cited by 98 · see citing works
Systems biology standards—the community speaks
Klipp, E., Liebermeister, W., Helbig, A., Kowald, A., & Schaber, J. (2007) · Nature Biotechnology · doi:10.1038/nbt0407-390 · Cited by 88 · see citing works
Young, J. D., Henne, K. L., Morgan, J. A., Konopka, A. E., & Ramkrishna, D. (2008) · Biotechnology and Bioengineering · doi:10.1002/bit.21780 · Cited by 84 · see citing works
Optimal experimental design with the sigma point method
Schenkendorf, R., Kremling, A., & Mangold, M. (2009) · IET Systems Biology · doi:10.1049/iet-syb:20080094 · Cited by 78 · see citing works
Lara, A. R., Taymaz‐Nikerel, H., Mashego, M. R., et al. (2009) · Biotechnology and Bioengineering · doi:10.1002/bit.22503 · Cited by 78 · see citing works
Dräger, A., Kronfeld, M., Ziller, M. J., Supper, J., Planatscher, H., Magnus, J. B., Oldiges, M., Kohlbacher, O., & Zell, A. (2009) · BMC Systems Biology · doi:10.1186/1752-0509-3-5 · Cited by 77 · see citing works
Network thermodynamics in the post-genomic era
Soh, K. C., & Vassily Hatzimanikatis, A. (2010) · Current Opinion in Microbiology · doi:10.1016/j.mib.2010.03.001 · Cited by 76 · see citing works
Engineering promoter regulation
Nevoigt, E., Fischer, C. R., Mucha, O., Matthäus, F., Ståhl, U., & Stephanopoulos, G. N. (2006) · Biotechnology and Bioengineering · doi:10.1002/bit.21129 · Cited by 74 · see citing works
Modeling of uncertainties in biochemical reactions
Mišković, L., & Vassily Hatzimanikatis, A. (2010) · Biotechnology and Bioengineering · doi:10.1002/bit.22932 · Cited by 74 · see citing works
Nikerel, I. E., van Winden, W. A., Verheijen, P. J. T., & Heijnen, J. J. (2009) · Metabolic Engineering · doi:10.1016/j.ymben.2008.07.004 · Cited by 71 · see citing works
Biochemical network models simplified by balanced truncation
Liebermeister, W., Baur, U., & Klipp, E. (2005) · FEBS Journal · doi:10.1111/j.1742-4658.2005.04780.x · Cited by 60 · see citing works
Dynamic modeling of Escherichia coli metabolic and regulatory systems for amino-acid production
Usuda, Y., Nishio, Y., Iwatani, S., Dien, S. J. V., Imaizumi, A., Shimbo, K., Kageyama, N., Iwahata, D., Miyano, H., & Matsui, K. (2010) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2010.02.018 · Cited by 59 · see citing works
13C labeling experiments at metabolic nonstationary conditions: An exploratory study
Wahl, A., Nöh, K., & Wiechert, W. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-152 · Cited by 59 · see citing works
Self-citationEnsemble Modeling for Aromatic Production in Escherichia coli
Rizk, M. L., & Liao, J. C. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006903 · Cited by 56 · see citing works
Douma, R. D., Verheijen, P. J. T., de Laat, W. T. A. M., Heijnen, J. J., & van Gulik, W. M. (2010) · Biotechnology and Bioengineering · doi:10.1002/bit.22689 · Cited by 55 · see citing works
Nıkerel, E., van Winden, W. A., van Gulik, W. M., et al. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-540 · Cited by 47 · see citing works
Data integration and analysis of biological networks
Kim, T. Y., Kim, H. U., & Lee, S. Y. (2010) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.01.003 · Cited by 46 · see citing works
Schaub, J., & Reuß, M. (2008) · Biotechnology Progress · doi:10.1002/btpr.59 · Cited by 44 · see citing works
An optimized algorithm for flux estimation from isotopomer distribution in glucose metabolites
Selivanov, V. A., Puigjaner, J., Sillero, A., et al. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth412 · Cited by 42 · see citing works
Nikolaev, E. V. (2009) · Metabolic Engineering · doi:10.1016/j.ymben.2009.08.010 · Cited by 37 · see citing works
Mechanistic kinetic model for symmetric carboligations using benzaldehyde lyase
Zavrel, M., Schmidt, T., Michalik, C., et al. (2008) · Biotechnology and Bioengineering · doi:10.1002/bit.21867 · Cited by 35 · see citing works
Wahl, A., Haunschild, M. D., Oldiges, M., & Wiechert, W. (2006) · Systems Biology · doi:10.1049/ip-syb:20050089 · Cited by 29 · see citing works
Self-citationTranslating biochemical network models between different kinetic formats
Hadlich, F., Noack, S., & Wiechert, W. (2008) · Metabolic Engineering · doi:10.1016/j.ymben.2008.10.002 · Cited by 29 · see citing works
Self-citationGENERIC ENZYMATIC RATE EQUATION UNDER LIVING CONDITIONS
Lee, L. W., Yin, L., Zhu, X., et al. (2007) · Journal of Biological Systems · doi:10.1142/s0218339007002295 · Cited by 27 · see citing works
Ederer, M., & Gilles, E. D. (2008) · Engineering in Life Sciences · doi:10.1002/elsc.200800040 · Cited by 25 · see citing works
Multi-Omics Data-Driven Systems Biology of E. coli
Ishii, N., & Tomita, M. (2009) · Springer eBooks · doi:10.1007/978-1-4020-9394-4_3 · Cited by 20 · see citing works
Hardiman, T., Meinhold, H., Hofmann, J. F.-X., Ewald, J. C., Siemann‐Herzberg, M., & Reuß, M. (2009) · Metabolic Engineering · doi:10.1016/j.ymben.2009.10.006 · Cited by 16 · see citing works
Automated piecewise power-law modeling of biological systems
Machina, A., Ponosov, A., & Voit, E. O. (2010) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2009.12.016 · Cited by 16 · see citing works
Visualizing regulatory interactions in metabolic networks
Noack, S., Wahl, A., Qeli, E., et al. (2007) · BMC Biology · doi:10.1186/1741-7007-5-46 · Cited by 16 · see citing works
Self-citationData and model integration using JWS Online.
van Gend, C. H. D. R., Conradie, R., du Preez, F. B., & Snoep, J. L. (2007) · PubMed · Cited by 13 · see citing works
Modeling Languages for Biochemical Network Simulation: Reaction vs Equation Based Approaches
Wiechert, W., Noack, S., & Elsheikh, A. (2009) · Springer eBooks · doi:10.1007/10_2009_64 · Cited by 10 · see citing works
Self-citationVertical systems biology: from DNA to flux and back.
Bevilacqua, A., Wilkinson, S. J., Dimelow, R. J., et al. (2008) · PubMed · doi:10.4324/9780203967195-6 · Cited by 10 · see citing works
Visualizing regulatory interactions in metabolic networks
Noack, S., Wahl, A., Oldiges, M., et al. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-s1-p5 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Adaptive laboratory evolution—Principles and applications for biotechnology
Dragosits, M., & Mattanovich, D. (2013) · Microbial Cell Factories · doi:10.1186/1475-2859-12-64 · Cited by 827 · see citing works
- Authors
- Wiechert, W., & Noack, S.
- Affiliations
- Forschungszentrum Jülich (DE)
- Corresponding author
- Wiechert, Wolfgang
- Venue
- Current Opinion in Biotechnology
- Publisher
- Elsevier BV
- Published
- 2011-10
- Volume
- 22
- Issue
- 5
- Pages
- 604-610
- ISSN
- 0958-1669 (ISSN-L)
- OpenAlex
- W2134280223
- Cited by
- 46
- References
- 57
- Citations by year
- 2026: 1 · 2024: 2 · 2022: 1 · 2021: 3 · 2020: 2 · 2019: 4 · 2018: 3 · 2017: 4 · 2016: 3 · 2015: 4 · 2014: 6 · 2013: 6 · 2012: 6
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- JuSER (Forschungszentrum Jülich) (submittedVersion)
- Funders
- European Commission
- 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
- mechanistic kinetic model, metabolic network modeling, genome-scale metabolic models, enzyme kinetics, enzyme regulation, stoichiometry, model validation
- MeSH terms
- Biotechnology, Enzymes, Gene Expression Regulation, Kinetics, Models, Biological, Systems Biology, Metabolic Networks and Pathways
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
