Representing dynamic biological networks with multi-scale probabilistic models
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Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales.
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Although the Wnt signaling pathway has also been used to illustrate features of rule-based modeling [95, 96], only few simulation models have been developed based on a rule-based approach.
“Although the Wnt signaling pathway has also been used to illustrate features of rule-based modeling [95, 96], only few simulation models have been developed based on a rule-based approach.”
From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [96]
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Cites
(1988) · Cited by 16,582 · see citing works
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855 · see citing works
Alberts, B., Johnson, A., Lewis, J. H., Morgan, D. O., RAFF, M. C., Roberts, K. J., & Walter, P. (2017) · W.W. Norton eBooks · doi:10.1201/9781315735368 · Cited by 7,937 · see citing works
Clarke, E. M., Grümberg, O., & Long, D. (1996) · Cited by 6,877 · see citing works
Newman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595 · see citing works
Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101065j · Cited by 5,349 · see citing works
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517384113 · Cited by 4,796 · see citing works
Wnt/β-Catenin Signaling, Disease, and Emerging Therapeutic Modalities
Nusse, R., & Clevers, H. (2017) · Cell · doi:10.1016/j.cell.2017.05.016 · Cited by 4,342 · see citing works
Network motifs: theory and experimental approaches
Alon, U. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2102 · Cited by 3,279 · see citing works
An Introduction to Systems Biology: Design Principles of Biological Circuits
Alon, U. (2006) · doi:10.1201/9781420011432 · Cited by 2,825 · see citing works
The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases
Orchard, S. E., Ammari, M. G., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F. C., Campbell, N. H., Chavali, G. B., Chen, C. C. L., del‐Toro, N., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1115 · Cited by 1,835 · see citing works
Introduction to Statistical Relational Learning
(2007) · doi:10.7551/mitpress/7432.001.0001 · Cited by 1,643 · see citing works
Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
Ilya Shmulevich, C., Dougherty, E. R., Kim, S., & Zhang, W. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.2.261 · Cited by 1,609 · see citing works
A Whole-Cell Computational Model Predicts Phenotype from Genotype
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B., Assad-Garcia, N., Glass, J. I., & Covert, M. W. (2012) · Cell · doi:10.1016/j.cell.2012.05.044 · Cited by 1,300 · see citing works
Prolog programming for artificial intelligence
(1986) · Computer-Aided Design · doi:10.1016/0010-4485(86)90018-7 · Cited by 1,080 · see citing works
Wnt Signaling through Inhibition of β-Catenin Degradation in an Intact Axin1 Complex
Li, V. S. W., Ng, S. S., Boersema, P. J., Low, T. Y., Karthaus, W. R., Gerlach, J. P., Mohammed, S., Heck, A. J. R., Maurice, M. M., Mahmoudi, T., et al. (2012) · Cell · doi:10.1016/j.cell.2012.05.002 · Cited by 889 · see citing works
Network propagation: a universal amplifier of genetic associations
Cowen, L., Ideker, T. E., Raphael, B. J., & Sharan, R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.38 · Cited by 666 · see citing works
Deisboeck, T. S., Wang, Z., Macklin, P., & Vittorio Cristini, C. (2011) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071910-124729 · Cited by 462 · see citing works
Classical deterministic complexity of Edmonds' Problem and quantum entanglement
Gurvits, L. (2003) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/780542.780545 · Cited by 459 · see citing works
Rule-Based Modeling of Biochemical Systems with BioNetGen
Faeder, J. R., Blinov, M. L., & Hlavacek, W. S. (2009) · Methods in molecular biology · doi:10.1007/978-1-59745-525-1_5 · Cited by 432 · see citing works
The Incoherent Feedforward Loop Can Provide Fold-Change Detection in Gene Regulation
Goentoro, L., Shoval, O., Kirschner, M. W., & Alon, U. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.11.018 · Cited by 424 · see citing works
Quantitative and logic modelling of molecular and gene networks
Novère, N. G. L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3885 · Cited by 422 · see citing works
Wielemaker, J., Schrijvers, T., Triska, M., & Lager, T. (2011) · Theory and Practice of Logic Programming · doi:10.1017/s1471068411000494 · Cited by 387 · see citing works
Loh, K. M., van Amerongen, R., & Nusse, R. (2016) · Developmental Cell · doi:10.1016/j.devcel.2016.08.011 · Cited by 335 · see citing works
The dynamic control of signal transduction networks in cancer cells
Kölch, W., Halász, M., Granovskaya, M., & Kholodenko, B. Ν. (2015) · Nature reviews. Cancer · doi:10.1038/nrc3983 · Cited by 332 · see citing works
Evidence that Fold-Change, and Not Absolute Level, of β-Catenin Dictates Wnt Signaling
Goentoro, L., & Kirschner, M. W. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.11.017 · Cited by 300 · see citing works
BioModels: ten-year anniversary
Chelliah, V., Juty, N., Ajmera, I., Ali, R., Dumousseau, M., Glont, M., Hucka, M., Jalowicki, G., Keating, S., Knight-Schrijver, V., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1181 · Cited by 281 · see citing works
Wnt Stabilization of β-Catenin Reveals Principles for Morphogen Receptor-Scaffold Assemblies
Kim, S., Huang, H., Zhao, M., Zhang, X., Zhang, A., Semonov, M. V., MacDonald, B. T., Zhang, X., Abreu, J. G., Peng, L., et al. (2013) · Science · doi:10.1126/science.1232389 · Cited by 230 · see citing works
Logical Modeling and Dynamical Analysis of Cellular Networks
Abou-Jaoudé, W., Traynard, P., Monteiro, P. T., Sáez-Rodríguez, J., Helikar, T., Thieffry, D., & Chaouiya, C. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00094 · Cited by 228 · see citing works
Vittorio Cristini, C., & Lowengrub, J. S. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511781452 · Cited by 216 · see citing works
BioNetGen 2.2: advances in rule-based modeling
Harris, L. A., Hogg, J., Tapia, J.-J., Sekar, J. A. P., Gupta, S., Korsunsky, I., Arora, A., Barua, D., Sheehan, R. P., & Faeder, J. R. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw469 · Cited by 211 · see citing works
From individual Wnt pathways towards a Wnt signalling network
Kestler, H. A., & Kühl, M. (2008) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2251 · Cited by 188 · see citing works
Self-citationMultiscale modeling of cancer: an integrated experimental and mathematical modeling approach
Vittorio Cristini, C., & Lowengrub, J. S. (2010) · Discovery Research Portal (University of Dundee) · Cited by 185 · see citing works
In silico prediction of physical protein interactions and characterization of interactome orphans
Kotlyar, M., Pastrello, C., Pivetta, F., Sardo, A. L., Cumbaa, C. A., Li, H., Naranian, T., Niu, Y., Ding, Z., Vafaee, F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.3178 · Cited by 160 · see citing works
Kinetic Responses of β-Catenin Specify the Sites of Wnt Control
Hernandez, A., Klein, A. M., & Kirschner, M. W. (2012) · Science · doi:10.1126/science.1228734 · Cited by 134 · see citing works
Continuous time boolean modeling for biological signaling: application of Gillespie algorithm
Stoll, G., Viara, E., Barillot, E., & Calzone, L. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-116 · Cited by 134 · see citing works
MaBoSS 2.0: an environment for stochastic Boolean modeling
Stoll, G., Caron, B., Viara, E., Dugourd, A. J. F., Zinovyev, A. Y., Naldi, A., Kroemer, G., Barillot, E., & Calzone, L. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx123 · Cited by 133 · see citing works
Kempf, H., Olmer, R., Haase, A., Franke, A., Bolesani, E., Schwanke, K., Diaz, D. R., Coffee, M., Göhring, G., Dräger, G., et al. (2016) · Nature Communications · doi:10.1038/ncomms13602 · Cited by 128 · see citing works
Self-citationOn the Implementation of the Probabilistic Logic Programming Language ProbLog
Kimmig, A., Demoen, B., Raedt, L. D., Costa, V. S., & Rocha, R. (2010) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 126 · see citing works
Hino, S.-I., Michiue, T., Asashima, M., & Kikuchi, A. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m213265200 · Cited by 104 · see citing works
On the implementation of the probabilistic logic programming language ProbLog
KIMMIG, A., DEMOEN, B., RAEDT, L. D., COSTA, V. S., & ROCHA, R. (2011) · Theory and Practice of Logic Programming · doi:10.1017/s1471068410000566 · Cited by 102 · see citing works
Cooperative development of logical modelling standards and tools with CoLoMoTo
Naldi, A., Monteiro, P. T., Müssel, C., Kestler, H. A., Thieffry, D., Xénarios, I., Sáez-Rodríguez, J., Helikar, T., & Chaouiya, C. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv013 · Cited by 93 · see citing works
Self-citationNetwork modeling of signal transduction: establishing the global view
Kestler, H. A., Wawra, C., Kracher, B., & Kühl, M. (2008) · BioEssays · doi:10.1002/bies.20834 · Cited by 84 · see citing works
Self-citationStochastic Boolean networks: An efficient approach to modeling gene regulatory networks
Liang, J., & Han, J. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-113 · Cited by 81 · see citing works
Powathil, G., Swat, M., & Chaplain, M. A. J. (2014) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2014.02.003 · Cited by 80 · see citing works
Extended analyses of the Wnt/β-catenin pathway: Robustness and oscillatory behaviour
Wawra, C., Kühl, M., & Kestler, H. A. (2007) · FEBS Letters · doi:10.1016/j.febslet.2007.07.043 · Cited by 56 · see citing works
Self-citationWallach, T. E., Schellenberg, K., Maier, B., Kalathur, R. K. R., Porras, P., Wanker, E. E., Futschik, M. E., & Kramer, A. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003398 · Cited by 55 · see citing works
Sinnberg, T. W., Makino, E., Krueger, M. A., Velić, A., Maček, B., Rothbauer, U., Groll, N., Poetz, O., Czemmel, S., Niessner, H., et al. (2016) · EBioMedicine · doi:10.1016/j.ebiom.2016.04.037 · Cited by 54 · see citing works
Rule-Based Cell Systems Model of Aging using Feedback Loop Motifs Mediated by Stress Responses
Kriete, A., Bosl, W. J., & Booker, G. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000820 · Cited by 53 · see citing works
Fletcher, A. G., Mirams, G. R., Murray, P. J., Walter, A., Kang, J. W., Cho, K. H., Maini, P. K., & Byrne, H. M. (2010) · CRC Press eBooks · doi:10.1201/b10407 · Cited by 49 · see citing works
Inferring Boolean network structure via correlation
Maucher, M., Kracher, B., Kühl, M., & Kestler, H. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr166 · Cited by 47 · see citing works
Self-citationCompartmental and Spatial Rule-Based Modeling with Virtual Cell
Blinov, M. L., Schaff, J. C., Vasilescu, D. D., Moraru, I. I., Bloom, J. E., & Loew, L. M. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.08.022 · Cited by 47 · see citing works
Rule-Based Modeling of Signal Transduction: A Primer
Sekar, J. A. P., & Faeder, J. R. (2012) · Methods in molecular biology · doi:10.1007/978-1-61779-833-7_9 · Cited by 35 · see citing works
Stochastic relational processes: Efficient inference and applications
Thon, I., Landwehr, N., & Raedt, L. D. (2010) · Machine Learning · doi:10.1007/s10994-010-5213-8 · Cited by 31 · see citing works
Automatic Screening for Perturbations in Boolean Networks
Schwab, J. D., & Kestler, H. A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00431 · Cited by 31 · see citing works
Self-citationChemical reaction network designs for asynchronous logic circuits
Cardelli, L., Kwiatkowska, M., & Whitby, M. (2017) · Natural Computing · doi:10.1007/s11047-017-9665-7 · Cited by 27 · see citing works
Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty
Grieb, M., Burkovski, A., Sträng, E., Kraus, J. M., Groß, A., Palm, G., Kühl, M., & Kestler, H. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131832 · Cited by 25 · see citing works
Self-citationViSiBooL—visualization and simulation of Boolean networks with temporal constraints
Schwab, J. D., Burkovski, A., Siegle, L., Müssel, C., & Kestler, H. A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw661 · Cited by 25 · see citing works
Self-citationMohr, K. B., Zirafi, O., Hennies, M., Wiese, S., Kirchhoff, F., & Münch, J. (2015) · Analytical Biochemistry · doi:10.1016/j.ab.2015.01.023 · Cited by 24 · see citing works
Self-citationLuckert, K., Götschel, F., Sorger, P. K., Hecht, A., Joos, T. O., & Poetz, O. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.007377 · Cited by 23 · see citing works
Self-citationVerlingue, L., Dugourd, A. J. F., Stoll, G., Barillot, E., Calzone, L., & Londoño‐Vallejo, A. (2016) · Aging Cell · doi:10.1111/acel.12504 · Cited by 23 · see citing works
Computational Modeling of Signaling Networks
(2012) · Methods in molecular biology · doi:10.1007/978-1-61779-833-7 · Cited by 23 · see citing works
Luckert, K., Gujral, T. S., Chan, M., Sevecka, M., Joos, T. O., Sorger, P. K., MacBeath, G., & Poetz, O. (2012) · Science Signaling · doi:10.1126/scisignal.2002372 · Cited by 23 · see citing works
Self-citationDynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling
Fröhlich, H., Valderrama, G., Götschel, F., & Korf, U. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142646 · Cited by 20 · see citing works
A proof theoretic view of spatial and temporal dependencies in biochemical systems
Olarte, C. A., Chiarugi, D., Falaschi, M., & Hermith, D. (2016) · Theoretical Computer Science · doi:10.1016/j.tcs.2016.03.029 · Cited by 15 · see citing works
Verification of Spatial and Temporal Modalities in Biochemical Systems
Chiarugi, D., Falaschi, M., Hermith, D., & Olarte, C. A. (2015) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2015.06.009 · Cited by 7 · see citing works
SPATKIN: a simulator for rule-based modeling of biomolecular site dynamics on surfaces
Kochańczyk, M., Hlavacek, W. S., & Lipniacki, T. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx456 · Cited by 4 · see citing works
Bead-Based Peptide Arrays for Profiling the Specificity of Modification State-Specific Antibodies
Filomena, A., Beiter, Y., Templin, M. F., Joos, T. O., Schneiderhan‐Marra, N., & Poetz, O. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2999-3_22 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
1 of the 69 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- [12]Sauro, H. M. in Computational Systems Biology (eds. Ireton, R., Montgomery, K., Bumgarner, R., Samudrala, R. & McDermott, J.) vol. 541, pp. 269–309 (Humana Press, New York, NY, USA, 2009).
- [19]Fages, F. & Soliman, S. in Probabilistic Inductive Logic Programming (eds. De Raedt, L., Frasconi, P., Kersting, K. & Muggleton, S.) vol. 4911, pp. 287–304 (Springer, Berlin Heidelberg, 2008).
- [46]Newman, M. E. J. Networks: an introduction. (Oxford University Press, Oxford, UK, 2010).
- [55]Filomena, A. et al. in Peptide Antibodies (ed. Houen, G.) vol. 1348, pp. 251–265 (Springer, New York, NY, USA, 2015).
- [59]Faeder, J. R., Blinov, M. L. & Hlavacek, W. S. in Systems Biology (ed. Maly, I. V.) vol. 500, pp. 113–167 (Humana Press, New York, NY, USA, 2009).
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Armacki, M., Polaschek, S., Waldenmaier, M., Morawe, M., Ruhland, C., Schmid, R., Lechel, A., Tharehalli, U., Steup, C., Bektas, Y., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.05.052 · Cited by 73 · see citing works
Self-citationWist, M., Meier, L., Gutman, O., Haas, J., Endres, S., Zhou, Y., Rösler, R., Wiese, S., Stilgenbauer, S., Hobeika, E., et al. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.011946 · Cited by 34 · see citing works
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Rimban, E. L. (2025) · Preprints.org · doi:10.20944/preprints202504.2388.v1
Missaghimamaghani, O., Nehri, L. N., Bakhshi, S., Karaosmanoğlu, O., Sivas, H., Acar, A. C., & Banerjee, S. (2025) · Tissue Barriers · doi:10.1080/21688370.2025.2611205
- Authors
- Groß, A., Kracher, B., Kraus, J. M., Kühlwein, S. D., Pfister, A. S., Wiese, S., Luckert, K., Pötz, O., Joos, T., Van Daele, D., De Raedt, L., Kühl, M., & Kestler, H. A.
- Affiliations
- Universität Ulm (DE)
- Natural and Medical Sciences Institute (DE)
- Corresponding author
- Kühl, Michael
- Kestler, Hans A.
- ORCID
- Venue
- Communications Biology
- Published
- 2019-01-17
- Volume
- 2
- Issue
- 1
- Pages
- 21-21
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC6336720
- OpenAlex
- W2909957393
- Cited by
- 32
- References
- 69
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 1 · 2022: 2 · 2021: 7 · 2020: 9 · 2019: 3
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- EC | Seventh Framework Programme (602783)
- Bundesministerium für Bildung und Forschung (0315894A, 01ZX1407A, 01ZX1708C)
- Topics
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- signaling networks, Wnt signaling, network motifs, Boolean networks, Bayesian networks, differential equations, feedback loops, model robustness, gene expression regulation, metabolic networks, systems biology modeling
- MeSH terms
- Bayes Theorem, Feedback, Humans, Models, Biological, Phosphorylation, Transfection, Models, Statistical, Signal Transduction, Systems Biology, beta Catenin, Gene Regulatory Networks, Gene Knockdown Techniques, HEK293 Cells, Wnt Signaling Pathway
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