Spatio-temporal Model of Endogenous ROS and Raft-Dependent WNT/Beta-Catenin Signaling Driving Cell Fate Commitment in Human Neural Progenitor Cells
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Canonical WNT/β-catenin signaling is a central pathway in embryonic development, but it is also connected to a number of cancers and developmental disorders. Here we apply a combined in-vitro and in-silico approach to investigate the spatio-temporal regulation of WNT/β-catenin signaling during the early neural differentiation process of human neural progenitors cells (hNPCs), which form a new prospect for replacement therapies in the context of neurodegenerative diseases. Experimental measurements indicate a second signal mechanism, in addition to canonical WNT signaling, being involved in the regulation of nuclear β-catenin levels during the cell fate commitment phase of neural differentiation. We find that the biphasic activation of β-catenin signaling observed experimentally can only be explained through a model that combines Reactive Oxygen Species (ROS) and raft dependent WNT/β-catenin signaling. Accordingly after initiation of differentiation endogenous ROS activates DVL in a redox-dependent manner leading to a transient activation of down-stream β-catenin signaling, followed by continuous auto/paracrine WNT signaling, which crucially depends on lipid rafts. Our simulation studies further illustrate the elaborate spatio-temporal regulation of DVL, which, depending on its concentration and localization, may either act as direct inducer of the transient ROS/β-catenin signal or as amplifier during continuous auto-/parcrine WNT/β-catenin signaling. In addition we provide the first stochastic computational model of WNT/β-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent β-catenin activation. The model's predictive ability is demonstrated under a wide range of varying conditions for in-vitro and in-silico reference data sets. Our in-silico approach is realized in a multi-level rule-based language, that facilitates the extension and modification of the model. Thus, our results provide both new insights and means to further our understanding of canonical WNT/β-catenin signaling and the role of ROS as intracellular signaling mediator.
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Only a few simulation models were both calibrated and validated based on wet-lab data, namely those of Lee et al. (2003) [28], Kogan et al. (2012) [45], and Haack et al. (2015) [51].
“When looking at the CSM and VSM activities, we detect that only a few simulation models were both calibrated and validated based on wet-lab data, namely those of Lee et al. (2003) [28], Kogan et al. (2012) [45], and Haack et al. (2015) [51].”
From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [51]
Six out of 19 publications recorded their own wet-lab data [28, 31, 32, 45, 51, 57].
“When looking at Wet-lab Data, six out of 19 publications recorded their own wet-lab data [28, 31, 32, 45, 51, 57].”
From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [51]
A second cluster forms around the simulation studies [45, 46, 51, 56, 57] and either includes the same wet-lab data from [105, 106], the Cell cycle [46] or ROS [51, 57].
“A second cluster forms around the simulation studies [45, 46, 51, 56, 57] and either includes the same wet-lab data from [105, 106], the Cell cycle [46] or ROS [51, 57].”
From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [51]
Some studies develop a story line where a simulation model has been successively refined, extended, or composed ([28, 33, 34, 36, 41, 46, 48, 49, 51, 56]).
“Some studies develop a story line where a simulation model has been successively refined, extended, or composed ([28, 33, 34, 36, 41, 46, 48, 49, 51, 56]).”
From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [51]
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Dysregulation of cellular membrane homeostasis as a crucial modulator of cancer risk
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Staehlke, S., Haack, F., Waldner, A.-C., Koczan, D., Moerke, C., Mueller, P., Uhrmacher, A. M., & Nebe, J. B. (2020) · Cells · doi:10.3390/cells9081784 · Cited by 36 · see citing works
Self-citationLipid Rafts: The Maestros of Normal Brain Development
Viljetić, B., Blažetić, S., Labak, I., Ivić, V., Zjalić, M., Heffer, M., & Balog, M. (2024) · Biomolecules · doi:10.3390/biom14030362 · Cited by 36 · see citing works
Neuronal Signaling Involved in Neuronal Polarization and Growth: Lipid Rafts and Phosphorylation
Igarashi, M., Honda, A., Kawasaki, A., & Nozumi, M. (2020) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2020.00150 · Cited by 32 · see citing works
Provenance in modeling and simulation studies — Bridging gaps
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Self-citationIn Sickness and in Health: The Oxygen Reactive Species and the Bone
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Self-citationRelating simulation studies by provenance—Developing a family of Wnt signaling models
Budde, K., Smith, J., Wilsdorf, P., Haack, F., & Uhrmacher, A. M. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009227 · Cited by 23 · see citing works
“Submodels involving crosstalk with ERK/FGF/PI3K/Akt [32, 36, 53], Notch [33, 36, 48], and ROS/ Dvl-mediated pathways [51, 57] are shown in the lower panels of (b), respectively. https://doi.org/10.1371/journal.pcbi.1009227.g001”
BackgroundInfluentialSelf-citationA Model-Driven Approach for Conducting Simulation Experiments
Wilsdorf, P., Heller, J., Budde, K., Zimmermann, J., Warnke, T., Haubelt, C. D., Timmermann, D., van Rienen, U., & Uhrmacher, A. M. (2022) · Applied Sciences · doi:10.3390/app12167977 · Cited by 23 · see citing works
Self-citationArtifact-Based Workflows for Supporting Simulation Studies
Ruscheinski, A., Warnke, T., & Uhrmacher, A. M. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2899840 · Cited by 23 · see citing works
Self-citationAutomatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns
Wilsdorf, P., Wolpers, A., Hilton, J., Haack, F., & Uhrmacher, A. M. (2022) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/3564928 · Cited by 21 · see citing works
Self-citationTalabattula, V. A. N., Morgan, P., Frech, M. J., Uhrmacher, A. M., Herchenröder, O., Pützer, B. M., Rolfs, A., & Luo, J. (2017) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2017.07.030 · Cited by 20 · see citing works
Self-citationExploring the mechanistic and temporal regulation of LRP6 endocytosis in canonical WNT signaling
Haack, F., Budde, K., & Uhrmacher, A. M. (2020) · Journal of Cell Science · doi:10.1242/jcs.243675 · Cited by 18 · see citing works
Self-citationInhibition of Ape1 Redox Activity Promotes Odonto/osteogenic Differentiation of Dental Papilla Cells
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Conceptual Models in Simulation Studies: Making it Explicit
Wilsdorf, P., Haack, F., & Uhrmacher, A. M. (2020) · 2020 Winter Simulation Conference (WSC) · doi:10.1109/wsc48552.2020.9383984 · Cited by 15 · see citing works
Self-citationLi, T., He, H., Zhang, E., Hu, F., Wang, Z., Xu, J., Zeng, M., & Peng, B. (2025) · Brain Pathology · doi:10.1111/bpa.70033 · Cited by 15 · see citing works
Automatic Runtime Adaptation for Component-Based Simulation Algorithms
Helms, T., Ewald, R., Rybacki, S., & Uhrmacher, A. M. (2015) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/2821509 · Cited by 15 · see citing works
Self-citationReceptor/Raft Ratio Is a Determinant for LRP6 Phosphorylation and WNT/β-Catenin Signaling
Haack, F., Köster, T., & Uhrmacher, A. M. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.706731 · Cited by 14 · see citing works
Self-citationBittig, A. T., & Uhrmacher, A. M. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2598162 · Cited by 14 · see citing works
Self-citationPeng, D., Warnke, T., Haack, F., & Uhrmacher, A. M. (2017) · SIMULATION · doi:10.1177/0037549717704314 · Cited by 14 · see citing works
Self-citationSyntax and Semantics of a Multi-Level Modeling Language
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Self-citationWang, X., Chen, J., Hu, H., Gong, M., Wu, M., Ye, B., Hu, H. H., Du, Z., Liu, A., Huang, S., et al. (2025) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2025.117284 · Cited by 12 · see citing works
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Xue, X., Fan, C., Wang, L., Gao, L., Fan, K. C., Peng, M., Fang, F., Cheng, J., & Wang, J. (2022) · Theriogenology · doi:10.1016/j.theriogenology.2022.02.028 · Cited by 12 · see citing works
Bustamante-Barrientos, F. A., Lara-Barba, E., Herrera-Luna, Y., García-Guerrero, C., Silva-Pavez, E., Morales-Reyes, J., Araya, M. J., Yantén-Fuentes, L., Luque‐Campos, N., Altamirano, C., et al. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1555153 · Cited by 11 · see citing works
GENERATING SIMULATION EXPERIMENTS BASED ON MODEL DOCUMENTATIONS AND TEMPLATES
Ruscheinski, A., Budde, K., Warnke, T., Wilsdorf, P., Hiller, B. C., Dombrowsky, M., & Uhrmacher, A. M. (2018) · Winter Simulation Conference · doi:10.1109/wsc.2018.8632515 · Cited by 11 · see citing works
Self-citationBhowmick, R., & Sarkar, R. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235204 · Cited by 10 · see citing works
Conducting systematic, partly automated simulation studies – Unde Venis et Quo Vadis
Wilsdorf, P., Haack, F., Budde, K., Ruscheinski, A., & Uhrmacher, A. M. (2020) · AIP conference proceedings · doi:10.1063/5.0026939 · Cited by 9 · see citing works
Self-citationHe, Z., Du, X., Wu, Y., Hua, L., Wan, L., & Yan, N. (2019) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2019.4310 · Cited by 9 · see citing works
dos Santos, B. G., Klein, C. P., Crestani, M. S., Maurmann, R. M., Hözer, R. M., RODRIGUES, K. D. S., August, P. M., & Matté, C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18094805 · Cited by 9 · see citing works
Badralmaa, Y., & Natarajan, V. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.108057 · Cited by 8 · see citing works
Role of Nox4 in Neuronal Differentiation of Mouse Subventricular Zone Neural Stem Cells
Park, K.-Y., Na, Y., & Kim, M. S. (2016) · Journal of Life Science · doi:10.5352/jls.2016.26.1.8 · Cited by 8 · see citing works
Hartung, N., Benary, U., Wolf, J., & Kofahl, B. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0470-9 · Cited by 7 · see citing works
Modelling the role of redox-related mechanisms in musculoskeletal ageing
Guimera, A. M., Shanley, D. P., & Proctor, C. J. (2018) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2018.09.013 · Cited by 7 · see citing works
Ortiz-González, A., González-Pérez, P. P., Cárdenas-García, M., & Hernández-Linares, M. G. (2022) · Cancer Informatics · doi:10.1177/11769351221087028 · Cited by 7 · see citing works
Computational Models on Pathological Redox Signalling Driven by Pregnancy: A Review
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Discovering Biochemical Reaction Models by Evolving Libraries
Kreikemeyer, J. N., Burrage, K., & Uhrmacher, A. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71671-3_10 · Cited by 6 · see citing works
Self-citationYang, J., Zhu, B., & Lin, S. (2024) · Food Bioscience · doi:10.1016/j.fbio.2024.104348 · Cited by 5 · see citing works
5-Fluorouracil induces apoptosis of colorectal cancer cells
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Costa, R., Bellesso, S., Lualdi, S., Manzoli, R., Pistorio, V., Filocamo, M., & Moro, E. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz293 · Cited by 5 · see citing works
Expressive rule-based modeling and fast simulation for dynamic compartments
Köster, T., Henning, P., Warnke, T., & Uhrmacher, A. M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0312813 · Cited by 4 · see citing works
Self-citationSimulation studies of social systems: telling the story based on provenance patterns
Wilsdorf, P., Reinhardt, O., Prike, T., Hinsch, M., Bijak, J., & Uhrmacher, A. M. (2024) · Royal Society Open Science · doi:10.1098/rsos.240258 · Cited by 4 · see citing works
Self-citationPotential based, spatial simulation of dynamically nested particles
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Self-citationWilsdorf, P., Fischer, N., Haack, F., & Uhrmacher, A. M. (2021) · Winter Simulation Conference · doi:10.1109/wsc52266.2021.9715362 · Cited by 4 · see citing works
Self-citationCytochrome P450 enzymes as modulators of oncogenic signaling via the Wnt/β-catenin signaling pathway
Braeuning, A. (2026) · Archives of Toxicology · doi:10.1007/s00204-025-04295-7 · Cited by 3 · see citing works
Challenges and promises of self-adaptive simulation models
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Self-citationModeling cell signaling in heterogeneous cancer environments
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Introduction to In Silico Modeling to Study ROS Dynamics
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SIMPROV: Provenance capturing for simulation studies
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Self-citationMulti-Level Modeling and Simulation of Cellular Systems: An Introduction to ML-Rules
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Self-citationSingle-cell dynamics of core pluripotency factors in human pluripotent stem cellsPreprint
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Exploiting equation-free analysis for multi-level, agent-based models in cell biology
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Self-citationHaack, F., Burrage, K., & Uhrmacher, A. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.20.689481
Self-citationGoal-Oriented Generation of Simulation Experiments
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- Authors
- Haack, F., Lemcke, H., Ewald, R., Rharass, T., & Uhrmacher, A. M.
- Affiliations
- University of Rostock (DE)
- Universitätsmedizin Rostock (DE)
- Max Delbrück Center (DE)
- Corresponding author
- Haack, Fiete
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-03-20
- Volume
- 11
- Issue
- 3
- Pages
- e1004106
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4368204
- OpenAlex
- W1971196026
- Cited by
- 66
- References
- 90
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 9 · 2022: 7 · 2021: 6 · 2020: 8 · 2019: 8 · 2018: 3 · 2017: 8 · 2016: 5 · 2015: 3
- FWCI
- 2.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (1387/1)
- Universität Rostock
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Wnt/β-catenin signaling in development and cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- Wnt/β-catenin signaling, reactive oxygen species, lipid rafts, neural progenitor cells, neuronal differentiation, lineage commitment, Dvl, redox regulation, paracrine signaling, rule-based models, neurodegenerative disease treatment
- MeSH terms
- Cell Line, Computer Simulation, Humans, Reproducibility of Results, Reactive Oxygen Species, Computational Biology, Wnt Proteins, beta Catenin, Neural Stem Cells, Wnt Signaling Pathway, Spatio-Temporal Analysis
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