Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways

Overview

Journal Int J Mol Sci

Publisher MDPI

Specialties Biochemistry
Chemistry
Molecular Biology

Date 2024 Sep 28

PMID 39337687

Authors

Jinping Feng

Xinan Zhang

Tianhai Tian

Affiliations

Soon will be listed here.

Abstract

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

References

Schoeberl B, Eichler-Jonsson C, Gilles E, Muller G . Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol. 2002; 20(4):370-5. DOI: 10.1038/nbt0402-370. View

Gagliardi P, Pertz O . The mitogen-activated protein kinase network, wired to dynamically function at multiple scales. Curr Opin Cell Biol. 2024; 88:102368. DOI: 10.1016/j.ceb.2024.102368. View

Kestler H, Wawra C, Kracher B, Kuhl M . Network modeling of signal transduction: establishing the global view. Bioessays. 2008; 30(11-12):1110-25. DOI: 10.1002/bies.20834. View

Mendoza M, Er E, Blenis J . The Ras-ERK and PI3K-mTOR pathways: cross-talk and compensation. Trends Biochem Sci. 2011; 36(6):320-8. PMC: 3112285. DOI: 10.1016/j.tibs.2011.03.006. View

Zeng Q, Li S, Chepeha D, Giordano T, Li J, Zhang H . Crosstalk between tumor and endothelial cells promotes tumor angiogenesis by MAPK activation of Notch signaling. Cancer Cell. 2005; 8(1):13-23. DOI: 10.1016/j.ccr.2005.06.004. View

Birtwistle M, Hatakeyama M, Yumoto N, Ogunnaike B, Hoek J, Kholodenko B . Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol. 2007; 3:144. PMC: 2132449. DOI: 10.1038/msb4100188. View

Frohlich F, Gerosa L, Muhlich J, Sorger P . Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance. Mol Syst Biol. 2023; 19(2):e10988. PMC: 9912026. DOI: 10.15252/msb.202210988. View

Lebedeva G, Sorokin A, Faratian D, Mullen P, Goltsov A, Langdon S . Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network. Eur J Pharm Sci. 2011; 46(4):244-58. PMC: 3398788. DOI: 10.1016/j.ejps.2011.10.026. View

Yazdani A, Lu L, Raissi M, Karniadakis G . Systems biology informed deep learning for inferring parameters and hidden dynamics. PLoS Comput Biol. 2020; 16(11):e1007575. PMC: 7710119. DOI: 10.1371/journal.pcbi.1007575. View

10.

Simone A, Evanitsky M, Hayden L, Cox B, Wang J, Tornini V . Control of osteoblast regeneration by a train of Erk activity waves. Nature. 2021; 590(7844):129-133. PMC: 7864885. DOI: 10.1038/s41586-020-03085-8. View

11.

Ogura Y, Wen F, Sami M, Shibata T, Hayashi S . A Switch-like Activation Relay of EGFR-ERK Signaling Regulates a Wave of Cellular Contractility for Epithelial Invagination. Dev Cell. 2018; 46(2):162-172.e5. DOI: 10.1016/j.devcel.2018.06.004. View

12.

Kim M, Kim E . Mathematical model of the cell signaling pathway based on the extended Boolean network model with a stochastic process. BMC Bioinformatics. 2022; 23(1):515. PMC: 9710037. DOI: 10.1186/s12859-022-05077-z. View

13.

Keating S, Waltemath D, Konig M, Zhang F, Drager A, Chaouiya C . SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol. 2020; 16(8):e9110. PMC: 8411907. DOI: 10.15252/msb.20199110. View

14.

Wang J, Wu Q, Hu X, Tian T . An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein. Methods. 2016; 110:3-13. DOI: 10.1016/j.ymeth.2016.08.001. View

15.

Waltermann C, Klipp E . Information theory based approaches to cellular signaling. Biochim Biophys Acta. 2011; 1810(10):924-32. DOI: 10.1016/j.bbagen.2011.07.009. View

16.

Mac Gabhann F, Popel A . Dimerization of VEGF receptors and implications for signal transduction: a computational study. Biophys Chem. 2007; 128(2-3):125-39. PMC: 2711879. DOI: 10.1016/j.bpc.2007.03.010. View

17.

Bhalla U, Ram P, Iyengar R . MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network. Science. 2002; 297(5583):1018-23. DOI: 10.1126/science.1068873. View

18.

Chen A, Ren Q, Zhou T, Burrage P, Tian T, Burrage K . Balanced implicit Patankar-Euler methods for positive solutions of stochastic differential equations of biological regulatory systems. J Chem Phys. 2024; 160(6). DOI: 10.1063/5.0187202. View

19.

Albert R, Wang R . Discrete dynamic modeling of cellular signaling networks. Methods Enzymol. 2009; 467:281-306. DOI: 10.1016/S0076-6879(09)67011-7. View

20.

Haugh J . A unified model for signal transduction reactions in cellular membranes. Biophys J. 2002; 82(2):591-604. PMC: 1301871. DOI: 10.1016/S0006-3495(02)75424-6. View