AA1 – Mechanisms of Life



Spatial Dynamics of Cell Signaling Explored with Agent-Based Modeling

Project Heads

Edda Klipp, Rune Linding, Caren Tischendorf

Project Members

Jorin Diemer

Project Duration

01.01.2022 − 31.12.2023

Located at

HU Berlin


Within the cell, a spatio-temporal network of protein interactions is responsible for signal transduction. The rate of signal transmission depends on the spatial organization of the cell as well as the number of individual proteins involved. The high number of signalling proteins and their respective phosphorylation states requires a rule-based modeling approach to assess their dynamics. We developed such a model for protein complex formation and cell signalling in breast cancer cells, which commit to migration during a wound healing assay. To study the decision mechanism behind the commencing migration a comprehensive dataset was aquired, including imaging, proteomics, phosphoproteomic, and transcriptomic data. During migration cells change their shape as well as their phosphoproteome. To integrate these findings, we developed a spatio-temporal rule-based model for cell signalling.

The current model describes the initial steps of the well-studied RAS-RAF-MEK-ERK pathway and it incorporates protein diffusion in two and three dimensions, describing membrane-bound and cytosolic movement of proteins, respectively. The cell shape in the model is currently assumed to be spherical/ellipsoidal, but will be later replaced by more realistic shapes extracted from the imaging data. Furthermore, the phosphoproteomics data will be used to identify sub-networks which show changes during the migration process. Those sub-networks can then be simulated in realistic cell shapes to study the influence of the shape on the signal transduction in silico.

External Website

Related Publications

  • W. Giese, G. Milicic, A. Schroder, and E. Klipp, “Spatial modeling of the membrane-cytosolic interface in protein kinase signal transduction”, PLoS Computational Biology, 14(4):e1006075, 2018. doi: 10.1371/journal.pcbi.1006075.
  • W. Giese, S. Eigel Matthias und Westerheide, C. Engwer, and E. Klipp, “In
    uence of cell shape, inhomogeneities and di usion barriers in cell polarization models”, Phys Biol., 12(6):066014, 2015. doi: 10.1088/1478-3975/12/6/066014.
  • B. Goldenbogen, W. Giese, M. Hemmen, J. Uhlendorf, A. Herrmann, and E. Klipp, “Dynamics of cell wall elasticity pattern shapes the cell during yeast mating morphogenesis”, Open Biol., 6(9):160136, 2016. doi: 10.1098/rsob.160136.
  • B. Goldenbogen, S. O. Adler, O. Bodeit, J. A. H. Wodke, et al., R. Linding, and E. Klipp, “Optimality in COVID-19 vaccination strategies determined by heterogeneity in humanhuman interaction networks”, medRxiv, 2020. doi: 10.1101/2020.12.16.20248301.
  • Diemer J, Hahn J, Goldenbogen B, Müller K, Klipp E (2021) Sperm migration in the genital tract—In silico experiments identify key factors for reproductive success. PLoS Comput Biol 17(7): e1009109. https://doi.org/10.1371/journal.pcbi.1009109
  • J. Longden, X. Robin, M. Engel, J. Ferkingho -Borg, I. Kjaer, I. D. Horak, M. W. Pedersen, and R. Linding, “Deep neural networks identify signaling mechanisms of erbb-family drug resistance from a continuous cell morphology space”, Cell Rep., 34(3):108657, 2021. doi:10.1016/j.celrep.2020.108657.
  • J. Grie, R. Peters, and D. M. Owen, “An agent-based model of molecular aggregation at the cell membrane”, PLOS ONE, 2020. doi: 10.1371/journal.pone.0226825.
  •  L. Jansen and C. Tischendorf, “A unfi ed (P)DAE modeling approach for
    flow networks”, in Progress in Di erential-Algebraic Equations, S. Schops and et.al., Eds., Springer Berlin
    Heidelberg, 2014, pp. 127-151. doi: 10.1007/978-3-662-44926-4_7.
  •  I. C. Garcia, S. Schops, C. Strohm, and C. Tischendorf, “Generalized elements for a structural analysis of circuits,” in Progress in Di erential-Algebraic Equations II., T. Reis and et al., Eds., Springer, Cham, 2020, pp. 397-431. doi: 10.1007/978-3-030-53905-4_13.
  • T. Streubel, C. Strohm, P. Trunschke, and C. Tischendorf, “Generic construction and ecient evaluation of flow network DAEs and their derivatives in the context of gas networks.”, in Operations Research Proceedings 2017, N. Kliewer and et al., Eds., Springer, Cham, 2018, pp. 627-632. doi:10.1007/978-3-319-89920-6_83.

Related Pictures

Rule-based modelling of cell signalling

When MDA-MB231 cells migrate, they change shape and phosphoproteome. To integrate these findings a spatio-temporal, rule-based model is developed.