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 transmission depends on the spatial organization of the cell as well as the number of individual proteins involved. The number of molecules differs between a few dozens scaffolding proteins and thousands for the final MAP kinase, which is responsible for transducing the signal from the membrane bound protein complexes towards the cell’s nucleus. Abnormal regulation of MAPK cascades contributes to cancer. This project aims to better understand the influence that the spatial organization of a cell has on the organization of protein complexes and thus the efficacy of cell signaling. Therefore, we develop a spatio-temporal agent-based model for protein complex formation and cell signalling. Simulation results will be compared to an available comprehensive dataset for breast cancer, comprising of time-resolved transcriptomic, (phosphor-)proteomic and imaging data.
The model will incorporate protein diffusion in two and three dimensions, describing membrane-bound and cytosolic movement of proteins respectively. Spatial restriction caused by intracellular organelles, which may limit the accessibility of protein (or shield the protein), are considered. By tracking the interactions of each protein, the underlying protein network can be extracted and analyzed.

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.