Amstein2022 - TNFR1 Signal Transduction, Petri Net

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MODEL2210170001
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  • Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis.
  • Amstein LK, Ackermann J, Hannig J, Đikić I, Fulda S, Koch I
  • PLoS computational biology , 8/ 2022 , Volume 18 , Issue 8 , pages: e1010383 , PubMed ID: 35994517
  • Goethe University Frankfurt, Institute of Computer Science, Department of Molecular Bioinformatics, Frankfurt am Main, Germany. Cognitive Information Systems, Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen, Friedberg, Germany. Goethe University Frankfurt, Institute of Biochemistry II, Medical Faculty, Frankfurt am Main, Germany.
  • The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by in silico knockout analysis. Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system's dynamics using a discrete, semi-quantitative Petri net model.
Contributors
Submitter of the first revision: Ann-Kathrin Otto
Submitter of this revision: Ann-Kathrin Otto
Curators: Ann-Kathrin Otto

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isDescribedBy
PubMed 35994517
hasProperty
Mathematical Modelling Ontology Petri net

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Non-curated

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Amstein2022 - TNFR1 Signal Transduction - Petri Net.xml SBML L3V1 representation of Amstein2022 - Petri Net Model of TNFR1 signal transduction 194.37 KB Preview | Download

  • Model originally submitted by : Ann-Kathrin Otto
  • Submitted: Oct 17, 2022 9:17:25 AM
  • Last Modified: Oct 17, 2022 9:17:25 AM
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  • Version: 1 public model Download this version
    • Submitted on: Oct 17, 2022 9:17:25 AM
    • Submitted by: Ann-Kathrin Otto
    • With comment: Import of Amstein2022 - TNFR1 Signal Transduction, Petri Net