ArticlePLoS computational biology2015
Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Systems Biology of the Cancer Cell.Annual review of biomedical engineering · 2025Review
- Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease.Molecular systems biology · 2024Article
- Systems biology of angiogenesis signaling: Computational models and omics.WIREs mechanisms of disease · 2022Review
- Deciphering signal transduction networks in the liver by mechanistic mathematical modelling.The Biochemical journal · 2022Article
- TopoFilter: a MATLAB package for mechanistic model identification in systems biology.BMC bioinformatics · 2020Article
- Coupled feedback regulation of nuclear factor of activated T-cells (NFAT) modulates activation-induced cell death of T cells.Scientific reports · 2019Article
- Mechanistically detailed systems biology modeling of the HGF/Met pathway in hepatocellular carcinoma.NPJ systems biology and applications · 2019Article
- Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation.Molecular systems biology · 2017Article
- Computational Modeling in Liver Surgery.Frontiers in physiology · 2017Review
- In silico model-based inference: an emerging approach for inverse problems in engineering better medicines.Current opinion in chemical engineering · 2015Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.