ArticleCancer research2011
Comparing signaling networks between normal and transformed hepatocytes using discrete logical models.
Article in Cancer research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
69 citing papers in PubMed.
- Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model.Molecular oncology · 2014Trial
- Central role for MCP-1/CCL2 in injury-induced inflammation revealed by in vitro, in silico, and clinical studies.PloS one · 2013Trial
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Personalized Boolean network dynamic profiling and machine learning approaches to identify combinatorial treatments for prostate cancer.Frontiers in pharmacology · 2026Article
- Computational modeling and simulation in oncology.Clinical and translational medicine · 2025Article
- Systems Biology of the Cancer Cell.Annual review of biomedical engineering · 2025Review
- Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.BMC chemistry · 2023Article
- Identifying mutant-specific multi-drug combinations using comparative network reconstruction.iScience · 2022Article
- Alveolar Regeneration in COVID-19 Patients: A Network Perspective.International journal of molecular sciences · 2021Review
- Unsupervised logic-based mechanism inference for network-driven biological processes.PLoS computational biology · 2021Article
- Network model-based screen for FDA-approved drugs affecting cardiac fibrosis.CPT: pharmacometrics & systems pharmacology · 2021Article
- Integrating Patient-Specific Information into Logic Models of Complex Diseases: Application to Acute Myeloid Leukemia.Journal of personalized medicine · 2021Article
- WebMaBoSS: A Web Interface for Simulating Boolean Models Stochastically.Frontiers in molecular biosciences · 2021Article
- Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer.Frontiers in medicine · 2021Article
- LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.BMC bioinformatics · 2020Article
- Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies.Molecular systems biology · 2020Article
- Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma.PLoS computational biology · 2019Article
- Conceptual Evolution of Cell Signaling.International journal of molecular sciences · 2019Review
- Comparative Network Reconstruction using mixed integer programming.Bioinformatics (Oxford, England) · 2018Article
- Boolean network modeling in systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2018Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.
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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.