ArticlePLoS computational biology2011
Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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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
66 citing papers in PubMed.
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- Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3DBiophysics reviews · 2024Review
- Ten quick tips for fuzzy logic modeling of biomedical systems.PLoS computational biology · 2023Article
- Computational systems biology in disease modeling and control, review and perspectives.NPJ systems biology and applications · 2022Review
- Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Blue Biotechnology: Computational Screening ofMarine drugs · 2021Article
- Simulating ATO Mechanism and EGFR Signaling with Fuzzy Logic and Petri Net.Journal of biomedical physics & engineering · 2021Article
- Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells.Bioinformatics (Oxford, England) · 2020Article
- Executable pathway analysis using ensemble discrete-state modeling for large-scale data.PLoS computational biology · 2019Article
- Encoding the β-Arrestin Trafficking Fate of Ghrelin Receptor GHSR1a: C-Tail-Independent Molecular Determinants in GPCRs.ACS pharmacology & translational science · 2019Article
- Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz.F1000Research · 2019Article
- From Discrete to Continuous Modeling of Lymphocyte Development and Plasticity in Chronic Diseases.Frontiers in immunology · 2019Review
- Mathematical modeling and computational prediction of cancer drug resistance.Briefings in bioinformatics · 2018Review
- Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks.BMC bioinformatics · 2018Article
- Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy.PLoS biology · 2018Article
- Therapeutic target discovery using Boolean network attractors: improvements of kali.Royal Society open science · 2018Article
- A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology.PloS one · 2017Article
- Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.BMC systems biology · 2016Article
- Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1.PLoS computational biology · 2016Article
- Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data.Scientific reports · 2016Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.
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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.