ArticleBMC systems biology2011
Combined logical and data-driven models for linking signalling pathways to cellular response.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 28 citations in OpenAlex.
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- Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma.PLoS computational biology · 2019Article
- Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine.Frontiers in bioengineering and biotechnology · 2018Review
- Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models.FEMS yeast research · 2017Review
- Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.BMC systems biology · 2016Article
- Network-Based Analysis of Nutraceuticals in Human Hepatocellular Carcinomas Reveals Mechanisms of Chemopreventive Action.CPT: pharmacometrics & systems pharmacology · 2015Article
- Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge.Bioinformatics (Oxford, England) · 2015Article
- A crowd-sourcing approach for the construction of species-specific cell signaling networks.Bioinformatics (Oxford, England) · 2015Article
- Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.PloS one · 2015Article
- Leveraging systems biology approaches in clinical pharmacology.Biopharmaceutics & drug disposition · 2013Review
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- Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.PloS one · 2012Article
- Nonparametric simulation of signal transduction networks with semi-synchronized update.PloS one · 2012Article
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSignalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity.
resultsIn this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines.
conclusionsWe developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion.
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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.