ArticlePLoS computational biology2020
Perturbation biology links temporal protein changes to drug responses in a melanoma cell line.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 24 citations in OpenAlex.
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- A comprehensive mechanistic model of adipocyte signaling with layers of confidence.NPJ systems biology and applications · 2023Article
- Hypothesis-generating proteome perturbation to identify NEU-4438 and acoziborole modes of action in the African Trypanosome.iScience · 2022Article
- Network inference from perturbation time course data.NPJ systems biology and applications · 2022Article
- Identifying mutant-specific multi-drug combinations using comparative network reconstruction.iScience · 2022Article
- A method for the inference of cytokine interaction networks.PLoS computational biology · 2022Article
- Combination treatment optimization using a pan-cancer pathway model.PLoS computational biology · 2021Article
- HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks.PLoS computational biology · 2021Article
- PHENSIM: Phenotype Simulator.PLoS computational biology · 2021Article
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.
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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.