ArticlePLoS computational biology2013
Perturbation biology: inferring signaling networks in cellular systems.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed.
- Inferring feedback regulation from static snapshots of a single signal.Biochemistry and biophysics reports · 2026Article
- Patient-specific modeling of G2/M cell cycle dynamics reveals prognostic dynamical regimes driven by copy number alterations.NPJ systems biology and applications · 2026Article
- AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes.Briefings in bioinformatics · 2025Article
- Article
- AUC-PR is a More Informative Metric for Assessing the Biological Relevance of In Silico Cellular Perturbation Prediction Models.bioRxiv : the preprint server for biology · 2025Article
- Mathematical models of intercellular signaling in breast cancer.Seminars in cancer biology · 2025Review
- AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.eLife · 2025Article
- Hypoxia alters the response of ovarian cancer cells to the mitomycin C drug.Frontiers in cell and developmental biology · 2025Article
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
- AI-empowered perturbation proteomics for complex biological systems.Cell genomics · 2024Article
- Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets.Cancers · 2024Article
- Graph Structured Neural Networks for Perturbation Biology.bioRxiv : the preprint server for biology · 2024Article
- A proteomic landscape of pharmacologic perturbations for functional relevance.Journal of pharmaceutical analysis · 2024Article
- Reversing pathological cell states: the road less travelled can extend the therapeutic horizon.Trends in cell biology · 2023Review
- Constructing maps between distinct cell fates and parametric conditions by systematic perturbations.Bioinformatics (Oxford, England) · 2023Article
- Understanding and applying biological resilience, from genes to ecosystems.npj biodiversity · 2023Review
- Evolutionary Stability of Small Molecular Regulatory Networks That Exhibit Near-Perfect Adaptation.Biology · 2023Article
- Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.Journal of chemical information and modeling · 2023Review
- Network inference from perturbation time course data.NPJ systems biology and applications · 2022Article
- Parallelization with Dual-Trap Single-Column Configuration Maximizes Throughput of Proteomic Analysis.Analytical chemistry · 2022Article
15 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
16 authors.
Funding
Abstract
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.
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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.