ArticlePLoS computational biology2007
A logical model provides insights into T cell receptor signaling.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
134 citing papers in PubMed.
- Hybrid Computational Modeling with Multi-Level Validation Identifies TK1-VIM as a Robust Therapeutic Pair in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- Multiscale predictive cellular modeling: integrating hypothesis grammars, digital twins, and multi-omics for In silico oncology and precision theranostics.Functional & integrative genomics · 2026Review
- Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks.NPJ systems biology and applications · 2026Article
- Circulating microRNAs in preterm white matter injury: a systems biology/qPCR-based pilot study.Pediatric research · 2026Article
- VIOLIN: A modular framework for scalable reconciliation of heterogeneous interaction graphs.bioRxiv : the preprint server for biology · 2026Article
- Context-dependent immune regulation: a mechanistic and AI-enabled integrative framework.Frontiers in immunology · 2026Review
- Personalized Boolean network dynamic profiling and machine learning approaches to identify combinatorial treatments for prostate cancer.Frontiers in pharmacology · 2026Article
- Article
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- Logic-based modeling of inflammatory macrophage cross talk with glomerular endothelial cells in diabetic kidney disease.American journal of physiology. Renal physiology · 2025Article
- Quantitative modeling of signaling in aggressive B cell lymphoma unveils conserved core network.PLoS computational biology · 2024Article
- Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024Review
- Dynamical methods for target control of biological networks.Royal Society open science · 2023Article
- A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion.NPJ systems biology and applications · 2023Article
- Emergent Criticality in Coupled Boolean Networks.Entropy (Basel, Switzerland) · 2023Article
- A multiscale mechanistic model of human dendritic cells forFrontiers in immunology · 2023Article
- Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search.Frontiers in cell and developmental biology · 2023Article
- CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19.Pathogens (Basel, Switzerland) · 2022Article
- Relative importance of composition structures and biologically meaningful logics in bipartite Boolean models of gene regulation.Scientific reports · 2022Article
- Regulating the discriminatory response to antigen by T-cell receptor.Bioscience reports · 2022Review
74 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.
Indexed as
Identifiers
What OpenQuestion holds
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.