ArticlePLoS computational biology2011
Dynamical and structural analysis of a T cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia.
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 80 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
80 citing papers in PubMed, 193 citations in OpenAlex.
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- Phenotype Control techniques for Boolean gene regulatory networks.Bulletin of mathematical biology · 2023Review
- Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Phenotype control techniques for Boolean gene regulatory networks.bioRxiv : the preprint server for biology · 2023Article
- Boolean network sketches: a unifying framework for logical model inference.Bioinformatics (Oxford, England) · 2023Article
- Relative importance of composition structures and biologically meaningful logics in bipartite Boolean models of gene regulation.Scientific reports · 2022Article
- Probabilistic edge weights fine-tune Boolean network dynamics.PLoS computational biology · 2022Article
- Influence maximization in Boolean networks.Nature communications · 2022Article
- Identification of periodic attractors in Boolean networks using a priori information.PLoS computational biology · 2022Article
- Attractor detection and enumeration algorithms for Boolean networks.Computational and structural biotechnology journal · 2022Review
- Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022Article
- Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks.Science advances · 2021Article
- The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The blood cancer T cell large granular lymphocyte (T-LGL) leukemia is a chronic disease characterized by a clonal proliferation of cytotoxic T cells. As no curative therapy is yet known for this disease, identification of potential therapeutic targets is of immense importance. In this paper, we perform a comprehensive dynamical and structural analysis of a network model of this disease. By employing a network reduction technique, we identify the stationary states (fixed points) of the system, representing normal and diseased (T-LGL) behavior, and analyze their precursor states (basins of attraction) using an asynchronous Boolean dynamic framework. This analysis identifies the T-LGL states of 54 components of the network, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions. We further test and validate one of these newly identified states experimentally. Specifically, we verify the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3. Our systematic perturbation analysis using dynamical and structural methods leads to the identification of 19 potential therapeutic targets, 68% of which are corroborated by experimental evidence. The novel therapeutic targets provide valuable guidance for wet-bench experiments. In addition, we successfully identify two new candidates for engineering long-lived T cells necessary for the delivery of virus and cancer vaccines. Overall, this study provides a bird's-eye-view of the avenues available for identification of therapeutic targets for similar diseases through perturbation of the underlying signal transduction network.
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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.