ArticlePloS one2014
Control of asymmetric Hopfield networks and application to cancer attractors.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 29 citations in OpenAlex.
- Article
- Article
- Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks.Frontiers in genetics · 2020Article
- Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
- Hope4Genes: a Hopfield-like class prediction algorithm for transcriptomic data.Scientific reports · 2019Article
- Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems.PLoS computational biology · 2017Article
- Mathematical models of cell phenotype regulation and reprogramming: Make cancer cells sensitive again!Biochimica et biophysica acta. Reviews on cancer · 2017Review
- Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity.Cancer research · 2017Article
- A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space.PloS one · 2015Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
The asymmetric Hopfield model is used to simulate signaling dynamics in gene regulatory networks. The model allows for a direct mapping of a gene expression pattern into attractor states. We analyze different control strategies aimed at disrupting attractor patterns using selective local fields representing therapeutic interventions. The control strategies are based on the identification of signaling bottlenecks, which are single nodes or strongly connected clusters of nodes that have a large impact on the signaling. We provide a theorem with bounds on the minimum number of nodes that guarantee control of bottlenecks consisting of strongly connected components. The control strategies are applied to the identification of sets of proteins that, when inhibited, selectively disrupt the signaling of cancer cells while preserving the signaling of normal cells. We use an experimentally validated non-specific and an algorithmically-assembled specific B cell gene regulatory network reconstructed from gene expression data to model cancer signaling in lung and B cells, respectively. Among the potential targets identified here are TP53, FOXM1, BCL6 and SRC. This model could help in the rational design of novel robust therapeutic interventions based on our increasing knowledge of complex gene signaling networks.
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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.