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Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns
Published on: February 13, 2011
Control of asymmetric Hopfield networks and application to cancer attractors
Anthony Szedlak1, Giovanni Paternostro2, Carlo Piermarocchi3
1Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America.
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.
Insights
This study models gene regulatory networks using the asymmetric Hopfield model to identify therapeutic targets. It pinpoints critical signaling bottlenecks for selective cancer cell disruption, aiding novel drug design.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Gene regulatory networks (GRNs) govern cellular functions through complex signaling pathways.
- Understanding GRN dynamics is crucial for identifying therapeutic targets in diseases like cancer.
Purpose of the Study:
- To develop and apply a computational model for simulating GRN signaling dynamics.
- To identify key signaling bottlenecks for targeted therapeutic interventions.
- To discover potential protein targets for selective disruption of cancer cell signaling.
Main Methods:
- Utilized the asymmetric Hopfield model to map gene expression patterns to attractor states.
- Analyzed control strategies based on local fields to disrupt attractor patterns.
- Identified signaling bottlenecks (nodes or clusters) impacting network dynamics.
- Developed a theorem for controlling bottlenecks in strongly connected components.
- Applied the model to experimentally validated B cell and lung cancer GRNs.
Main Results:
- Demonstrated the mapping of gene expression patterns to attractor states.
- Identified specific proteins (e.g., TP53, FOXM1, BCL6, SRC) as potential therapeutic targets.
- Showcased the model's ability to differentiate between cancer and normal cell signaling.
- Provided theoretical bounds for controlling network bottlenecks.
Conclusions:
- The asymmetric Hopfield model effectively simulates GRN signaling and identifies therapeutic targets.
- Targeting identified signaling bottlenecks offers a strategy for selective cancer therapy.
- This approach facilitates the rational design of novel, robust therapeutic interventions for complex diseases.
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