Computing Minimal Boolean Models of Gene Regulatory Networks
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Guy Karlebach, Peter Robinson
2021
Abstract
Models of Gene Regulatory Networks (GRNs) capture the dynamics of the regulatory processes that occur within the cell as a means to understand the variability observed in gene expression between different conditions. Possibly the simplest mathematical construct used for modeling is the Boolean network, which dictates a set of logical rules for transition between states described as Boolean vectors. Due to the complexity of gene regulation and the limitations of experimental technologies, in most cases knowledge about regulatory interactions and Boolean states is partial. In addition, the logical rules themselves are not known a-priori. Our goal in this work is to present a methodology for inferring this information from the data, and to provide a measure for comparing network states under different biological conditions. Methods: We present a novel methodology for integrating experimental data and performing a search for the optimal consistent structure via optimization of a linear objective function under a set of linear constraints. We also present a statistical approach for testing the similarity of network states under different conditions. Results: Our methodology finds the optimal model using an experimental gene expression dataset from human CD4 T-cells and shows that network states are different between healthy controls and rheumatoid arthritis patients. Conclusion: The problem can be solved optimally using real-world data. Properties of the inferred network show the importance of a general approach. Significance: Our methodology will enable researchers to obtain a better understanding of the function of gene regulatory networks and their biological role.
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Date 2021-05-23
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