Boolean Network Modeling

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While the expression level of thousands of genes can be measured in parallel, our knowledge about causal relationship between regulator and target genes is limited.  If we describe the relationships between regulators and targets as a directed graph, where the source of the edge is the regulator and the target is the target of regulation, then we lack knowledge about which subgraph is relevant under various conditions.   We also do not have a detailed understanding of the combined influence of regulators on their targets and the timing of the effect.  How do we model the system of interactions despite these limitations? The Boolean network model provides an abstraction of the actual biological interactions, making it easier to work with partial knowledge.  However, inference is difficult due to computational challenges.  We developed an inference method based on Kolmogorov Complexity, which is described in the papers below.

Publications:

Constructing logical models of gene regulatory networks by integrating transcription factor-DNA interactions with expression data: an entropy-based approach 

Computing Minimal Boolean Models of Gene Regulatory Networks 

Optimal Inference of Asynchronous Boolean Network Models 

Software:

https://www.github.com/karleg/MEDSI