Inferring Boolean network states from partial information

EURASIP Journal on Bioinformatics & Systems Biology
Guy Karlebach

Abstract

Networks of molecular interactions regulate key processes in living cells. Therefore, understanding their functionality is a high priority in advancing biological knowledge. Boolean networks are often used to describe cellular networks mathematically and are fitted to experimental datasets. The fitting often results in ambiguities since the interpretation of the measurements is not straightforward and since the data contain noise. In order to facilitate a more reliable mapping between datasets and Boolean networks, we develop an algorithm that infers network trajectories from a dataset distorted by noise. We analyze our algorithm theoretically and demonstrate its accuracy using simulation and microarray expression data.

Citations

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Jan 6, 2012·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Guy Karlebach, Ron Shamir

Related Concepts

Biological Neural Networks
Simulation
Silo (Dataset)
Orientation (spatial)
Protein Expression

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