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Published in:
2013 | OriginalPaper | Chapter
Emergence of Motifs in Model Gene Regulatory Networks
Author : Marcin Zagórski
Published in: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Publisher: Springer Berlin Heidelberg
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Gene regulatory networks arise in all living cells, allowing the control of gene expression patterns. The study of their circuitry has revealed that certain subgraphs of interactions or motifs appear at anomalously high frequencies. We investigate here whether the overrepresentation of these motifs can be explained by the functional capabilities of these networks. Given a framework for describing regulatory interactions and dynamics, we consider in the space of all regulatory networks those that have a prescribed function. Markov Chain Monte Carlo sampling is then used to determine how these functional networks lead to specific motif statistics in the interaction structure. We conclude that different classes of network motifs are found depending on the functional constraint (multi-stability or oscillatory behaviour) imposed on the system evolution. The discussed computational framework can also be used for predicting regulatory interactions, if only the experimental gene expression pattern is provided.