Abstract
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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Alon, U.: Network motifs: theory and experimental approaches. Nature Reviews Genetics 8, 450 (2007)
Burda, Z., Krzywicki, A., Martin, O.C., Zagorski, M.: Proc. Natl. Acad. Sci. U.S.A. 108, 17263 (2011)
Elowitz, M., Leibler, S.: A synthetic oscillatory network of transcriptional regulators. Nature 403, 335 (2000)
Gardner, T., Cantor, C., Collins, J.: Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339 (2000)
Gerstein, M.B., et al.: Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91 (2012)
Herrgard, M., Covert, M., Palsson, B.: Reconstruction of microbial transcriptional regulatory networks. Current Opinion in Biotechnology 15, 70 (2004)
Hu, Z., Killion, P., Iyer, V.: Genetic reconstruction of a functional transcriptional regulatory network. Nature Genetics 39, 683 (2007)
Lee, T., et al.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799 (2002)
Ma, H., et al.: An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. Nucleic Acids Research 32, 6643 (2004)
Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296, 910 (2002)
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087 (1953)
Ptashne, M.: A Genetic Switch: Phage λ Revisited. Cold Harbor Spring Laboratory Press, NY (2004)
Salgado, H., et al.: Regulondb (version 5.0): Escherichia coli k-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Research 34, D394 (2006)
Shen-Orr, S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 (2002)
Zagorski, M., Krzywicki, A., Martin, O.C.: Edge usage, motifs and regulatory logic for cell cycling genetic networks. Phys. Rev. E 87, 012727 (2013)
Zhu, J., et al.: Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genetics 40, 854 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zagórski, M. (2013). Emergence of Motifs in Model Gene Regulatory Networks. In: Vanneschi, L., Bush, W.S., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science, vol 7833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37189-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-37189-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37188-2
Online ISBN: 978-3-642-37189-9
eBook Packages: Computer ScienceComputer Science (R0)