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Emergent Properties of Gene Regulatory Networks: Models and Data

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Abstract

We emphasize here the importance of generic models of biological systems that aim at describing the features that are common to a wide class of systems, instead of studying in detail a specific subsystem in a specific cell type or organism. Among generic models of gene regulatory networks, Random Boolean networks (RBNs) are reviewed in depth, and it is shown that they can accurately describe some important experimental data, in particular the statistical properties of the perturbations of gene expression levels induced by the knock-out of a single gene. It is also shown that this kind of study may shed light on a candidate general dynamical property of biological systems. Several biologically plausible modifications of the original model are reviewed and discussed, and it is also show how RBNs can be applied to describe cell differentiation.

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Notes

  1. 1.

    A term borrowed from condensed matter physics.

  2. 2.

    It might then appear surprising that a random network is able to describe the data on gene knock-out: this is actually due to the fact that the initial choice of the parameters concentrated on critical networks, that are the most widely studied in the literature.

  3. 3.

    As it was observed, the asymptotic states of finite RBNs are cycles of finite length (a fixed point being a cycle of length 1), so no real chaotic dynamics is possible; however, due to the sensitive dependence upon initial conditions, the disordered region is also often termed "chaotic".

  4. 4.

    Remember that, as it was observed in Sect. 3.3, the number of attractors with a significant attraction basin grows in this way.

  5. 5.

    From now on, the term attractor will always refer to those of the deterministic RBN.

  6. 6.

    In a few cases two ergodic sets have been observed, in networks with a few hundred nodes.

  7. 7.

    In some cases the rest of the system is not ignored, but described at a very aggregated level in a crude way: the remarks in the text hold also in this case.

Abbreviations

RBN:

Random Boolean Network

DNA:

Deoxyribonucleic acid

RNA:

Ribonucleic acid

miRNA:

micro RNA (short RNA molecule)

mRNA:

messenger RNA

cDNA:

complementary DNA

SFRBN:

Scale-free Random Boolean Network

TES:

Threshold Ergodic Sets

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Acknowledgments

We have developed our understanding of genetic network dynamics interacting with several scientists and collaborators. In particular, we are grateful to Stuart Kauffman who shared with us much of his insight, and of his evolving vision. Among several students and collaborators let us mention in particular Chiara Damiani, Alessandro Filisetti, Alex Graudenzi (the complete list would be too long). We also had the privilege of discussing our ideas with some biologists, in particular with Annamaria Colacci. On a more general ground, our understanding of the role of modeling such complex systems has been shaped in several fruitful discussions and joint works with David Lane and Gianni Zanarini, although of course we take full responsibility of the positions taken in this chapter. The present work has been partly supported by the Miur Project MITICA (FISR nr. 2982/Ric) and by the EU-FET projects MD (ref. 284625) and INSITE (ref. 271574) under the 7th Framework Programme.

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Authors and Affiliations

  1. Department of Physics, Computer Science and Mathematics, Modena and Reggio Emilia University, v. Campi 213b, 41125, Modena, Italy

    Roberto Serra & Marco Villani

  2. European Centre for Living Technology, Ca’Minich, S. Marco 2940, 30124, Venezia, Italy

    Roberto Serra & Marco Villani

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  1. Roberto Serra
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Correspondence to Roberto Serra .

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Editors and Affiliations

  1. Chemical and Biological Engineering, Vanderbilt University, Nashville, TN, USA

    Aleš Prokop

  2. Research Group on Process Network Engineering, Kaposvár University, Kaposvár, Hungary

    Béla Csukás

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Serra, R., Villani, M. (2013). Emergent Properties of Gene Regulatory Networks: Models and Data. In: Prokop, A., Csukás, B. (eds) Systems Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6803-1_3

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