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Modeling Genetic Regulatory Networks

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Modeling in Systems Biology

Part of the book series: Computational Biology ((COBO,volume 16))

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Abstract

Cellular systems are regulated by complex genetic control structures known as genetic regulatory networks (GRNs). In this chapter, we present a range of practical techniques for qualitatively modeling and analyzing GRNs using Petri nets. Our starting point is the well-known Boolean network approach, where regulatory entities (i.e., genes, proteins and environmental signals) are viewed abstractly as binary switches. We present an approach for translating synchronous Boolean networks into Petri net models and introduce the support tool GNaPN which automates model construction. We illustrate our techniques by modeling the GRN for carbon stress response in Escherichia coli and, in particular, consider how existing Petri net techniques and tools can be used to understand and analyze such a GRN model. While asynchronous GRN models are considered more realistic than their synchronous counterparts, they often suffer from the problem of capturing too much behavior. We investigate how techniques from asynchronous electronic circuit design based on Signal Transition Graphs (STGs) and Speed-Independent circuits can be used to address this, by identifying and refining conflicting behavioral choices within a model. We illustrate these techniques by developing an asynchronous model for the lysis–lysogeny switch in phage λ.

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Notes

  1. 1.

    In particular, such systems in principle can not be modeled by synchronous Boolean networks, which are deterministic by definition.

  2. 2.

    Only an abstraction of the environment can be included into the model; such abstractions are often nondeterministic even if the environment itself is deterministic.

  3. 3.

    Note that even though the circuit is constructed of logic gates only, such gates can exhibit a nondigital behavior under certain circumstances.

  4. 4.

    One can show that safeness and consistency are preserved by the FOE transformation, as it can only reduce the set of reachable markings. Moreover, though it does not preserve the CSC property, it preserves the stronger USC property, see [23]. Since the STGs derived from Boolean networks using the construction in Fig. 5.8 are always safe (i.e., every place can contain at most one token), consistent and have USC [23], these three properties can be ignored when refining such models. However, for the manually constructed STGs they have to be checked.

  5. 5.

    Choosing a meaningful initial state is outside the scope of this paper; we just remark that typically a biological system has cyclic behavior, and that any state on this cycle can be taken.

  6. 6.

    This is very typical, as the original STG contained a lot of (rather random) behavior which is not realizable in practice.

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Acknowledgements

We would like to thank the Epsrc for supporting R. Banks and the Bbsrc for their support via the Centre for Integrated Systems Biology of Ageing and Nutrition (Cisban). This research was also supported by the Royal Academy of Engineering/Epsrc post-doctoral research fellowship EP/C53400X/1 (Davac) and the Newcastle Systems Biology Resource Centre.

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    1. Richard Banks
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    2. Victor Khomenko
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    3. L. Jason Steggles
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    Correspondence to L. Jason Steggles .

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    Editors and Affiliations

    1. Institute for Computer Science, J.W. Goethe University Frankfurt, Robert-Mayer-Straße 11-15, Frankfurt am Main, 60325, Germany

      Ina Koch

    2. Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

      Wolfgang Reisig

    3. Institute of Computer Science, Martin Luther Univ. Halle-Wittenberg, Von Seckendorff Platz 1, Halle, 06120, Germany

      Falk Schreiber

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    Banks, R., Khomenko, V., Steggles, L.J. (2011). Modeling Genetic Regulatory Networks. In: Koch, I., Reisig, W., Schreiber, F. (eds) Modeling in Systems Biology. Computational Biology, vol 16. Springer, London. https://doi.org/10.1007/978-1-84996-474-6_5

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    • DOI: https://doi.org/10.1007/978-1-84996-474-6_5

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