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Modelling the Influence of Cell Signaling on the Dynamics of Gene Regulatory Networks

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Biomechanics of Cells and Tissues

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 9))

Abstract

Boolean models have proven to be effective in capturing some features of the dynamical behavior of the gene regulatory network of isolated cells. Cells are however constantly exposed to several signals that affect the regulation of their genes and are therefore not isolated. Moreover, cells in multi-cellular organisms and, to some extent, also in colonies of unicellular ones modify their gene expression profiles in a coordinated fashion. Many of these processes are controlled by cell–cell communication mechanisms. It appears therefore important to understand how the interplay among gene regulatory networks, by means of the signaling network, may alter their dynamical properties. In order to explore the issue, a model based on interconnected identical Boolean networks has been proposed, which has allowed to investigate the influence that cell-signaling may have on the expression patterns of individual cells, with particular regard on their variety and homeostasis. The main results described in this chapter show that both the diversity of emergent behaviors and the diffusion of perturbations may not depend linearly on the fraction of genes involved in the signaling network. On the contrary, when cells exchange a moderate quantity of signals with neighbors, the variety of their activation patterns is maximized, together with the number of genes that can be damaged as a consequence of a minor alteration of the system.

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Notes

  1. 1.

    Although different threshold levels for different genes would be plausible from the biological point of view, in our study a fixed \(\tau \) has been used in order to isolate the effect of the choice of a given \(\tau \) on the dynamics of the system.

  2. 2.

    Due to the inseparability and reciprocity of receptor/signaling-molecule pairs, they are considered as a unit while counting the number \(n\) of nodes in the graph: \(n=g+r\).

  3. 3.

    It should be noted that the signaling-molecule does not influence the cell where it is produced.

  4. 4.

    This number depends on the average connectivity of genes and molecules. It is assumed that the two entities have the same typical connectivity \(K\).

  5. 5.

    For every realization of a MRBN made of \(5\times 5\) cells with 100 nodes each, 150 experiments have been performed. In each experiment the system has been simulated for the 10 values of the coupling strength. For each coupling level the system is allowed to evolve from a given initial condition until an attractor is found. The 150 experiments differ therefore from one another in the initial activation of the nodes and in the selection of the genes to be turned into shared-nodes/receptors. Unless otherwise stated, the average value of the measures described in the next sections are obtained over 100 MRBNs and over these 150 experiments. Only MRBNs that reach an attractor within the computational limit of 2500 steps, in all the experiments and for all the level of \(\chi \) under study, are included in the sample. The fraction of MRBNs that are removed from the sample can vary for different updating schemes. The highest case observed is about \(0.30\).

  6. 6.

    For each MRBN 150 experiments are performed differing from each other for the initial activation of the nodes, for the selection of the genes to be turned into shared-nodes/receptors at each considered coupling strength and for the choice of the node to be perturbed. Unless otherwise stated, the average values \(\langle A_{nodes}(T) \rangle \) and \(\langle A_{cells}(T) \rangle \) are obtained over 100 MRBNs and over such 150 experiments. The study here presented is once again limited to the analysis of \(5 \times 5\) MRBNs (i.e. \(m=25\)). Cells are made of 100 nodes.

  7. 7.

    It is however reminded that the MRBN level may be regarded either as an organ or an entire organism, or a bacteria colony and so on.

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  1. COSBI The Microsoft Research—University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, 38068 , Rovereto, Italy

    Chiara Damiani

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  1. Centre for Computational Mechanics, Microsoft Research lab, University of Trento, Piazza Manci 17, Trento, 38123, Italy

    Paola Lecca

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Damiani, C. (2013). Modelling the Influence of Cell Signaling on the Dynamics of Gene Regulatory Networks. In: Lecca, P. (eds) Biomechanics of Cells and Tissues. Lecture Notes in Computational Vision and Biomechanics, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5890-2_5

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  • DOI: https://doi.org/10.1007/978-94-007-5890-2_5

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