Abstract
Many complex systems, both natural and artificial, may be represented by networks of interacting nodes. Nevertheless, it is often difficult to find meaningful correspondences between the dynamics expressed by these systems and the topological description of their networks. In contrast, many of these systems may be well described in terms of coordinated behavior of their dynamically relevant parts. In this paper we use the recently proposed Relevance Index approach, based on information-theoretic measures. Starting from the observation of the dynamical states of any system, the Relevance Index is able to provide information about its organization. Moreover, we show how the application of the proposed approach leads to novel and effective interpretations in the T helper network case study.
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- 1.
The lower and upper level being constituted by the fluid particles and their global stream, by cells and the organism to which they belong, and by human beings and societies, respectively.
- 2.
In this work we do not make hypotheses about the biological plausibility (or stability or biological function, if any) of these attractors, suggesting the interested readers to refer to Mendoza and Xenarios [19] and to the references quoted therein. Rather we highlight that, once a mathematical model has been established, its structure implies the presence of a well-defined set of attractors: so, an analysis that takes into account their presence (and therefore which highlights their interrelated dynamical relationships) should provide better results than a method that does not act in this way.
- 3.
The node JAK1 is constantly inactive in all attractors. Thus, its presence is useless for the purposes of a dynamical analysis and no CRS include it. Indeed, it is active in transient states, but this kind of analysis is out of the scope of this work (see [24] for a first comparison of the results of RI application to transients and asymptotic states).
- 4.
Note that the node STAT1 participates in Group 3, one of the “sensors groups” of the Th differentiation system.
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Acknowledgments
The work of Michele Amoretti was supported by the University of Parma Research Fund - FIL 2016 - Project “NEXTALGO: Efficient Algorithms for Next-Generation Distributed Systems”.
This work greatly benefited from discussions with Andrea Roli, to whom the authors are warmly thankful.
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Villani, M. et al. (2018). A Relevance Index Method to Infer Global Properties of Biological Networks. In: Pelillo, M., Poli, I., Roli, A., Serra, R., Slanzi, D., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2017. Communications in Computer and Information Science, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-319-78658-2_10
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