A Relevance Index Method to Infer Global Properties of Biological Networks

Villani, Marco; Sani, Laura; Amoretti, Michele; Vicari, Emilio; Pecori, Riccardo; Mordonini, Monica; Cagnoni, Stefano; Serra, Roberto

doi:10.1007/978-3-319-78658-2_10

Cited by 10 publications

(11 citation statements)

References 35 publications

(46 reference statements)

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“…We can therefore use the same framework for both situations. Indeed, we analyzed several systems with strong dynamical organization, by juxtaposing the states belonging to different asymptotic behaviors of the same system (different attractors of a genetic regulatory network [ 20 , 31 , 32 ] and patients affected by the same kind of disease [ 33 ]) or by observing the trajectory of a single system (a socio-economic system [ 34 ]), sometimes perturbing it (metabolic networks [ 24 ] and autocatalytic systems [ 19 , 35 ]). The performed RI analyses show some common characteristics, so in this paper we choose to expose them by commenting in detail a particular system: an autocatalytic reaction network introduced in [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

D’Addese

Sani²,

Rocca³

et al. 2021

Entropy

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The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.

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Section: Resultsmentioning

confidence: 99%

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

D’Addese

Sani²,

Rocca³

et al. 2021

Entropy

Self Cite

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“…Another type of separating method applied to objects such as graphs relies on graph-theoretic properties (e.g. [12]) shared among networks. In this category belong ways to separate graphs by indices such as edge betweenness or by the frequency of over-representation of certain subgraphs, also called network motifs [18,19], and by more sophisticated criteria such as shared graph spectral features [20,22,23,24].…”

Section: Survey Of Related Workmentioning

confidence: 99%

Causal deconvolution by algorithmic generative models

et al. 2019

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Complex data usually results from the interaction of objects produced by different generating mechanisms. Here we introduce a universal, unsupervised and parameter-free model-oriented approach, based upon the seminal concept of algorithmic probability, that decomposes an observation into its most likely algorithmic generative sources. Our approach uses a causal calculus to infer model representations. We demonstrate its ability to deconvolve interacting mechanisms regardless of whether the resultant objects are strings, space-time evolution diagrams, images or networks. While this is mostly a conceptual contribution and a novel framework, we provide numerical evidence evaluating the ability of our methods to separate data from observations produced by discrete dynamical systems such as cellular automata and complex networks. We think that these separating techniques can contribute to tackling the challenge of causation, thus complementing other statistically oriented approaches.

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“…In particular, we utilize the index called zI (see below), which makes it possible to identify, as components of a system, sets of variables that show a high degree of internal coordination. The RI methodology has been applied with interesting results to several systems: some of them had been artificially designed in order to test the effectiveness of the technique, while others referred to interesting physical, chemical, biological, or socio-economic systems [13][14][15][16].…”

Section: The Relevance Indexmentioning

confidence: 99%

“…Different methods to explore the dynamical organization of complex systems can be proposed. In this paper, we make use of one such method, which was introduced by us some years ago [10,11] and which has later been improved [12] and applied to different classes of systems [13][14][15][16]. The method identifies integrated groups of variables, by means of measures based upon information theory (the Relevance Index methodology-RI in the following), which is briefly recalled in Section 2.1 (referring the interested reader to the literature [12] for further details).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Dynamic Organization of Random and Evolved Boolean Networks

et al. 2020

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The properties of most systems composed of many interacting elements are neither determined by the topology of the interaction network alone, nor by the dynamical laws in isolation. Rather, they are the outcome of the interplay between topology and dynamics. In this paper, we consider four different types of systems with critical dynamic regime and with increasingly complex dynamical organization (loosely defined as the emergent property of the interactions between topology and dynamics) and analyze them from a structural and dynamic point of view. A first noteworthy result, previously hypothesized but never quantified so far, is that the topology per se induces a notable increase in dynamic organization. A second observation is that evolution does not change dramatically the size distribution of the present dynamic groups, so it seems that it keeps track of the already present organization induced by the topology. Finally, and similarly to what happens in other applications of evolutionary algorithms, the types of dynamic changes strongly depend upon the used fitness function.

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A Relevance Index Method to Infer Global Properties of Biological Networks

Cited by 10 publications

References 35 publications

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

Causal deconvolution by algorithmic generative models

Exploring the Dynamic Organization of Random and Evolved Boolean Networks

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