The Detection of Dynamical Organization in Cancer Evolution Models

Sani, Laura; D’Addese, Gianluca; Graudenzi, Alex; Villani, Marco

doi:10.1007/978-3-030-45016-8_6

Cited by 4 publications

(7 citation statements)

References 35 publications

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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%

“…In this paper, we presented a methodology for the identification of mesolevel objects, which we call relevant subsets, based on entropic measures, which may involve dynamical aspects [ 19 , 24 , 27 , 34 ] or juxtapose different realizations within a population of individuals sharing the same common organization [ 20 , 31 , 33 ]. We identified an entropic measure useful for the detection of relevant subsets and studied its theoretical distribution, a fact that helps in the interpretation of the results and allows to avoid the excessively onerous bootstrap calculations from a homogeneous system that are needed to compare groups of different size.…”

Section: Discussionmentioning

confidence: 99%

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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%

Section: Discussionmentioning

confidence: 99%

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

D’Addese

Sani²,

Rocca³

et al. 2021

Entropy

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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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“…In order to identify the Relevant Sets, we have developed the Relevance Index (RI) method [13][14][15], which is used to evaluate, for each subset of variables, how well they satisfy conditions (i) and (ii) above. Inspired by work by Tononi and Edelman [8,16,17], it makes use of some information-theoretical measures, which can be computed from the observations of the values of the system variables in different circumstances (which often, but not necessarily, correspond to different observation times) [15,[18][19][20]. By directly applying the RI method to various subsets and by ranking them according to their RI evaluations, one finds overlapping sets with similar values of the index, a condition which makes it very difficult to identify what the RSs really are.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems

et al. 2021

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In many complex systems one observes the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its better understanding. We have developed in the past a powerful method to achieve this goal, which however requires a heavy computational cost in several real-world cases. In this work we introduce a modified version of our approach, which reduces the computational burden. The design of the new algorithm allowed the realization of an original suite of methods able to work simultaneously at the micro level (that of the binary relationships of the single variables) and at meso level (the identification of dynamically relevant groups). We apply this suite to a particularly relevant case, in which we look for the dynamic organization of a gene regulatory network when it is subject to knock-outs. The approach combines information theory, graph analysis, and an iterated sieving algorithm in order to describe rather complex situations. Its application allowed to derive some general observations on the dynamical organization of gene regulatory networks, and to observe interesting characteristics in an experimental case.

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The Detection of Dynamical Organization in Cancer Evolution Models

Cited by 4 publications

References 35 publications

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

Exploring the Dynamic Organization of Random and Evolved Boolean Networks

A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems

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