Although outdegree distributions of gene regulatory networks have scale-free characteristics similar to other biological networks, indegree distributions have single-scale characteristics with significantly lower variance than that of outdegree distributions. In this study, we mathematically explain that such asymmetric characteristics arise from dynamical robustness, which is the property of maintaining an equilibrium state of gene expressions against inevitable perturbations to the networks, such as gene dysfunction and mutation of promoters. We reveal that the expression of a single gene is robust to a perturbation for a large number of inputs and a small number of outputs. Applying these results to the networks, we also show that an equilibrium state of the networks is robust if the variance of the indegree distribution is low (i.e., single-scale characteristics) and that of the outdegree distribution is high (i.e., scale-free characteristics). These asymmetric characteristics are conserved across a wide range of species, from bacteria to humans.
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http://dx.doi.org/10.1103/PhysRevE.97.062315 | DOI Listing |
J Neurosci Methods
January 2025
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena 47521, Italy.
Background: Epilepsy, characterized as a network disorder, involves widely distributed areas following seizure propagation from a limited onset zone. Accurate delineation of the epileptogenic zone (EZ) is crucial for successful surgery in drug-resistant focal epilepsy. While visual analysis of scalp electroencephalogram (EEG) primarily elucidates seizure spreading patterns, we employed brain connectivity techniques and graph theory principles during the pre-ictal to ictal transition to define the epileptogenic network.
View Article and Find Full Text PDFJ Theor Biol
December 2024
Centre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University, Stellenbosch 7602, South Africa; Mathematical Biosciences Unit, African Institute for Mathematical Sciences, Cape Town 7945, South Africa; International Initiative for Theoretical Ecology, London N1 2EE, United Kingdom. Electronic address:
Ecological networks experiencing persistent biological invasions may exhibit distinct topological properties, complicating the understanding of how network topology affects disease transmission during invasion-driven community assembly. We developed a trait-based network model to assess the impact of network topology on disease transmission, measured as community- and species-level disease prevalence. We found that trait-based feeding interactions between host species determine the frequency distribution of the niche of co-occurring species in steady-state communities, being either bimodal or multimodal.
View Article and Find Full Text PDFPhys Rev E
November 2023
ERA Chair for Cultural Data Analytics, School of Digital Technologies, Tallinn University, 10120 Tallinn, Estonia.
Undirected hyperbolic graph models have been extensively used as models of scale-free small-world networks with high clustering coefficient. Here we presented a simple directed hyperbolic model where nodes randomly distributed on a hyperbolic disk are connected to a fixed number m of their nearest spatial neighbors. We introduce also a canonical version of this network (which we call "network with varied connection radius"), where maximal length of outgoing bond is space dependent and is determined by fixing the average out-degree to m.
View Article and Find Full Text PDFMath Financ Econ
May 2023
Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL USA.
We develop a model for contagion risks and optimal security investment in a directed network of interconnected agents with heterogeneous degrees, loss functions, and security profiles. Our model generalizes several contagion models in the literature, particularly the independent cascade model and the linear threshold model. We state various limit theorems on the final size of infected agents in the case of random networks with given vertex degrees for finite and infinite-variance degree distributions.
View Article and Find Full Text PDFEntropy (Basel)
April 2023
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands.
Network controllability and its robustness have been widely studied. However, analytical methods to calculate network controllability with respect to node in- and out-degree targeted removals are currently lacking. This paper develops methods, based on generating functions for the in- and out-degree distributions, to approximate the minimum number of driver nodes needed to control directed networks, during node in- and out-degree targeted removals.
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