Publications by authors named "Alvaro Lozano Rojo"

In this paper we introduce random proliferation models on graphs. We consider two types of particles: type-1/mutant/invader/red particles proliferates on a population of type-2/wild-type/resident/blue particles. Unlike the well-known Moran model on graphs -as introduced in Lieberman et al.

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The evolutionary dynamics of a finite population where resident individuals are replaced by mutant ones depends on its spatial structure. Usually, the population adopts the form of an undirected graph where the place occupied by each individual is represented by a vertex and it is bidirectionally linked to the places that can be occupied by its offspring. There are undirected graph structures that act as amplifiers of selection increasing the probability that the offspring of an advantageous mutant spreads through the graph reaching any vertex.

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Rent's rule is empirical power law introduced in an effort to describe and optimize the wiring complexity of computer logic graphs. It is known that brain and neuronal networks also obey Rent's rule, which is consistent with the idea that wiring costs play a fundamental role in brain evolution and development. Here we propose a method to validate this power law for a certain range of network partitions.

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Inspired by recent works on evolutionary graph theory, an area of growing interest in mathematical and computational biology, we present examples of undirected structures acting as suppressors of selection for any fitness value r > 1. This means that the average fixation probability of an advantageous mutant or invader individual placed at some node is strictly less than that of this individual placed in a well-mixed population. This leads the way to study more robust structures less prone to invasion, contrary to what happens with the amplifiers of selection where the fixation probability is increased on average for advantageous invader individuals.

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Article Synopsis
  • The concept of robustness in networks refers to their ability to maintain function despite disruptions, with this study focusing on how networks evolve from healthy to diseased nodes via site invasion rather than node removal.
  • The research examines the robustness of the US high-voltage power grid, the Internet2 academic network, and the C. elegans connectome, comparing these to both modular and non-modular benchmark networks, as well as random networks with similar degree distributions.
  • Findings reveal that for large networks, robustness is less correlated with conventional metrics and more influenced by degree distribution, while community detection methods struggle to identify complex hierarchical structures in these networks.
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