arXiv subjects

David Soriano Paños

Publications and source records attributed to David Soriano Paños.

2 recordsLinked to original sources

arXiv2025

Structured Interactions Drive Abrupt Transitions in the Spatial Organization of Microbial Communities

Bacteria possess diverse mechanisms to regulate their motility in response to environmental and physiological signals, enabling them to navigate complex habitats and adapt their behavior. Among these mechanisms, interspecies recognition enables cells to modulate their movement based on the ecological identity of neighboring species. Here, we introduce a model in which we assume bacterial species recognizes each other and interact via local signals that either enhance or suppress the motility of neighboring cells. Through large-scale simulations and a coarse-grained stochastic model, we demonstrate the emergence of a sharp transition driven by nucleation processes: increasing the density of motility-suppressing interactions drives the system from a fully mixed, motile phase to a state characterized by large, stationary bacterial clusters. Remarkably, in systems with a large number of interacting species, this transition can be triggered solely by altering the structure of the motility-regulation interaction matrix while maintaining species and interaction densities constant. In particular, we find that heterogeneous and modular interactions promote the transition more readily than homogeneous random ones. These results contribute to the ongoing effort to understand microbial interactions, suggesting that structured, non-random ones may be key to reproducing commonly observed spatial patterns in microbial communities.

q-bio.PE↗

arXiv2025

Quantifying edge relevance for epidemic spreading via the semi-metric topology of complex networks

Sparsification aims at extracting a reduced core of associations that best preserves both the dynamics and topology of networks while reducing the computational cost of simulations. We show that the semi-metric topology of complex networks yields a natural and algebraically-principled sparsification that outperforms existing methods on those goals. Weighted graphs whose edges represent distances between nodes are semi-metric when at least one edge breaks the triangle inequality (transitivity). We first confirm with new experiments that the metric backbone$\unicode{x2013}$a unique subgraph of all edges that obey the triangle inequality and thus preserve all shortest paths$\unicode{x2013}$recovers Susceptible-Infected dynamics over the original non-sparsified graph. This recovery is improved when we remove only those edges that break the triangle inequality significantly, i.e., edges with large semi-metric distortion. Based on these results, we propose the new semi-metric distortion sparsification method to progressively sparsify networks in decreasing order of semi-metric distortion. Our method recovers the macro- and micro-level dynamics of epidemic outbreaks better than other methods while also yielding sparser yet connected subgraphs that preserve all shortest paths. Overall, we show that semi-metric distortion overcomes the limitations of edge betweenness in ranking the dynamical relevance of edges not participating in any shortest path, as it quantifies the existence and strength of alternative transmission pathways.

physics.soc-ph↗