arXiv subjects

Saúl Ares

Publications and source records attributed to Saúl Ares.

6 recordsLinked to original sources

arXiv2026

Optimization of sequential therapies to maximize extinction of resistant bacteria through collateral sensitivity

Antimicrobial resistance (AMR) threatens global health. A promising and underexplored strategy to tackle this problem is sequential therapies exploiting collateral sensitivity (CS), whereby resistance to one drug increases sensitivity to another. Here, we develop a four-genotype stochastic birth-death model with two bacteriostatic antibiotics to identify switching periods that maximize bacterial extinction under subinhibitory concentrations. We show that extinction probability depends nonlinearly on switching period, with stepwise increases aligned to discrete switch events: fast sequential therapies are suboptimal as they do not allow for the evolution of resistance, a key ingredient in these therapies. A geometric distribution framework accurately predicts cumulative extinction probabilities, where the per-switch extinction probability rises with switching period. We further derive a heuristic approximation for the extinction probability based on times to fixation of single-resistant mutants. Sensitivity analyses reveal that strong reciprocal CS is required for this strategy to work, and we explore how increasing antibiotic doses and higher mutation rates modulate extinction in a nonmonotonic manner. Finally, we discuss how longer therapies maximize extinction but also cause higher resistance, leading to a Pareto front of optimal switching periods. Our results provide quantitative design principles for in vitro and clinical sequential antibiotic therapies, underscoring the potential of CS-guided regimens to suppress resistance evolution and eradicate infections.

q-bio.PE↗

arXiv2025

Optimal COVID-19 vaccine prioritization by age depends critically on inter-group contacts and vaccination rates

The limited availability of COVID-19 vaccines has prompted extensive research on optimal vaccination strategies. Previous studies have considered various non-pharmaceutical interventions, vaccine efficacy, and distribution strategies. In this work, we address the combined effects of inter-group contacts and vaccination rates under contact reduction, analyzing the Spanish population's demographic and age group contact patterns and incorporating reinfection dynamics. We conduct an exhaustive analysis, evaluating 362,880 permutations of 9 age groups across 6 vaccination rates and two distinct, empirically quantified scenarios for social contacts. Our results show that at intermediate-to-high vaccination rates with unrestricted social contacts, optimal age-based vaccination strategies only slightly deviate from older-to-younger prioritization, yielding marginal reductions in deaths and infections. However, when significant reductions in social contacts are enforced -similar to the lockdowns in 2020-, there are substantial improvements, particularly at moderate vaccination rates. These restrictions lead to a transition where infection propagation is halted, a scenario that became achievable during the pandemic with the observed vaccination rates. Our findings emphasize the importance of combining appropriate social contact reductions with vaccination to optimize age-based vaccination strategies, underscoring the complex, nonlinear dynamics involved in pandemic dynamics and the necessity for tailored, context-specific interventions.

q-bio.PE↗

arXiv2024

Non-equilibrium microbial dynamics unveil a new macroecological pattern beyond Taylor's law

We introduce a comprehensive analytical benchmark, relying on Fokker-Planck formalism, to study microbial dynamics in presence of both biotic and abiotic forces. In equilibrium, we observe a balance between the two kinds of forces, leading to no correlations between species abundances. This implies that real microbiomes, where correlations have been observed, operate out of equilibrium. Therefore, we analyze non-equilibrium dynamics, presenting an ansatz for an approximate solution that embodies the complex interplay of forces in the system. This solution is consistent with Taylor's law as a coarse-grained approximation of the relation between species abundance and variance, but implies subtler effects, predicting unobserved structure beyond Taylor's law. Motivated by this theoretical prediction, we refine the analysis of existing metagenomic data, unveiling a novel universal macroecological pattern. Finally, we speculate on the physical origin of Taylor's law: building upon an analogy with Brownian motion theory, we propose that Taylor's law emerges as a Fluctuation-Growth relation resulting from equipartition of environmental resources among microbial species.

q-bio.PE↗

arXiv2024

Mathematical models of the Arabidopsis circadian oscillator

We review the construction and evolution of mathematical models of the Arabidopsis circadian clock, structuring the discussion into two distinct historical phases of modeling strategies: extension and reduction. The extension phase explores the bottom-up assembly of regulatory networks introducing as many components and interactions as possible in order to capture the oscillatory nature of the clock. The reduction phase deals with functional decomposition, distilling complex models to their essential dynamical repertoire. Current challenges in this field, including the integration of spatial considerations and environmental influences like light and temperature, are also discussed. The review emphasizes the ongoing need for models that balance molecular detail with practical simplicity.

physics.bio-ph↗

arXiv2020

Predictability: Can the turning point and end of an expanding epidemic be precisely forecast?

No, they can't. Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. An SIR model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slow-down in the number of newly infected individuals during the expansion phase allows to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the mid-term is not due to wrong or incomplete data, since it persists in error-free, synthetically produced data sets, and does not necessarily improve by using larger data sets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective or phenomenological models, and supports that only probabilities of different outcomes can be confidently given.

q-bio.PE↗

arXiv2018

Synchronization Dynamics in the Presence of Coupling Delays and Phase Shifts

In systems of coupled oscillators, the effects of complex signaling can be captured by time delays and phase shifts. Here, we show how time delays and phase shifts lead to different oscillator dynamics and how synchronization rates can be regulated by substituting time delays by phase shifts at constant collective frequency. For spatially extended systems with time delays, we show that fastest synchronization can occur for intermediate wavelengths, giving rise to novel synchronization scenarios.

nlin.AO↗