Biology subjects

Avila, P.

Publications and source records attributed to Avila, P..

5 recordsLinked to original sources

bioRxiv2026

A life history model of indeterminate growth, somatic maintenance, and negative senescence

Some organisms exhibit declining mortality and increasing fecundity following sexual maturity, a demographic pattern known as negative senescence. According to life history theory, ageing occurs because resources are preferentially allocated to reproduction over somatic maintenance. Models connecting indeterminate growth to negative senescence exist, but none integrate somatic maintenance as a competing allocation decision alongside growth and reproduction. We formulate a life history model in which an individual allocates energy among reproduction, somatic growth, and somatic maintenance and mortality rate depends on both body size and somatic damage. We show that negative actuarial senescence, whereby mortality declines with age, occurs when the proportional change in reproductive value exceeds the proportional change in fitness returns from current investments into reproduction and soma. We derive the necessary conditions for an uninvadable allocation strategy using invasion analysis and Pontryagin's maximum principle, and examine biologically relevant cases numerically. We show that both negative senescence and indeterminate growth arise together as uninvadable outcomes even when maintenance competes for the same resources as growth and reproduction. We show that both diminishing returns to reproduction and diminishing returns to growth can give rise to negative senescence. These results extend the disposable soma theory to organisms with indeterminate growth, in which mortality decreases with size, and identify key mechanisms for the empirically observed association between indeterminate growth and non-senescent demographic trajectories.

evolutionary biology↗

bioRxiv2026

Assessing the translation of AI-prioritized genome-derived peptide fragments into validated antimicrobial candidates

The emergence of antibiotic-resistant pathogens such as Staphylococcus aureus demands accelerated antimicrobial discovery strategies. Artificial intelligence (AI) enables large-scale inference of candidate antimicrobial peptides (AMPs), yet experimental validation remains essential to determine whether predictions translate into biological function. Genome-guided mining, rather than unconstrained or randomly generated sequence exploration, offers a biologically grounded search space derived from organisms shaped by ecological and evolutionary pressures. Here, we evaluate this principle using Malassezia furfur, a skin-associated yeast that coexists with bacterial colonizers such as S. aureus, as a genomic source for AI-prioritized antimicrobial candidates. Candidate fragments were generated from two M. furfur genomes, filtered by physicochemical properties, prioritized with deep-learning AMP predictors, synthesized, and experimentally characterized. Selected peptides underwent cross-kingdom antimicrobial screening against S. aureus, combining kinetic growth and ultrastructural assays, complemented by in silico structural prediction, lipid-membrane interaction analysis, and human keratinocyte cytotoxicity evaluation. AI-guided genomic mining enriched biologically motivated sequence space for peptides with measurable antimicrobial activity, while revealing biases and generalizability limits of AI-based AMP inference. Closing the loop between genome-derived candidate generation, AI-based inference, synthesis, and functional characterization, this study provides an experimental assessment of model-guided AMP discovery and a reproducible route from computational prediction to validated antimicrobial candidates.

bioengineering↗

bioRxiv2022

Life-history and deleterious mutation rate coevolution

The cost of germline maintenance gives rise to a trade-off between lowering the deleterious mutation rate and investing in life history functions. Therefore, life history and the mutation rate coevolve, but this coevolution is not well understood. We develop a mathematical model to analyse the evolution of resource allocation traits, which simultaneously affect life history and the deleterious mutation rate. First, we show that the invasion fitness of such resource allocation traits can be approximated by the basic reproductive number of the least-loaded class; the expected lifetime production of offspring without deleterious mutations born to individuals without deleterious mutations. Second, we apply the model to investigate (i) the coevolution of reproductive effort and germline maintenance and (ii) the coevolution of age-at-maturity and germline maintenance. This analysis provides two resource allocation predictions when exposure to environmental mutagens is higher. First, selection favours higher allocation to germline maintenance, even if it comes at the expense of life history functions, and leads to a shift in allocation towards reproduction rather than survival. Second, life histories tend to be faster, characterized by individuals with shorter lifespans and smaller body sizes at maturity. Our results suggest that mutation accumulation via the cost of germline maintenance can be a major force shaping life-history traits.

evolutionary biology↗

bioRxiv2020

Hamilton's rule, gradual evolution, and the optimal (feedback) control of reaction norms and other function-valued traits

Most traits expressed by organisms, such as gene expression profiles, developmental trajectories, behavioural sequences and reaction norms are function-valued traits (colloquially "phenotypically plastic traits"), since they vary across an individuals age and in response to various internal and/or external factors (state variables). Furthermore, most organisms live in populations subject to limited genetic mixing and are thus likely to interact with their relatives. We here formalise selection on genetically determined function-valued traits of individuals interacting in a group-structured population, by deriving the marginal version of Hamiltons rule for function-valued traits. This rule simultaneously gives a condition for the invasion of an initially rare mutant function-valued trait and its ultimate fixation in the population (invasion thus implies substitution). Hamiltons rule thus underlies the gradual evolution of function-valued traits and gives rise to necessary first-order conditions for their uninvadability (evolutionary stability). We develop a novel analysis using optimal control theory and differential game theory, to simultaneously characterise and compare the first-order conditions of (i) open-loop traits - functions of time (or age) only, and (ii) closed-loop (state-feedback) traits - functions of both time and state variables. We show that closed-loop traits can be represented as the simpler open-loop traits when individuals do no interact or when they interact with clonal relatives. Our analysis delineates the role of state-dependence and interdependence between individuals for trait evolution, which has implications to both life-history theory and social evolution.

evolutionary biology↗

bioRxiv2018

Sex allocation conflict and sexual selection throughout the lifespan of eusocial colonies

Models of sex allocation conflict are central to evolutionary biology but have mostly assumed static decisions, where resource allocation strategies are constant over colony lifespan. Here, we develop a model to study how the evolution of dynamic resource allocation strategies is affected by the queen-worker conflict in annual eusocial insects. We demonstrate that the time of dispersal of sexuals affects the sex allocation ratio through sexual selection on males. Furthermore, our model provides three predictions that depart from established results of classic static allocation models. First, we find that the queen wins the sex allocation conflict, while the workers determine the maximum colony size and colony productivity. Second, male-biased sex allocation and protandry evolve if sexuals disperse directly after eclosion. Third, when workers are more related to new queens, then the proportional investment into queens is expected to be lower, which results from the interacting effect of sexual selection (selecting for protandry) and sex allocation conflict (selecting for earlier switch to producing sexuals). Overall, we find that colony ontogeny crucially affects the outcome of sex-allocation conflict because of the evolution of distinct colony growth phases, which decouples how queens and workers affect allocation decisions and can result in asymmetric control.

evolutionary biology↗