Biology subjects

Broom, M.

Publications and source records attributed to Broom, M..

3 recordsLinked to original sources

bioRxiv2026

Metabolic inhibition can enable compact physical integration of microbial partners

Microbes often generate by-products that inhibit own or consortial growth. Such imposing constraints can be alleviated by symbiotic partnerships where the partner consumes the limiting factor resulting in mutually beneficial cooperation. Theories of mitochondrial origin envisage such metabolic syntrophy of partners as the initial interaction, often coupled with mechanistic membrane gymnastics to trap the alphaproteobacterial symbiont to ensure vertical inheritance and as a prerequisite or vestibule of endosymbiosis. Yet we do not know how this happened. Here, using a mathematical model, we investigate how such metabolic self-inhibition enables and facilitates the evolution of compact physical integration between syntrophic partners. We show that when one species produces a metabolite that is self-inhibitory, selection favors increasingly tight spatial association of a detoxifying partner to mitigate local accumulation of the self-limiting product. Under a broad parameter range, this process drives the emergence of surface-associated configurations promoting morphological adaptations such as membrane protrusions or invaginations that increase inter-partner contact area. Our results prove that such costly host membrane contortions can evolve as a means to improve benefits of inhibition reduction by protective syntrophic ectosymbionts. Additionally, a unilateral syntrophic interaction between parties is sufficient for the transition; a mutualistic (or bilateral) one is not necessary. Our model establishes a direct evolutionary link between metabolic coupling and physical integration, suggesting that the need to alleviate (self-generated) inhibition can be a primary driver of ectosymbiotic attachment and its transition toward more intimate associations. Our findings provide a general theoretical framework for understanding how microbial consortia evolve structural complexity from initially loose metabolic interactions, with implications for the origins of stable symbioses and the early steps toward cellular integration.

evolutionary biology↗

bioRxiv2025

Eco-Evolutionary game dynamics with Three Strategies: how geometric analysis provides insights into stability

In this paper we extend a modeling framework that embeds ecological realism by examining how demographic factors determine payoffs, which are subsequently incorporated into evolutionary game models. In particular, it segregates births and deaths, as well as activities in addition to the between player interactions central to the classical theory. In the standard examples of evolutionary games so far considered such as the Hawk-Dove game, a two strategy population has been studied. The above demographic models are extended from two strategies to three strategies, giving a thorough analysis of the mechanistic framework, which lays down the necessary conditions on the existence of steady states for a constrained system. We show that population trajectories are drawn toward the density manifold by logistic suppression, which in turn structures the interaction between frequency dynamics and fixed points. We also introduce a novel approach which helps derive stability from geometric insights through phase space elements (nullclines and their intersections) and streamlines calculations by decomposing gradients into the components that contribute to directional change, allowing dynamic and stability conditions to be more easily interpreted directly from the figures.

evolutionary biology↗

bioRxiv2021

A Markov chain model of cancer treatment

This paper develops and analyzes a Markov chain model for the treatment of cancer. Cancer therapy is modeled as the patients Markov Decision Problem, with the objective of maximizing the patients discounted expected quality of life years. Patients choose the number of treatment rounds they wish to administer based on the progression of the disease as well as their own preferences. We obtain a powerful analytic decision tool by which patients may select their preferred treatment strategy. In a second model patients may make choices on the timing of treatment rounds as well. By delaying a round of therapy the patient forgoes the gains of therapy for a time in order to delay its side effects. We obtain an analytic tool that allows numerical approximations of the optimal times of delay.

cancer biology↗