Biology subjects

Gjini, E.

Publications and source records attributed to Gjini, E..

17 recordsLinked to original sources

bioRxiv2026

Integrating Bottleneck Size into Selection Tests for Biological Diversity Data

Population bottlenecks profoundly shape genetic diversity, but distinguishing stochastic drift from selective pressure requires precise estimation and accounting for bottleneck size. While deep-sequencing data enable inference via frameworks like beta-binomial modeling, integrating these estimates directly into selection tests remains a critical challenge. In this study, based on existing computational approaches, we propose a new method that explicitly incorporates bottleneck size estimates into neutrality tests for biological diversity data. Designed for variant frequency data, our framework accounts for sequencing errors and sampling biases to improve the precision and interpretability of selection signature detection. We validate this framework using previously published Streptococcus pneumoniae in vivo experimental data, successfully replicating established fitness results, while uncovering novel genes relevant to infection and pathogenesis. This integrated new model with explicit bottleneck effects narrows down the set of candidate genes under selection and provides a robust, generalizable tool for disentangling drift from selection across a wide range of biological systems.

genetics↗

bioRxiv2026

Modeling the human vaginal microbiome and its protection against pathogens using the replicator framework for invasion

The human vaginal microbiota plays a central role in protecting against urogenital infections, including bacterial vaginosis, yeast infections, HIV, and urinary tract infections. However, the ecological mechanisms connecting microbial community structure to clinical indicators such as Nugent scores remain poorly understood. Although machine-learning approaches can accurately predict bacterial vaginosis (BV) from microbiota profiles, they provide limited biological insight. Here, we introduce a mechanistic framework that links vaginal microbiota composition to Nugent score and identifies key ecological interactions underlying health and disease-associated community states. We analyzed microbiota data from an already published North American women cohort, aggregating taxa at the phylum level to improve stability and interpretability. Using a replicator model, we quantified both the direct contributions of individual phyla and nonlinear effects arising from pairwise interactions, capturing transitions among healthy, intermediate, and BV-positive states. The model predicted BV-positive status with 92% accuracy for the 394 women in the study, on par with machine-learning benchmarks. More importantly, it provides a clear ecological interpretation of BV-associated community change. Beyond the BV setting, the model itself illustrates a general proof-of-concept for microbiota-invader links via the replicator formalism.

microbiology↗

bioRxiv2026

How host contact network impacts N -strain SIS dynamics with coinfection via a global replicator equation

Dynamical processes on complex networks have a long history of study with increasing applications across many fields. While epidemics in heterogeneous networks have received much attention in terms of how connectivity patterns drive epidemic outbreaks, affect critical thresholds, timescales, final outbreak size and immunization efforts, less attention has been devoted to endemic multi-strain scenarios and questions of selection and coexistence dynamics. Here, we provide an SIS framework for multiple co-circulating strains and co-infection, which can be reduced to a replicator dynamics on a host contact network. Using the analytical tractability of the replicator formalism, we study how network heterogeneity affects multi-strain dynamics, and compare its effects relative to the homogeneous contact distribution, identifying key relevant metrics for comparison. In particular, the pairwise invasion fitness matrix comparison reveals that higher network heterogeneity acts to increase the speed of multi-strain dynamics and typically tends to have stabilizing effects that reduce the number of coexisting strains. While many aspects of the replicator dynamics remain complex to study, especially for high number of strains, the advantage of this model representation lies in the dimensional reduction of a huge system, enabling general, more direct and efficient numerical computations. Furthermore the explicit bottom-up constitution of crucial parameters yields biological and epidemiological insight for critical system transitions across macroscopic gradients and can be used to guide interventions.

biophysics↗

bioRxiv2025

Multi-strain SIS dynamics with coinfection across heterogeneous patches

We study a Susceptible-Infected-Susceptible (SIS) model with coinfection and multiple interacting strains where hosts move between a set of inter-connected patches. Under strain similarity and slow migration, we obtain a discrete model, following the corresponding continuous space model derived in (Le and Madec, 2023). In this model, the fast variables are total prevalence of susceptibles, single-infected and co-infected hosts in each patch (S, I, D). The slow variables are strain frequencies (z) in each patch. These local strain frequencies in each patch are governed by a replicator-like equation, where an additional contribution arises from migration, scaled explicitly by emergent patch heterogeneity. In our model, the strains can vary epidemiologically within- and between-patches along several traits, including transmission rates, clearance rates, priority effects and pairwise susceptibilities to coinfection; a complexity that totally defies straightforward prediction of their ecological outcome. However, harnessing the analytical advantage of this model reduction, we can investigate several key scenarios for the outcome of the coupled P-patch N-strain system. We focus on the global and local factors that promote or hinder coexistence, and on regimes that favour spatial segregation of strains, or ultimately competitive exclusion. The analytical tractability of this meta-population model, illustrated with several examples for N = 2 strains and 2-3 patches, makes it a useful framework for application to general multi-species co-colonization systems with migration across heterogeneous environments.

ecology↗

bioRxiv2024

Linking spatial drug heterogeneity to microbial growthdynamics in theory and experiment

Diffusion and migration play pivotal roles in microbial communities - shaping, for example, colonization in new environments and the maintenance of spatial structures of biodiversity. While previous research has extensively studied free diffusion, such as range expansion, there remains a gap in understanding the effects of biologically or physically deleterious confined environments. In this study, we examine the interplay between migration and spatial drug heterogeneity within an experimental meta-community of E. faecalis, a Gram-positive opportunistic pathogen. When the community is confined to spatially-extended habitats ( islands) bordered by deleterious conditions, we find that the population level response depends on the trade-off between the growth rate within the island and the rate of transfer into regions with harsher conditions, a phenomenon we explore by modulating antibiotic concentration within the island. In heterogeneous islands, composed of spatially patterned patches that support varying levels of growth, the populations fate depends critically on the specific spatial arrangement of these patches - the same spatially averaged growth rate leads to diverging responses. These results are qualitatively captured by simple simulations, and analytical expressions which we derive using first-order perturbation approximations to reaction-diffusion models with explicit spatial dependence. Among all possible spatial arrangements, our theoretical and experimental findings reveal that the arrangement with the highest growth rates at the center most effectively mitigates population decline, while the arrangement with the lowest growth rates at the center is the least effective. Extending this approach to more complex experimental communities with varied spatial structures, such as a ring-structured community, further validates the impact of spatial drug arrangement. Our findings suggest new approaches to interpreting diverging clinical outcomes when applying identical drug doses and inform the possible optimization of spatially-explicit dosing strategies. Author summaryIn this study, we develop an automated platform to experimentally investigate short-term population growth and migration dynamics under spatial drug heterogeneity. Our findings reveal that the collective spatial response of the population can vary significantly, even with the same migration rate and averaged drug dose, due to different spatial drug arrangements. By constructing a simple reaction-diffusion model, we observed that simulated short-term spatial growth rate closely matches the experimental data. Furthermore, this short-term spatial growth rate aligns well with the long-term spatial growth rate, defined by the largest eigenvalue, as the spatial system quickly enters the equilibrium growth state. Using concepts from perturbation theory, we derived an analytical relationship between the boundary diffusion effect, homogeneous growth effect, and heterogeneous effect. Our results highlight that in spatially-extended habitats, the spatial growth response is an emergent property. The bacterial population quickly enters equilibrium growth, suggesting that the spatial growth rate measured at an ecological scale may be used to predict resistance evolutionary behavior.

biophysics↗

bioRxiv2024

Unpacking fitness differences between two invaders in a multispecies context

Ecosystems are constantly exposed to newcoming strains or species. Which newcomer will be able to invade a resident multi-species community depends on the invaders relative fitness. Classical fitness differences between two growing strains are measured using the exponential model. Here we complement this approach, developing a more explicit framework to quantify fitness differences between two co-invading strains, based on the replicator equation. By assuming that the resident species frequencies remain constant during the initial phase of invasion, we are able to determine the invasion fitness differential between the two strains, which drives growth rate differences post-invasion. We then apply our approach to a critical current global problem: invasion of the gut microbiota by antibiotic-resistant strains of the pathobiont Escherichia coli, using previously-published data. Our results underscore the context-dependent nature of fitness and demonstrate how species frequencies in a host environment can explicitly modulate the selection coefficient between two strains. This mechanistic framework can be augmented with machine-learning algorithms and multi-objective optimization to predict relative fitness in new environments, to steer selection, and design strategies to lower resistance levels in microbiomes.

ecology↗

bioRxiv2024

Nested inference of pairwise interactions from strain frequency data across settings and context-dependent mutual invasibilities

We propose a method to estimate pairwise strain interactions from population-level frequencies across different endemic settings. We apply the framework of replicator dynamics, derived from a multi-strain SIS model with co-colonization, to extract from 5 datasets the fundamental backbone of strain interactions. In our replicator, each pairwise invasion fitness explicitly arises from local environmental context and trait variations between strains. We adopt the simplest formulation for multi-strain coexistence, where context is encoded in basic reproduction number R0 and mean global susceptibility to co-colonization k, and trait variations ij capture pairwise deviations from k. We integrate Streptococcus pneumoniae serotype frequencies and serotype identities collected from 5 environments: epidemiological surveys in Denmark, Nepal, Iran, Brazil and Mozambique, and mechanistically link their distributions. Our results have twofold implications. First, we offer a new proof-of-concept in the inference of multi-species interactions based on cross-sectional data. We also discuss 2 key aspects of the method: the site ordering for sequential fitting, and stability constraints on the dynamics. Secondly, we effectively estimate at high-resolution more than 70% of the 92 x 92 pneumococcus serotype interaction matrix in co-colonization, allowing for further projections and hypotheses testing. We show that in these bacteria both within- and between- serotype interaction coefficients distribution emerge to be unimodal, their difference in mean broadly reflecting stability assumptions on serotype coexistence. This framework enables further model calibration to global data: cross-sectional across sites, or longitudinal in one site over time, - and should allow a more robust and integrated investigation of intervention effects in such biodiverse ecosystems.

ecology↗

bioRxiv2024

MetaSpread: A cancer growth and metastatic spread simulation program in Python

We develop and provide MetaSpread, an open-source simulation package and interactive program in Python for tumor growth and metastatic spread, based on a mathematical model by Franssen et al. (2019). This paper proposed a hybrid modeling and computational framework where cellular growth and metastatic spread are described and simulated in a spatially explicit manner, accounting for stochastic individual cell dynamics and deterministic dynamics of abiotic factors. This model incorporates several key processes such as the growth and movement of epithelial and mesenchymal cells, the role of the extracellular matrix, diffusion, haptotaxis, circulation and survival of cancer cells in the vasculature, and seeding and growth in secondary sites. In the software that we develop, these growth and metastatic dynamics are programmed using MESA, a Python Package for Agent-based modeling (Masad & Kazil, 2015).

bioengineering↗

bioRxiv2023

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

evolutionary biology↗

bioRxiv2021

Disentangling how multiple traits drive 2 strain frequencies in SIS dynamics with coinfection

A general theory for competitive dynamics among many strains at the epidemiological level is required to understand polymorphisms in virulence, transmissibility, antibiotic resistance and other biological traits of infectious agents. Mathematical coinfection models have addressed specific systems, focusing on the criteria leading to stable coexistence or competitive exclusion, however, due to their complexity and nonlinearity, analytical solutions in coinfection models remain rare. Here we study a 2-strain SIS compartmental model with co-infection/co-colonization, incorporating multiple fitness dimensions under the same framework: variation in transmissibility, duration of carriage, pairwise susceptibilities to coinfection, coinfection duration, and transmission priority effects from mixed coinfection. Taking advantage of a singular perturbation approach, under the assumption of strain similarity, we expose how strain dynamics on a slow timescale are explicitly governed by a replicator equation which encapsulates all traits and their interplay. This allows us to predict explicitly not only the final epidemiological outcome of a given 2-player competition, but moreover, their entire frequency dynamics as a direct function of their relative variation and of strain-transcending global parameters. Based on mutual invasion fitnesses, we analyze and report rigorous results on transition phenomena in the 2-strain system, strongly mediated via coinfection prevalence. We show that coinfection is not always a promoter of coexistence; instead, its effect to favour or prevent polymorphism is non-monotonic and depends on the type and level of phenotypic differentiation between strains. This framework offers a deeper analytical understanding of 2-strain competitive games in coinfection, with theoretical and practical applications in epidemiology, ecology and evolution.

ecology↗

bioRxiv2021

Towards a mathematical understanding of colonization resistance

Microbial community composition and dynamics are key to health and disease. Explaining the forces generating and shaping diversity in the microbial consortia making up our bodys defenses is a major aim of current research in microbiology. For this, tractable models are needed, that bridge the gap between observations of patterns and underlying mechanisms. While most microbial dynamics models are based on the Lotka-Volterra framework, we still do not have an analytic quantity for colonization resistance, by which a microbial systems fitness as a whole can be understood. Here, inspired by an epidemiological perspective, we propose a rather general modeling framework whereby colonization resistance can be clearly mathematically defined and studied. In our model, N similar species interact with each other through a co-colonization interaction network encompassing pairwise competition and cooperation, abstractly mirroring how organisms effectively modify their micro-scale environment in relation to others. This formulation relies on a generic notion of shared resources between members of a consortium, yielding explicit frequency-dependent dynamics among N species, in the form of a replicator equation, and offering a precise definition of colonization resistance. We demonstrate that colonization resistance arises and evolves naturally in a multispecies system as a collective quadratic term in a replicator equation, describing dynamic mean invasion fitness. Each pairwise invasion growth rate between two ecological partners, [Formula], is derived explicitly from species asymmetries and mean traits. This makes the systemic colonization resistance [Formula] also an emergent function of global mean-field parameters and trait variation architecture, weighted by the evolving relative abundances among species. In particular, if the underlying invasion fitness matrix {Lambda} displays species-specific invasiveness or invasibility structure, colonization resistance will be insensitive to mean micro-scale cooperation or competition. However, in general, colonization resistance depends on and may undergo critical transitions with changes in mean environment, e.g. cooperation and growth level in a community. We illustrate several key links between our proposed measure of colonization resistance and invader success, including sensitivity to timing, and to the intrinsic pairwise invasion architecture of the resident community. Our simulations reveal that symmetric and invader-driven mutual invasion among resident species tend to maximize systemic colonization resistance to outsiders, when compared to resident-driven, anti-symmetric, almost anti-symmetric and random {Lambda} structures. We contend this modeling approach is a powerful new avenue to study, test and validate interaction networks and invasion topologies in diverse microbial consortia, and quantify analytically their role in colonization resistance, system function, and invasibility.

microbiology↗

bioRxiv2020

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction, DI) and how resistance to one drug impacts resistance to the other (collateral effects, CE). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug sensitive (ancestral) and drug resistant (mutant) populations. We show that evolved resistance to the component drugs-and in turn, the adaptation of growth rate-is governed by a Price equation whose covariance terms encode geometric features of both the two-drug response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. Our results clarify the complex relationship between drug interactions and collateral effects in multi-drug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally-explicit selective outcomes.

evolutionary biology↗

bioRxiv2020

The key to complexity in interacting systems with multiple strains

Ecological community structure, persistence and stability are shaped by multiple forces, acting on multiple scales. These include patterns of resource use and limitation, spatial heterogeneities, drift and migration. Pathogen strains co-circulating in a host population are a special type of an ecological community. They compete for colonization of susceptible hosts, and sometimes interact via altered susceptibilities to co-colonization. Diversity in such pairwise interaction traits enables the multiple strains to create dynamically their niches for growth and persistence, and engineer their common environment. How such a network of interactions with others mediates collective coexistence remains puzzling analytically and computationally difficult to simulate. Furthermore, the gradients modulating stability-complexity regimes in such multi-player systems remain poorly understood. In a recent study, we presented an analytic framework for N-type coexistence in an SIS epidemiological system with co-colonization interactions. The multi-strain complexity was reduced from O(N2) dimensions of population structure to only N equations for strain frequency evolution on a long timescale. Here, we examine the key drivers of coexistence regimes in such a system. We find the ratio of single to co-colonization critically determines the type of equilibrium for multi-strain dynamics. This key quantity in the model encodes a trade-off between overall transmission intensity R0 and mean interaction coefficient in strain space k. Preserving a given coexistence regime, under fixed trait variation, can only be achieved from a balance between higher competition in favourable environments, and higher cooperation in harsher environments, consistent with the stress gradient hypothesis in ecology. Multi-strain coexistence regimes are more stable when is small, whereas as increases, dynamics tends to increase in complexity. There is an intermediate ratio that maximizes the existence and stability of a unique coexistence equilibrium between strains. This framework provides a foundation for linking invariant principles in collective coexistence across biological systems, and for understanding critical shifts in community dynamics, driven by simple and random pairwise interactions but potentiated by mean-field and environmental gradients.

ecology↗

bioRxiv2019

Cell competition regulates the kinetics of thymopoiesis and thymus cellularity

Cell competition in the thymus is a homeostatic process that drives turnover. If the process is impaired, thymopoiesis can be autonomously maintained for several weeks, but this causes leukemia. We aimed to understand the impact of cell competition on thymopoiesis, identify the cells involved and determine how the process is regulated. Using thymus transplantation experiments we found that cell competition occurs within the double negative 2 (DN2) and 3 early (DN3e) thymocytes and inhibits thymus autonomy. Furthermore, the expansion of DN2b is regulated by a negative feedback loop imposed by double positive thymocytes and determines the kinetics of thymopoiesis. This feedback loop impacts on cell cycle duration of DN2b, in a response controlled by interleukin 7 availability. Altogether, we show that thymocytes do not merely follow a pre-determined path if provided with the correct signals. Instead, thymopoiesis dynamically integrates cell autonomous and non-cell autonomous aspects that fine-tune normal thymus function.

immunology↗

bioRxiv2019

Co-colonization interactions drive explicit frequency-dependent dynamics among N types

Multi-type spreading processes are ubiquitous in ecology, epidemiology and social systems, but remain hard to model mathematically and to understand on a fundamental level. Here, we describe and study a multi-type susceptible-infected-susceptible (SIS) model that allows for up to two co-infections of a host. Fitness differences between N infectious agents are mediated through altered susceptibilities to secondary infections that depend on colonizer- co-colonizer interactions. By assuming small differences between such pairwise traits (and other infection parameters equal), we derive a model reduction framework using separation of timescales. This quasi-neutrality in strain space yields a fast timescale where all types behave as neutral, and a slow timescale where non-neutral dynamics take place. On the slow timescale, N equations govern strain frequencies and accurately approximate the dynamics of the full system with O(N2) variables. We show that this model reduction coincides with a special case of the replicator equation, which, in our system, emerges in terms of the pairwise invasion fitnesses among strains. This framework allows to build the multi-type community dynamics bottom-up from only pairwise outcomes between constituent members. We find that mean fitness of the multi-strain system, changing with individual frequencies, acts equally upon each type, and is a key indicator of system resistance to invasion. Besides efficient computation and complexity reduction, these results open new perspectives into high-dimensional community ecology, detection of species interactions, and evolution of biodiversity, with applications to other multi-type biological contests. By uncovering the link between an epidemiological system and the replicator equation, we also show our co-infection model relates to Fishers fundamental theorem and to conservative Lotka-Volterra systems.

ecology↗

bioRxiv2019

Mathematical modeling suggests that benefits of short or long antibiotic treatment depend on details of infection

Antibiotics are the major tool for treating bacterial infections. With rising antibiotic resistance in microbes, strategies that limit further evolution and spread of drug resistance are urgently needed, in individuals and populations. While classical recommendations favor longer and aggressive treatments, more recent studies and clinical trials advocate for moderate regimens. In this debate, two axes of aggressive treatment have typically been conflated: treatment intensity and treatment duration, the latter being rarely addressed by mathematical models. Here, by using a simple mathematical model of a generic bacterial infection, controlled by hosts immune response, we investigate the role of treatment timing and antibiotic efficacy in determining optimal duration of treatment. We show that even in such simple mathematical model, it is impossible to select for universally optimal treatment duration. In particular, short (3 day) or long (7 day) treatments may be both beneficial depending on treatment onset, on the criterion used, and on the antibiotic efficacy. This results from the dynamic trade-off between immunity and resistance in acute, self-limiting infections, and uncertainty relating symptoms to the start of infection. We find that treatment timing can shift the trend between resistance selection and length of antibiotic exposure in individual hosts. We propose that major advances in predicting impact of antibiotics on bacterial infections must come from deeper experimental understanding of bacterial infection dynamics in humans. To guide rational therapy, mathematical models need to be constrained by data, including details of pathology and symptom thresholds in patients, and of host immune control of infection.

systems biology↗

bioRxiv2019

Quantifying bacterial fitness in intracellular dynamics

Understanding bacterial infection is challenging because it involves a complex interplay of host, pathogen, and intervention factors. To design successful control measures, mathematical models that quantify such interplay at the level of populations and phenotypes are needed. Here, we study a key aspect of intracellular infection: the interaction dynamics between bacteria and target cells, applicable to pathogens such as Salmonella, E. coli or Listeria monocytogenes. Our mathematical model focuses on the macrophage-bacteria system, implicitly accounting for host immunity, and illustrates three infection scenarios driven by the balance between bacterial growth and death processes. Our analysis reveals critical parameter combinations for the intracellular vs. extracellular fitness advantage of persistent bacteria, and the drivers of overall infection success across acute and persistent regimes. Our results provide quantitative insights on transitions from persistent, to acute, to containment of infection, and suggest biological parameters, such as infected macrophage apoptosis rate and burst size, as suitable intervention targets.

systems biology↗