Biology subjects

Komarova, N. L.

Publications and source records attributed to Komarova, N. L..

14 recordsLinked to original sources

bioRxiv2026

Microbiomes change the rules of host evolution

Microbiomes are pervasive determinants of host phenotype and fitness, yet their consequences for host population genetics remain poorly understood. Here we show that microbiome-associated population structure can fundamentally alter the evolutionary dynamics of host populations. We develop a general framework that maps hosts distributed among an arbitrary number of microbial states onto an effective population size, $N_{\mathrm{eff}}$, and effective selection coefficient, $s_{\mathrm{eff}}$, thereby reducing potentially high-dimensional host--microbiome dynamics to the quantities governing genetic drift and selection. Applying the framework to five empirically anchored host--microbe systems spanning ciliates, insects, and plants with bacterial, algal, and fungal associates reveals substantial departures from classical population-genetic predictions. Microbiome-associated structure can considerably alter both the effective strength of genetic drift and the strength of selection acting on host genetic variation, shifting populations into evolutionary regimes that differ markedly from those predicted from census size and intrinsic mutational effects alone. These changes can have profound consequences for evolutionary outcomes, including either suppression or enhancement of selection for both advantageous and disadvantageous mutations, sometimes reaching orders-of-magnitude differences. The effects are not determined simply by whether a microbial association is beneficial or detrimental, but by the ecological processes governing microbial acquisition, transmission, loss, and host demography. Our results provide a population-genetic extension of the holobiont perspective: when microbial associations generate distinct demographic states among hosts, they can alter the evolutionary forces acting on the host genome without requiring the host and microbiome to constitute a single hereditary unit.

evolutionary biology↗

bioRxiv2026

Superspreaders increase deleterious mutant burden and can accelerate the evolution of complex traits in pathogens

Degree heterogeneity in contact networks is known to accelerate the spread of infectious diseases through the presence of superspreaders, but its evolutionary consequences remain less understood. Here we study how network heterogeneity shapes the fate of competing pathogen strains in a stochastic susceptible-infected-susceptible framework. We show that heterogeneous networks act as strong suppressors of selection: both advantageous and disadvantageous mutants exhibit fixation probabilities close to neutral expectations, in stark contrast to well-mixed populations. We derive an analytical theory that captures this effect through a single suppression factor determined by network structure and infection dynamics, and validate it against simulations on synthetic and empirical contact networks. Mechanistically, suppression arises because most transmission events are effectively neutral, while selection acts only in rare configurations. As a consequence, heterogeneous networks substantially increase the persistence of deleterious mutants and elevate mutation-selection balance, but they can either accelerate or decelerate multi-step evolutionary processes such as fitness valley crossing. Our results reveal a fundamental trade-off induced by superspreaders: While they enhance epidemic spread, they weaken selective pressures and thereby promote evolutionary diversification.

evolutionary biology↗

bioRxiv2025

Cellular turnover can increase or decrease the mutant burden in expanding cell population

Expanding cell populations, including bacterial colonies and tumors, continuously accumulate mutations as they grow. Yet how mutant burden depends on the cellular death-to-birth ratio in spatially structured populations has not been addressed, despite clinical studies associating higher tumor turnover with both better and worse patient outcomes across cancers. This question is especially relevant for deleterious mutants, which can become advantageous if the environment changes, such as when treatment begins. Mutant accumulation has been studied extensively in well-mixed populations. Here, we derive a new closed-form approximation for the mean deleterious mutant burden at a specified population size when cell death is nonzero, serving as a baseline for comparisons to spatial structure. We show that spatial structure, a defining feature of solid tumors, can fundamentally alter how mutant burden depends on the death-to-birth ratio. Relationships that increase monotonically in well-mixed populations can become nonmonotonic or even decrease in spatially expanding populations, depending on population size and the fitness cost of mutants. These results provide a mechanistic framework for understanding why higher tumor turnover has been associated with either improved or worsened outcomes across patients and cancer types. An analysis of the probability that a colony regrows after an environmental change (such as treatment initiation) reveals a similar pattern: spatial structure can reverse or reshape this dependence. Overall, our findings establish spatial structure as a key determinant of how cellular death-to-birth ratios shape mutant accumulation, with implications for interpreting clinical data and predicting the burden of preexisting mutants during tumor evolution. Significance statementThe number of mutants present when a growing cell population reaches a given size shapes its capacity to adapt, since deleterious mutants can become advantageous once conditions change, such as when cancer treatment begins. Clinical studies report contradictory links between tumor cell turnover (death-to-birth ratio) and patient outcomes, with higher turnover linked to both better and worse prognosis. Building on established theory for well-mixed populations, we show that spatial population structure, a key feature of solid tumors, can make mutant burden increase, decrease, or vary non-monotonically with the death-to-birth ratio, depending on tumor size and mutant fitness cost, patterns absent in the well-mixed system. This resolves the contradiction and clarifies how tumor turnover shapes its potential to evolve and progress.

evolutionary biology↗

bioRxiv2025

Pros and cons of group averaging in studies of stochastic rodent locomotion

We analyze spontaneous walks of rodent pups in a novel environment. Individual speed distributions follow a two-component Rayleigh distribution, reflecting bimodal locomotion (progressions and lingering), while the ensemble-averaged distribution simplifies to a single exponential decay with a slower high-speed decay rate. This discrepancy arises from population heterogeneity in characteristic speeds. We propose a stochastic differential equation (SDE) for the averaged rodent, which in some cases can be derived by averaging the individual SDE. The SDEs reveal a striking mechanistic difference in the nature of individual and averaged motion: individual rodents exhibit viscous friction (deceleration proportional to speed), whereas the ensemble behaves as if subject to dry friction (constant deceleration). We propose an algorithm of creating a "reference" rodent, which retains the shape of individual distributions but utilizes averaged scale parameters. It can be a useful personalized comparison tool to extract the information on the locomotor modes, e.g. in biomedical contexts.

animal behavior and cognition↗

bioRxiv2025

Population structure reverses selection of variants with proportionally scaled birth and death rates

A frequently observed phenomenon across the kingdom of life is that a higher reproduction rate can be accompanied by higher mortality. During tumor progression, variants emerge that both reproduce and die faster; faster replicating viruses can be characterized by a faster decay rate; and more frequent pregnancy can be accompanied by a higher chance to die due to predation in ecological systems. Variants with proportionally scaled birth and death rates have been called quasi-neutral mutants. Although life-time reproductive success is not changed, such variants are characterized by fixation probabilities that are somewhat lower (higher) than expected for neutral mutants if birth and death rates are proportionally larger (smaller). Studies were performed in the context of well-mixed populations, and despite the deviation from neutrality, quasi-neutral mutants do not have characteristics of disadvantageous or advantageous mutants, as their fixation probabilities still scale with their initial fractions. Here, we report that in deme-or spatially structured populations, variants with proportionally increased (decreased) birth and death rates become truly disadvantageous (advantageous), and calculate their effective fitness. Furthermore, if mutants have a higher life-time reproductive output than the wild-types and are thus advantageous, a proportional increase of birth and death rates can render them strongly disadvantageous, and vice versa. This changes our understanding of how life-time reproductive success correlates with selection, and has implications for evolutionary dynamics across a range of biological systems.

evolutionary biology↗

bioRxiv2024

Surface remodeling and inversion of cell-matrix interactions underlies community recognition and dispersal in Vibrio cholerae biofilms

Biofilms are ubiquitous surface-associated bacterial communities embedded in an extracellular matrix. While it is commonly assumed that biofilm-dwelling cells are glued together by the matrix, how the cell-matrix interaction depends on the specific biochemistry of the matrix components and how this interaction varies during biofilm growth remains unclear. Here, we investigated cell-matrix interactions in Vibrio cholerae (Vc), the causative agent of cholera. We combine genetics, microscopy, simulation, and biochemical tools to show that Vc cells are not attractive to Vibrio polysaccharide (VPS), the main matrix component, but they can be bridged with each other and to the VPS network through crosslinking by Bap1. Downregulation of VPS and surface trimming by the polysaccharide lyase RbmB cause surface remodeling as biofilms age, shifting the nature of cell-matrix interactions from attractive to repulsive and facilitating cell dispersal as aggregated groups. Our results suggest a new conceptual model in understanding the intricate cell-matrix interaction as the major driver for biofilm development, which is potentially generalizable to certain other biofilm-forming species and exopolysaccharides.

biophysics↗

bioRxiv2023

Dynamically adjusted cell fate decisions and resilience to mutant invasion during steady state hematopoiesis revealed by an experimentally parameterized mathematical model

A major next step in hematopoietic stem cell (HSC) biology is to obtain a thorough quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models are key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. Mathematical models of hematopoiesis have either been parameterized experimentally without non-linear dynamics, or they include these complexities but have not been parameterized to the same extent. We bridge this gap using mouse data to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that non-linear feedback control drastically changes the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors (MPPs) can dynamically adjust to sustain themselves in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and a mutant-associated inflammatory environment. This helps us understand the evolution of e.g. TET2, DNMT3A, or JAK2 mutants, and how to potentially reduce mutant burden.

evolutionary biology↗

bioRxiv2023

Mutant fixation in the presence of a natural enemy

In the extensive literature about mutant invasion and fixation, populations are typically assumed to exist in isolation from their ecosystem. Yet, populations are part of ecological communities, and enemy-victim (e.g. predator-prey or pathogen-host) dynamics are particularly common. We use computational models to re-visit the established theory about mutant fixation in the presence of a natural enemy, which equally attacks both wild-type and mutant populations. We consider advantageous and disadvantageous mutants, whose fitness is unrelated to the infection. Using spatially structured agent-based as well as patch models of a population that is subject to infection, we investigate the fixation probability of a mutant that is introduced into the population at quasi-equilibrium. We find that infection significantly weakens selection. Thus, the presence of infection increases the fixation probability of disadvantageous mutants and decreases the fixation probability of advantageous mutants, with the magnitude of the effect rising with the infection rate. We show that this occurs because infection induces spatial structures, in which mutant and wild-type individuals are mostly spatially separated. Thus, instead of mutant and wild-type individuals competing with each other, it is mutant and wild-type "patches" that compete, resulting in smaller fitness differences and hence weakened selection. Because natural enemies such as infections are ubiquitous, this has broad applicability to natural systems. Our results imply that the burden of deleterious mutants in natural populations might be significantly higher than expected from mutant invasion theory developed in the absence of natural enemies.

evolutionary biology↗

bioRxiv2020

A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small subpopulations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability.

evolutionary biology↗

bioRxiv2020

Quantifying the dynamics of viral recombination during free virus and cell-to-cell transmission in HIV-1 infection

Recombination has been shown to contribute to HIV-1 evolution in vivo, but the underlying dynamics are extremely complex, depending on the nature of the fitness landscapes and of epistatic interactions. A less well-studied determinant of recombinant evolution is the mode of virus transmission in the cell population. HIV-1 can spread by free virus transmission, resulting largely in singly infected cells, and also by direct cell-to-cell transmission, resulting in the simultaneous infection of cells with multiple viruses. We investigate the contribution of these two transmission pathways to recombinant evolution, by applying mathematical models to in vitro experimental data on the growth of fluorescent reporter viruses under static conditions (where both transmission pathways operate), and under gentle shaking conditions, where cell-to-cell transmission is largely inhibited. The parameterized mathematical models are then used to extrapolate the viral evolutionary dynamics beyond the experimental settings. Assuming a fixed basic reproductive ratio of the virus (independent of transmission pathway), we find that recombinant evolution is fastest if virus spread is driven only by cell-to-cell transmission, and slows down if both transmission pathways operate. Recombinant evolution is slowest if all virus spread occurs through free virus transmission. This is due to cell-to-cell transmission (i) increasing infection multiplicity, (ii) promoting the co-transmission of different virus strains from cell to cell, and (iii) increasing the rate at which point mutations are generated as a result of more reverse transcription events. This work further resulted in the estimation of various parameters that characterize these evolutionary processes. For example, we estimate that during cell-to-cell transmission, an average of 3 viruses successfully integrated into the target cell, which can significantly raise the infection multiplicity compared to free virus transmission. In general, our study points towards the importance of infection multiplicity and cell-to-cell transmission for HIV-evolution.

evolutionary biology↗

bioRxiv2019

Effect of feedback regulation on stem cell fractions in tissues and tumors: understanding chemoresistance in bladder cancer

While resistance mutations are often implicated in the failure of cancer therapy, lack of response also occurs without such mutants. In bladder cancer mouse xenografts, repeated chemotherapy cycles have resulted in cancer stem cell (CSC) enrichment, and consequent loss of therapy response due to the reduced susceptibility of CSCs to drugs. A particular feedback loop present in the xenografts has been shown to promote CSC enrichment in this system. Yet, many other regulatory loops might also be operational and might promote CSC enrichment. Their identification is central to improving therapy response. Here, we perform a comprehensive mathematical analysis to define what types of regulatory feedback loops can and cannot contribute to CSC enrichment, providing guidance to the experimental identification of feedback molecules. We derive a formula that reveals whether or not the cell population experiences CSC enrichment over time, based on the properties of the feedback. We find that negative feedback on the CSC division rate or positive feedback on differentiated cell death rate can lead to CSC enrichment. Further, the feedback mediators that achieve CSC enrichment can be secreted by either CSCs or by more differentiated cells. The extent of enrichment is determined by the CSC death rate, the CSC self-renewal probability, and by feedback strength. Defining these general characteristics of feedback loops can guide the experimental screening for and identification of feedback mediators that can promote CSC enrichment in bladder cancer and potentially other tumors. This can help understand and overcome the phenomenon of CSC-based therapy resistance.

systems biology↗

bioRxiv2019

Effect of synaptic cell-to-cell transmission and recombination on the evolution of double mutants in HIV

Recombination in HIV infection can impact virus evolution in vivo in complex ways, as has been shown both experimentally and mathematically. The effect of free virus versus synaptic, cell-to-cell transmission on the evolution of double mutants, however, has not been investigated. Here we do so by using a stochastic agent-based model. Consistent with data, we assume spatial constraints for synaptic but not for free-virus transmission. Two important effects of the viral spread mode are observed: (i) For disadvantageous mutants, synaptic transmission protects against detrimental effects of recombination on double mutant persistence. Under free virus transmission, recombination increases double mutant levels for negative epistasis, but reduces them for positive epistasis. This reduction for positive epistasis is much diminished under pre-dominantly synaptic transmission, and recombination can in fact lead to increased mutant levels. (ii) The mode of virus spread also directly influences the evolutionary fate of double mutants. For disadvantageous mutants, double mutant production is the predominant driving force, and hence synaptic transmission leads to highest double mutant levels due to increased transmission efficiency. For advantageous mutants, double mutant spread is the most important force, and hence free virus transmission leads to fastest invasion due to better mixing. For neutral mutants, both production and spread of double mutants are important, and hence an optimal mixture of free virus and synaptic transmission maximizes double mutant fractions. Therefore, both free virus and synaptic transmission can enhance or delay double mutant evolution. Implications for drug resistance in HIV are discussed.

evolutionary biology↗

bioRxiv2019

Determinants of stem cell enrichment in healthy tissues and tumors: implications for non-genetic drug resistance

Drug resistance is a major challenge for cancer therapy. While resistance mutations are often the focus of investigation, non-genetic resistance mechanisms are also important. One such mechanism is the presence of relatively high fractions of cancer stem cells (CSCs), which have reduced susceptibility to chemotherapy, radiation, and targeted treatments compared to more differentiated cells. The reasons for high CSC fractions (CSC enrichment) are not well understood. Previous experimental and mathematical modeling work identified a particular feedback loop in tumors that can promote CSC enrichment. Here, we use mathematical models of hierarchically structured cell populations to build on this work and to provide a comprehensive analysis of how different feedback regulatory processes that might partially operate in tumors can influence the stem cell fractions during somatic evolution of healthy tissue or during tumor growth. We find that depending on the particular feedback loops that are present, CSC fractions can increase or decrease. We define characteristics of the feedback mechanisms that are required for CSC enrichment to occur, and show how the magnitude of enrichment is determined by parameters. In particular, enrichment requires a reduction in division rates or an increase in death rates with higher population sizes, and the feedback mediators that achieve this can be secreted by either CSCs or by more differentiated cells. The extent of enrichment is determined by the death rate of CSCs, the probability of CSC self-renewal, and by the strength of feedback on cell divisions. Defining these characteristics can guide experimental approaches that aim to screen for and identify feedback mediators that can promote CSC enrichment in specific cancers, which in turn can help understand and overcome the phenomenon of CSC-based therapy resistance.

evolutionary biology↗

bioRxiv2018

Multiple infection of cells changes the dynamics of basic viral evolutionary processes

The infection of cells by multiple copies of a given virus can impact virus evolution in a variety of ways, for example through recombination and reassortment, or through intra-cellular interactions among the viruses in a cell, such as complementation or interference. Surprisingly, multiple infection of cells can also influence some of the most basic evolutionary processes, which has not been studied so far. Here, we use computational models to explore how infection multiplicity affects the fixation probability of mutants, the rate of mutant generation, and the timing of mutant invasion. This is investigated for neutral, disadvantageous, and advantageous mutants. Among the results, we note surprising growth dynamics for neutral and disadvantageous mutants: Starting from a single mutant-infected cell, an initial growth phase is observed which is more characteristic of an advantageous mutant and is not observed in the absence of multiple infection. Therefore, in the short term, multiple infection increases the chances that neutral or dis-advantageous mutants are present. Following this initial growth phase, however, the mutant dynamics enter a second phase that is driven by neutral drift or negative selection, respectively, which determines the long-term fixation probability of the mutant. Contrary to the short-term dynamics, the probability of mutant fixation, and thus existence, is lower in the presence compared to the absence of multiple infection, and declines with infection multiplicity. Hence, while infection multiplicity promotes mutant existence in the short term, it makes it less likely in the longer term. Understanding of these dynamics is essential for the investigation of more complex viral evolutionary processes, for which the dynamics described here for the basis. We demonstrate relevance to the interpretation of experiments in the context of published data on phage {varphi}6 evolution at low and high multiplicities.

evolutionary biology↗