Biology subjects

Komarova, N.

Publications and source records attributed to Komarova, N..

13 recordsLinked to original sources

bioRxiv2026

Hierarchical tissue structure creates history-dependent barriers to clonal invasion

Tissues of higher organisms are maintained by hierarchies of stem and progenitor cell compartments regulated by homeostatic feedback. Somatic mutations generate genetically distinct clones whose evolutionary success depends not only on their fitness but also on the tissue architecture in which they arise. In previous work, we showed that this hierarchical organization creates invasion barriers that prevent advantageous mutants originating in downstream compartments from expanding unless their fitness exceeds a critical threshold. Here, we extend this framework to populations containing multiple competing mutant clones. We derive a general invasion criterion showing that the threshold for mutant expansion is determined by the equilibrium established by the resident clones and therefore depends on the evolutionary history of the system. Established clones modify the invasion barriers encountered by subsequent mutants, making clonal evolution history-dependent. The theory predicts competitive exclusion between clones entering the same compartment and shows that resident clones can prevent the establishment of later mutants. Using a model previously parameterized for murine hematopoiesis, we showed that our framework provides a mechanistic explanation for mutation-order effects involving JAK2 V617F and TET2 mutations in myeloproliferative neoplasms. Our results identify invasion barriers as a principle governing history-dependent clonal evolution in hierarchical tissues.

evolutionary biology↗

bioRxiv2024

Mutant scaling laws reveal that accelerated mutant evolution via gene amplifications requires spatially structured population growth

Principles of evolution in spatially structured expanding populations have recently received much attention, but more work remains to be performed, especially for complex, multi-step evolutionary processes, where mutations are accumulated in an expanding population. A key limitation is that the simulation of spatially explicit stochastic computational models is essential, but not feasible for larger population sizes characteristic of prokaryotic and eukaryotic cell populations. We describe a methodological advance by deriving scaling laws that allow the straightforward prediction of the number of single-hit, double-hit and multi-hit mutants as a function of wild-type population size in spatially expanding populations. While this is a versatile tool to address a range of cutting-edge evolutionary questions, here we apply this methodology to reconcile apparently contradicting data from experimental evolution studies regarding the role of gene amplifications for the emergence of point mutations in bacteria. Applying the scaling laws, we demonstrate that in populations that expand in a 2D or a 3D spatial setting, gene amplifications can significantly promote mutant emergence, and that this is not possible in well-mixed populations. In support of the predictions, experiments that do show accelerated mutant evolution through gene amplifications grew bacteria in spatially restricted lawns, while those that failed to show an effect grew bacteria in non-spatial liquid media.

evolutionary biology↗

bioRxiv2024

Two modes in the velocity statistics in cautious walks of laboratory rodents

We have analyzed a large number of rodent tracks in open-field tests, in order to elucidate the statistics of their velocities. We found that the probability distribution of the absolute velocity of rodents can be approximated by a superposition of two Rayleigh distributions, with distinct characteristic velocities v1 and v2 with v1 < v2; this is in contrast to the single Rayleigh distribution for the velocity of a Brownian particle executing 2D random motion. We propose that the part of the distribution near the larger velocity, v2, characterizes rodents progressions in space, while the part near v1 describes other types of motion, such as lingering and body micromovements. We observed that the animals switched randomly between these two modes. While both velocities, v1 and v2, increase with age, their ratio, v2/v1, also grows with age, implying an increased efficacy of switches between the two modes in older animals. Since the existence of the modes is observed both in preweaned, blind pups and in older animals, it cannot be ascribed to foraging, but instead reflects risk assessment and proactive inhibition. We called such motion "cautious walks". Statistical analysis of the data further revealed a biphasic decline in the velocity auto-correlation function, with two characteristic times,{tau} s < {tau}l, where{tau} s characterizes the width of velocity peaks, and{tau} l is associated with the timing of the switches between progression and lingering. To describe the motion, we propose a stochastic model, which assumes the existence of two interfering processes: impulses to move that arrive at random times, and continuous deceleration. Its 2D Langevin-like equation has a damping coefficient that switches between two values, representing mode switching in rodents. Techniques developed here may be applicable for locomotion studies in a wide variety of contexts, as long as tracking data of sufficient resolution are available.

biophysics↗

bioRxiv2022

Deterministic approximation for population dynamics in the presence of advantageous mutants

Spatial stochastic simulations of evolutionary processes are computationally expensive. Here, based on spatially explicit decoupling approximations (SEDA) introduced in [1], we derive a deterministic approximation to a spatial stochastic birth-death process in the presence of two types: the less advantageous resident type and a more advantageous mutant. At the core of this technique are two essential steps: (1) a system of ODEs that approximate spatial interactions among neighboring individuals must be solved; (2) the time-variable has to be rescaled with a factor (called "") that depends on the kinetic parameters of the wild type and mutant individuals. An explicit formula for is derived, which is a power law of division and death rates of the two types. The method is relatively fast and provides excellent time-series agreement with the stochastic simulation results for the spatial agent-based model. The methodology can be used to describe hard selective sweep events, including the expansion of driver mutations in carcinogenesis, bacterial evolution, and aspects of resistance dynamics.

evolutionary biology↗

bioRxiv2021

Effect of human behavior on the evolution of viral strains during an epidemic

It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here we explore a complimentary trend: the pathogen itself might experience a force of selection to become less "visible", or less "symptomatic", in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.

evolutionary biology↗

bioRxiv2021

The Myogenesis Program Drives Clonal Selection and Drug Resistance in Rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is a pediatric cancer with features of skeletal muscle; patients with unresectable or metastatic RMS fare poorly due to high rates of disease recurrence. Here, we use single cell and single nucleus RNA-sequencing to show that RMS tumors recapitulate the spectrum of embryonal myogenesis. Using matched patient samples from a clinical trial and orthotopic patient-derived xenografts (O-PDXs), we show chemotherapy eliminates the most proliferative component with features of myoblasts; after treatment, the quiescent immature population with features of paraxial mesoderm expands to reconstitute the developmental hierarchy of the original tumor. We discovered that this paraxial mesoderm population is dependent on EGFR signaling and is sensitive to EGFR inhibitors. Taken together, this data serves as a proof-of-concept that targeting each developmental state in RMS is an effective strategy for improving outcomes by preventing disease recurrence.

cancer biology↗

bioRxiv2021

Spatial dynamics of feedback and feedforward regulation in cell lineages

Feedback mechanisms within cell lineages are thought to be important for maintaining tissue homeostasis. Mathematical models that assume well-mixed cell populations, together with experimental data, have suggested that negative feedback from differentiated cells on the stem cell self-renewal probability can maintain a stable equilibrium and hence homeostasis. Cell lineage dynamics, however, are characterized by spatial structure, which can lead to different properties. Here, we investigate these dynamics using spatially explicit computational models, including cell division, differentiation, death, and migration / diffusion processes. According to these models, the negative feedback loop on stem cell self-renewal fails to maintain homeostasis, both under the assumption of strong spatial restrictions and fast migration / diffusion. Although homeostasis cannot be maintained, this feedback can regulate cell density and promote the formation of spatial structures in the model. Tissue homeostasis, however, can be achieved if spatially restricted negative feedback on self-renewal is combined with an experimentally documented spatial feedforward loop in which stem cells regulate the fate of transit amplifying cells. This indicates that the dynamics of feedback regulation in tissue cell lineages are more complex than previously thought, and that combinations of spatially explicit control mechanisms are likely instrumental.

systems biology↗

bioRxiv2021

The role of migration in mutant evolution in fragmented populations

Mutant evolution in fragmented populations has been studied extensively in evolutionary biology. With an increased focus on evolutionary dynamics in medical research, quantification of mutant load in fragmented populations with varying levels of migration has become especially important. Examples of fragmented populations are hematopoietic stem cell niches in the bone marrow where cells can re-circulate between niches through the blood, or colonic crypts where movement of cells across different crypts is not thought to be common. Here we use a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, the experiments confirmed that while the mean number of mutants is not influenced by migration, the probability distribution is, which manifested itself in a change in the skewedness of the distribution of the mutant numbers in the demes. In the case of disadvantageous mutants, we investigated the phenomenon of the increase in the expected number of mutants compared to that of the selection-mutation balance. In a single deme, this increase is observed when the deme size is lower than the critical size, Nc. In a fragmented system that consists of connected demes with a probability of migration, the increase in mutant numbers above the selection-mutation balance can be maintained in small (N < Nc) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection-mutation balance decays exponentially with N/Nc. These findings are relevant in the context of the complex and poorly understood processes that may lead to changes in the clonal composition in tissues and tumors.

evolutionary biology↗

bioRxiv2021

The protective effect of aspirin in colorectal carcinogenesis: a multiscale computational study from mutant evolution to age incidence curves

Aspirin intake has been shown to lead to significant protection against colorectal cancer, e.g. with an up to two-fold reduction in colorectal adenoma incidence rates at higher doses. The mechanisms contributing to protection are not yet fully understood. While aspirin is an anti-inflammatory drug and can thus influence the tumor microenvironment, in vitro and in vivo experiments have recently shown that aspirin can also have a direct effect on cellular kinetics and fitness. It reduces the rate of tumor cell division and increases the rate of cell death. The question arises whether such changes in cellular fitness are sufficient to significantly contribute to the epidemiologically observed protection. To investigate this, we constructed a class of mathematical models of in vivo evolution of advanced adenomas, parameterized it with available estimates, and calculated population level incidence. Fitting the predictions to age incidence data revealed that only a model that included colonic crypt competition can account for the observed age-incidence curve. This model was then used to predict modified incidence patterns if cellular kinetics were altered as a result of aspirin treatment. We found that changes in cellular fitness that were within the experimentally observed ranges could reduce advanced adenoma incidence by a sufficient amount to account for age incidence data in aspirin-treated patient cohorts. While the mechanisms that contribute to the protective effect of aspirin are likely complex and multi-factorial, our study demonstrates that direct aspirin-induced changes of tumor cell fitness can significantly contribute to epidemiologically observed reduced incidence patterns.

cancer biology↗

bioRxiv2019

Mutant evolution in spatially structured and fragmented populations

Understanding mutant evolution in spatially structured systems is crucially important for a range of biological systems, including bacterial populations and cancer. While previous work has shown that the mutation load is higher in spatially structured compared to well-mixed systems for neutral mutants in the absence of cell death, we demonstrate a significantly higher degree of complexity, using a comprehensive computational modeling approach that takes into account different mutant fitness, cell death, and different population structures. While an agent-based model assuming nearest neighbor interactions predicts a higher abundance of neutral or advantageous mutants compared to well-mixed systems of the same size, we show that for disadvantageous mutants, results depend on the nature of the disadvantage. In particular, if the disadvantage occurs through higher death rates, as opposed to lower reproduction rates, the result is the opposite, and a lower mutation load occurs in spatial compared to mixed systems. Interestingly, we show that in all cases, the same results are observed in fragmented patch models, where individuals can migrate to randomly chosen patches, thus lacking a strict spatial component. Hence, the results reported for spatial models are the consequence of population fragmentation, and not spatial restrictions per se. We further derive growth laws that characterize the expansion of wild type and mutant populations in different dimensionalities. For example, we find that while disadvantageous mutant abundance scales with the total population size, N, neutral mutants grow faster (as N2 for 1D, as N3/2 in 2D, and as N4/3 in 3D). Advantageous mutants scale with the cube of N in 1D and with the square of N in higher dimensions. These laws are universal (as long as mutants remain a minority) and independent of \"microscopic\" modeling details.

evolutionary biology↗

bioRxiv2019

Evolutionary dynamics of culturally transmitted, fertility-reducing traits

Human populations in many countries have undergone a phase of demographic transition, characterized by a major reduction in fertility at a time of increased resource availability. A key stylized fact is that the reduction in fertility is preceded by a reduction in mortality and a consequent increase in population density. Various theories have been proposed to account for the demographic transition process, including maladaptation, increased parental investment in fewer offspring, and cultural evolution. None of these approaches, including formal cultural evolutionary models of the demographic transitions, have addressed a possible direct causal relationship between a reduction in mortality and the subsequent decline in fertility. We provide mathematical models in which low mortality favors the cultural selection of low fertility traits. This occurs because reduced mortality slows turnover in the model, which allows the cultural transmission advantage of low fertility traits to out-race their reproductive disadvantage. For mortality to be a crucial determinant of outcome, a cultural transmission bias is required where slow reproducers exert higher social influence. Computer simulations of our models that allow for exogenous variation in the death rate can reproduce the central features of the demographic transition process, including substantial reductions in fertility within only 1-3 generations. A model assuming continuous evolution of reproduction rates through imitation errors predicts fertility to fall below replacement levels, if death rates are sufficiently low. This can potentially explain the very low preferred family sizes in Western Europe.

evolutionary biology↗

bioRxiv2017

The role of telomere shortening in carcinogenesis: A hybrid stochastic-deterministic approach

Genome instability is a characteristic of most cancers, contributing to the acquisition of genetic alterations that drive tumor progression. One important source of genome instability is linked to telomere dysfunction in cells with critically short telomeres that lack p53-mediated surveillance of genomic integrity. Here we research the probability that cancer emerges through an evolutionary pathway that includes a telomere-induced phase of genome instability. To implement our models we use a hybrid stochastic-deterministic approach, which allows us to perform large numbers of simulations using biologically realistic population sizes and mutation rates, circumventing the traditional limitations of fully stochastic algorithms. The hybrid methodology should be easily adaptable to a wide range of evolutionary problems. In particular, we model telomere shortening and the acquisition of two mutations: Telomerase activation and p53 inactivation. We find that the death rate of unstable cells, and the number of cell divisions that p53 mutants can sustain beyond the normal senescence setpoint determine the likelihood that the first double mutant originates in a cell with telomere-induced instability. The model has applications to an influential telomerase-null mouse model and p16 silenced human cells. We end by discussing algorithmic performance and a measure for the accuracy of the hybrid approximation.

cancer biology↗

bioRxiv2017

Passenger mutations can accelerate tumor suppressor gene inactivation in cancer evolution

Carcinogenesis is an evolutionary process whereby cells accumulate multiple mutations. Besides the \"driver mutations\" that cause the disease, cells also accumulate a number of other mutations with seemingly no direct role in this evolutionary process. They are called passenger mutations. While it has been argued that passenger mutations render tumors more fragile due to reduced fitness, the role of passenger mutations remains understudied. Using evolutionary computational models, we demonstrate that in the context of tumor suppressor gene inactivation (and hence fitness valley crossing), the presence of passenger mutations can accelerate the rate of evolution by reducing overall population fitness and increasing the relative fitness of intermediate mutants in the fitness valley crossing pathway. Hence, the baseline rate of tumor suppressor gene inactivation might be faster than previously thought. Conceptually, parallels are found in the field of turbulence and pattern formation, where instabilities can be driven by perturbations that are damped (disadvantageous), but provide a richer set of pathways such that a system can achieve some desired goal more readily. This highlights, through a number of novel parallels, the relevance of physical sciences in oncology.

evolutionary biology↗