Biology subjects

Alarcon, T.

Publications and source records attributed to Alarcon, T..

15 recordsLinked to original sources

bioRxiv2026

Emergence of travelling wave patterns in resource-mediated tissue competition

The study of tissue dynamics has been stimulated during the last decades thanks to the use of quantitative descriptions, with the development of several theoretical and computational frameworks, many of them revolving around the notion of reaction-diffusion systems, eventually with additional structure variables beyond time and space. The use of structure variables can accommodate phenotypic traits. In this work, we study a family of competition models, where a given population depends on a resource (e.g. oxygen) and several populations are competing for it. Our quantitative description incorporates phenotypic traits and heterogeneity at the level of cell cycle variations, which influence replication rates via oxygen consumption. This enables us to replicate the fitness of specific subpopulations to environmental conditions (e.g. oxygen shortage or external influences). Using numerical simulations, we show that such models display dynamical pattern formation in the form of coupled travelling wave profiles that expand or retreat at the same wave speed. The full theoretical analysis of such dynamics is quite involved; to circumvent this difficulty, we introduce a quasi-stationary approximation for the resource dynamics. We find that this approximation can reproduce the overall behaviour very accurately, with the additional benefit of allowing theoretical treatment of the reduced model. In this way, we provide estimates on the wave speed which are numerically shown to be robust across a wide range of macroscopic parameters of the full model. The wave speeds are thus found to depend strongly on the proliferation rate of the fittest population, resembling a winner-takes-all dynamics.

biophysics↗

bioRxiv2026

Asymmetric drug effects drive near-extinction cancer cell oscillations in transgenic oncolytic virotherapy: A modelling study

AO_SCPLOWBSTRACTC_SCPLOWOncolytic viruses provide cancer therapy using replication-competent viruses that selectively infect and lyse tumour cells. Their tumour-specific replication also enables the delivery of targeted, virus-encoded gene products, such as enzymes that activate prodrugs. This dual functionality offers the potential for synergistic effects by combining direct oncolysis with localised drug activation. The interplay between infection, replication, lysis, and gene product delivery remains poorly understood. Here, we introduce a spatially structured, multi-patch model of cancer cells infected by an oncolytic virus engineered to deliver a prodrug-activating enzyme. The spatial system is first represented as a microscopic model and subsequently reduced via spectral dimension reduction techniques. This reduction yields an ordinary differential equation model for a set of coarse-grained variables, which we analyze both without the transgene (OV model) and with the transgene (TOV model). For each case, we compute the basic reproduction number, R0, which determines the conditions for viral spread. Both models exhibit three regimes via transcritical bifurcations: (i) R0 < 0, extinction of both cancer and infected cells; (ii) 0 < R0 [≤] 1, persistence of cancer cells only; and (iii) R0 > 1, coexistence as a stable node or as a focus. The TOV model, as a difference form the OV model, can undergo periodic oscillations arising from a Hopf-Andronov bifurcation. Notably, oscillation amplitudes can be controlled such that cancer cells largely decrease when drug-induced death is stronger in non-infected cells than in infected ones, enabling effective cancer cells killing while maintaining viral replication and prodrug activation. The qualitative behaviour of the coarse-grained model is shown to be preserved in both the microscopic and spatially explicit models.

systems biology↗

bioRxiv2025

Dynamical analysis of a model of BCL-2-dependent cellular decision making

The BCL-2 protein family governs critical cell-fate decisions between survival, senescence, and apoptosis, yet the dynamical principles underlying these choices remain poorly understood. Here, we integrate mathematical modeling, bifurcation analysis, and stochastic simulations to dissect how BCL-2 network architecture encodes multistability and fate plasticity. Our coarse-grained model reveals tristable regimes requiring cooperative BH3-only and anti-apoptotic BCL-2 interactions, with stochastic fluctuations driving heterogeneous fate commitments in genetically identical cells. Comparative analysis of mechanistic models demonstrates that while bistability emerges from canonical BCL-2 interactions, robust tristability requires additional regulatory constraint, explaining the metastability of senescence in stress responses. Hybrid models further show that BH3-only binding cooperativity enables multistability, but physiological senescence likely depends on additional control mechanisms. These results establish a unified framework linking molecular interactions to cell-fate dynamics, with implications for targeting apoptosis resistance in disease.

systems biology↗

bioRxiv2025

A novel model system to address the relevance of aggregation in animal origins

The origin of animals from unicellular ancestors remains a fundamental biological question. Cell aggregation, a widespread eukaryotic behaviour, has been underappreciated as a potential pathway to multicellularity. Here, we establish Capsaspora owczarzaki, a close unicellular relative of animals, as a model system to investigate this process. We demonstrate C. owzarzaki aggregates dynamically deploying key metazoan-related genes such as integrins, tyrosine kinases, and Hippo pathway components. Moreover, we further model mathematically the aggregation process, revealing a threshold-like adhesion response to fetal bovine serum (FBS) and dynamic aggregation kinetics driven by cell-cell adhesion and access to FBS. Our findings suggest that cell aggregation could have played a pivotal role in the evolution of animal multicellularity, providing a context for the origin of genes now crucial for animal development. This work positions Capsaspora as a powerful model system for quantitatively studying the evolutionary transition to multicellularity through cell aggregation.

evolutionary biology↗

bioRxiv2025

Metabolic early warning signals of epigenetic tipping points under chromatin modifier competition

The maintenance of epigenetic landscapes (EL) requires the precise regulation of chromatin-modifying enzymes (ChME). Competition for ChME can lead to degradation of ELs, triggering large-scale changes in the cell fate information contained in EL. Predicting impending epigenetic tipping points (ETP) by identifying early warning signals (EWS) may help to anticipate the onset of cell identity loss during aging and cancer. We have developed a general mathematical framework that incorporates different connectivity patterns generated by the 3D chromatin folding structure to analyze competition-induced ETP in large EL. This framework allows us to measure the sensitivity and robustness of ETP to the availability of metabolic cofactors and to identify potential EWS. Using a dimension reduction method, we derived coarse-grained (CG) equations for the collective observables associated with chromatin modifications. Analysis of the CG system allows the prediction of global transitions that shape the large-scale features of EL, accurately reproduce the corresponding microscopic benchmarks, and reveal the existence of tipping points under conditions of ChME competition. We applied the CG method to predict ETP under different connectivity patterns, including heterogeneous profiles such as those found in Hi-C data. Although a robustness measure for stable EL was derived from the CG dynamics in bistable regimes, sensitivity analysis revealed that metabolic cofactors have the greatest impact on EL robustness. In particular, we identified the metabolic cofactors SAM and acetyl-CoA as potential EWS for the catastrophic loss of hyperacetylated EL induced by ChME competition. The ability to predict global ETP can facilitate the discovery of predictive biomarkers and inform metabolic interventions aimed at limiting and reversing pathological cell fate decisions. Author summaryCells maintain their identity through specific patterns of gene expression controlled by structures called "epigenetic landscapes" (EL). Deterioration of these EL can occur over time as the enzymatic machineries responsible for maintaining them compete with each other. When this competition reaches a "tipping point", cells can undergo critical shifts in cell fate and lose their original identity, leading to aging and cancer. Using mathematical models, the researchers developed a way to accurately predict these tipping points early on, based on "warning signals" derived from the abundance levels of metabolites used by chromatin-modifying enzymes. This work could lead to new methods for detecting when cells are about to lose their identity and open the door to metabolic treatments that prevent or reverse this process in aging and cancer.

systems biology↗

bioRxiv2024

Multiscale modelling shows how cell-ECM interactions impact ECM fibre alignment and cell detachment

The extracellular matrix (ECM) is a dynamic network structure that surrounds, supports, and influences cell behaviour. It facilitates cell communication and plays an important role in cell functions such as growth and migration. One way that cells interact with the ECM is via focal adhesions, which enable them to sense and respond to matrix mechanical properties and exert traction forces that deform it. This mechanical interplay between cells and the ECM, many aspects of which remain incompletely understood, involves the coordination of processes acting at different spatial scales and is highly influenced by the mechanical properties of the cells, ECM and focal adhesion components. To gain a better understanding of these mechanical interactions, we have developed a multiscale agent-based model based on a mechanical description of forces that simultaneously integrates the mechanosensitive regulation of focal adhesions, cytoskeleton dynamics, and ECM deformation. We use our model to quantify cell-cell communication mediated by ECM deformation and to show how this process depends on the mechanical properties of cells, the ECM fibres and the topology of the ECM network. In particular, we analyse the influence of ECM stiffness and cell contraction activity in the transmission of mechanical cues between cells and how the distinct timescales associated with different processes influence cell-ECM interaction. Our model simulations predict increased ECM deformation for stronger cell contraction and a sweet spot of ECM stiffness for the transmission of mechanical cues along its fibres. We also show how the network topology affects the ability of stiffer ECMs to transmit deformation and how it can induce cell detachment from the ECM. Finally, we demonstrate that integrating processes across different spatial and temporal scales is crucial for understanding how mechanical communication influences cell behaviour. Author summaryThe cell surrounding is a dynamic fibrous network known as the extracellular matrix (ECM). It supports and influences cell behaviour, playing a key role in cell communication, growth, and migration. Cells sense the ECMs mechanical properties and exert traction forces on it, leading to the deformation of matrix fibres and the transmission of mechanical stress. These changes are transmitted along the ECM fibres, influencing the behaviour of neighbouring cells. Different subcellular structures and extracellular matrix components interact at various spatial and temporal scales, making mathematical modelling a valuable tool for analysing these interactions. We have developed a multiscale force-based model that quantifies mechanical stress transmission, captures cell detachment, and explores the impact of mechanical properties of both cells and the ECM. Our analysis shows that stronger cell contraction increases extracellular matrix deformation and suggests a range of extracellular matrix stiffness for effective mechanical cell-cell communication. We also use our model to investigate how ECM network topology can induce cell detachment by modifying the ability of stiff ECMs to transmit deformation when subject to cell-induced traction forces. Our results show the importance of coupling the processes occurring at different scales to capture the overall behaviour.

biophysics↗

bioRxiv2024

Local Nearby Bifurcations Lead to Synergies in Critical Slowing Down: the Case of Mushroom Bifurcations

The behavior of nonlinear systems close to critical transitions has relevant implications in assessing complex systems stability, transient properties, and resilience. Transient times become extremely long near phase transitions (or bifurcations) in a phenomenon generically known as critical slowing down, observed in electronic circuits, quantum electrodynamics, ferromagnetic materials, ecosystems, and gene regulatory networks. Typically, these transients follow well-defined universal laws of the form{tau} [~] |{micro} - {micro}c| {beta}, describing how their duration,{tau} , varies as the control parameter, {micro}, approaches its critical value, {micro}c. For instance, transients delays right after a saddle-node (SN) bifurcation, influenced by so-called ghosts, follow {beta} = -1/2. Despite intensive research on slowing down phenomena over the past decades for single bifurcations, both local and global, the behavior of transients when several bifurcations are close to each other remains unknown. Here, we study transients close to two SN bifurcations collapsing into a transcritical one. To do so, we analyze a simple nonlinear model of a self-activating gene regulated by an external signal that exhibits a mushroom bifurcation. We also propose and study a normal form for a system with two SN bifurcations merging into a transcritical one. For both systems, we show analytical and numerical evidence of a synergistic increase in transients due to the coupling of the two ghosts and the transcritical slowing down. We also explore the influence of noise on the transients in the gene-regulatory model. We show that intrinsic and extrinsic noise play opposite roles in the slowing down of the transition allowing us to control the timing of the transition without compromising the precision of the timing. This establishes novel molecular strategies to generate genetic timers with transients much larger than the typical timescales of the reactions involved.

systems biology↗

bioRxiv2021

Repeated out-of-Africa expansions of Helicobacter pylori driven by replacement of deleterious mutations

Helicobacter pylori lives in the human stomach and has a population structure which resembles that of its host. However, H. pylori from Europe and the Middle East trace a substantially higher fraction of ancestry from modern African populations than the humans that carry them. Here, we used a collection of Afro-Eurasian H. pylori genomes to show that this African ancestry is due to at least three distinct admixture events. H. pylori from East Asia, which have undergone little admixture, have accumulated many more non-synonymous mutations than African strains. European and Middle Eastern bacteria have elevated African ancestry at the sites of these mutations compared to either non-segregating or synonymous sites, implying selection to remove them. We used simulations to show that demographic bottlenecks can lead to long-term segregation of deleterious mutations, despite high rates of homologous recombination, but that population fitness can be restored by migration of small numbers of bacteria from non-bottlenecked populations, leading to mosaic patterns of ancestry like that seen for H. pylori. We conclude that H. pylori have been able to spread repeatedly from Africa by outcompeting strains that carried deleterious mutations accumulated during the original out-of-Africa bottleneck.

evolutionary biology↗

bioRxiv2020

Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs

Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Few strategies have been proposed for quantifying the variability associated to individual cancer-cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Here, we report a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against chromatin modifiers. We have formulated a stochastic model of a bivalent transcription factor that allows us to characterise three different qualitative behaviours, namely: bistable, high- and low-gene expression. Comparison between analytical results and experimental data determined that the so-called bistable and high-gene expression behaviours can be identified with undifferentiated and differentiated cell types, respectively. Since undifferentiated cells with an aberrant self-renewing potential might exhibit a cancer/metastasis-initiating phenotype, we analysed the efficiency of combining epigenetic drugs against the background of heterogeneity within the bistable sub-ensemble. Whereas single-targeted approaches mostly failed to circumvent the therapeutic problems represented by tumour heterogeneity, combinatorial strategies fared much better. Specifically, the more successful combinations were predicted to involve modulators of the histone H3K4 and H3K27 demethylases KDM5 and KDM6A/UTX. Those strategies involving the H3K4 and H3K27 methyltransferases MLL2 and EZH2, however, were predicted to be less effective. Our theoretical framework provides a coherent basis for the development of an in-silico platform capable of identifying the epigenetic drugs combinations best-suited to therapeutically manage non-uniform responses of heterogenous cancer cell populations. Author summaryHeterogeneity in cancer cell populations is one of the main engines of resistance to targeted therapies, as it induces nonuniform responses within the population that clears the sensitive subpopulation, whilst leaving unaffected the non-responsive cells. Although this is a well-known fact, few successful approaches have been proposed aimed at both quantifying the variability associated to cell heterogeneity, and characterising strategies that circumvent its drug-resistance inducing effects. Here we present a computational approach that addresses these issues in the particular context of targeting epigenetic regulators (specifically, chromatin modifiers), which have been proposed as therapeutic targets in several types of cancer and also in ageing-related diseases. Our model predicts that the more successful combinations involve modulators of demethylase activity (specifically, KDM5/6 and UTX). By contrast, strategies involving EZH2 activity are predicted to be less effective. Our results support the use of our framework as a platform for in silico drug trials, as it accounts for non-homogeneous response of cell populations to drugs as well as identifying which subpopulations are more likely to respond to specific strategies.

systems biology↗

bioRxiv2020

A multiscale model of complex endothelial cell dynamics in early angiogenesis

We introduce a hybrid two-dimensional multiscale model of angiogenesis, the process by which endothelial cells (ECs) migrate from a pre-existing vascular bed in response to local environmental cues and cell-cell interactions, to create a new vascular network. Recent experimental studies have highlighted a central role of cell rearrangements in the formation of angiogenic networks. Our model accounts for this phenomenon via the heterogeneous response of ECs to their microenvironment. These cell rearrangements, in turn, dynamically remodel the local environment. The model reproduces characteristic features of angiogenic sprouting that include branching, chemotactic sensitivity, the brush border effect, and cell mixing. These properties, rather than being hardwired into the model, emerge naturally from the gene expression patterns of individual cells. After calibrating and validating our model against experimental data, we use it to predict how the structure of the vascular network changes as the baseline gene expression levels of the VEGF-Delta-Notch pathway, and the composition of the extracellular environment, vary. In order to investigate the impact of cell rearrangements on the vascular network structure, we introduce the mixing measure, a scalar metric that quantifies cell mixing as the vascular network grows. We calculate the mixing measure for the simulated vascular networks generated by ECs of different lineages (wild type cells and mutant cells with impaired expression of a specific receptor). Our results show that the time evolution of the mixing measure is directly correlated to the generic features of the vascular branching pattern, thus, supporting the hypothesis that cell rearrangements play an essential role in sprouting angiogenesis. Furthermore, we predict that lower cell rearrangement leads to an imbalance between branching and sprout elongation. Since the computation of this statistic requires only individual cell trajectories, it can be computed for networks generated in biological experiments, making it a potential biomarker for pathological angiogenesis. Author summaryAngiogenesis, the process by which new blood vessels are formed by sprouting from the pre-existing vascular bed, plays a key role in both physiological and pathological processes, including tumour growth. The structure of a growing vascular network is determined by the coordinated behaviour of endothelial cells in response to various signalling cues. Recent experimental studies have highlighted the importance of cell rearrangements as a driver for sprout elongation. However, the functional role of this phenomenon remains unclear. We formulate a new multiscale model of angiogenesis which, by accounting explicitly for the complex dynamics of endothelial cells within growing angiogenic sprouts, is able to produce generic features of angiogenic structures (branching, chemotactic sensitivity, cell mixing, etc.) as emergent properties of its dynamics. We validate our model against experimental data and then use it to quantify the phenomenon of cell mixing in vascular networks generated by endothelial cells of different lineages. Our results show that there is a direct correlation between the time evolution of cell mixing in a growing vascular network and its branching structure, thus paving the way for understanding the functional role of cell rearrangements in angiogenesis.

systems biology↗

bioRxiv2019

Abnormal morphology biases haematocrit distribution in tumour vasculature and contributes to heterogeneity in tissue oxygenation

Oxygen heterogeneity in solid tumours is recognised as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal vascular structure of the tumour, but the precise mechanisms linking abnormal structure and compromised oxygen transport are only partially understood. In this paper, we investigate the role that RBC transport plays in establishing oxygen heterogeneity in tumour tissue. We focus on heterogeneity driven by network effects, which are challenging to observe experimentally due to the reduced fields of view typically considered. Motivated by our findings of abnormal vascular patterns linked to deviations from current RBC transport theory, we calculate average vessel lengths [Formula] and diameters [Formula] from tumour allografts of three cancer cell lines and observe a substantial reduction in the ratio [Formula] compared to physiological conditions. Mathematical modelling reveals that small values of the ratio{lambda} (i.e. {lambda} < 6) can bias haematocrit distribution in tumour vascular networks and drive heterogeneous oxygenation of tumour tissue. Finally, we show an increase in the value of{lambda} in tumour vascular networks following treatment with the anti-angiogenic cancer agent DC101. Based on our findings, we propose{lambda} as an effective way of monitoring the efficacy of antiangiogenic agents and as a proxy measure of perfusion and oxygenation in tumour tissue undergoing anti-angiogenic treatment. Significance statementOxygen heterogeneity in solid tumours is recognised as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal tumour vascular structure. We investigate the role that anomalies in RBC transport play in establishing oxygen heterogeneity in tumour tissue. We introduce a metric to characterise tumour vasculature (mean vessel length-to-diameter ratio,{lambda} ) and demonstrate how it predicts tissue oxygen heterogeneity. We also report an increase in{lambda} following treatment with the antiangiogenic agent DC101. Together, we propose{lambda} as an effective way of monitoring the action of anti-angiogenic agents and a proxy measure of oxygen heterogeneity in tumour tissue. Unravelling the causal relationship between tumour vascular structure and tissue oxygenation will pave the way for new personalised therapeutic approaches.

cancer biology↗

bioRxiv2019

Age-structure as key to delayed logistic proliferation of scratch assays

Scratch assays are in-vitro methods for studying cell migration. In these experiments, a scratch is made on a cell monolayer and recolonisation of the scratched region is imaged to quantify cell migration rates. Typically, scratch assays are modelled by reaction diffusion equations depicting cell migration by Fickian diffusion and modelling proliferation by a logistic term. In a recent paper (Jin, W. et al. Bull Math Biol (2017)), the authors observed experimentally that during the early stage of the recolonisation process, there is a disturbance phase where proliferation is not logistic, and this is followed by a growth phase where proliferation appears to be logistic. The authors did not identify the precise mechanism that causes the disturbance phase but showed that ignoring it can lead to incorrect parameter estimates. The aim of this work is to show that a non-linear age-structured population model can account for the two phases of proliferation in scratch assays. The model consists of an age-structured cell cycle model of a cell population, coupled with an ordinary differential equation describing the resource concentration dynamics in the substrate. The model assumes a resource-dependent cell cycle threshold age, above which cells are able to proliferate. By studying the dynamics of the full system in terms of the subpopulations of cells that can proliferate and the ones that can not, we are able to find conditions under which the model captures the two-phase behaviour. Through numerical simulations we are able to show that the resource concentration in the substrate regulates the biphasic dynamics.

cell biology↗

bioRxiv2018

A multiscale model of epigenetic heterogeneity reveals the kinetic routes of pathological cell fate reprogramming

The inherent capacity of somatic cells to switch their phenotypic status in response to damage stimuli in vivo might have a pivotal role in ageing and cancer. However, how the entry-exit mechanisms of phenotype reprogramming are established remains poorly understood. In an attempt to elucidate such mechanisms, we herein introduce a stochastic model of combined epigenetic regulation (ER)-gene regulatory network (GRN) to study the plastic phenotypic behaviours driven by ER heterogeneity. Furthermore, based on the existence of multiple scales, we formulate a method for stochastic model reduction, from which we derive an efficient hybrid simulation scheme that allows us to deal with such complex systems. Our analysis of the coupled system reveals a regime of tristability in which pluripotent stem-like and differentiated steady-states coexist with a third indecisive state. Crucially, ER heterogeneity of differentiation genes is for the most part responsible for conferring abnormal robustness to pluripotent stem-like states. We then formulate epigenetic heterogeneity-based strategies capable of unlocking and facilitating the transit from differentiation-refractory (pluripotent stem-like) to differentiation-primed epistates. The application of the hybrid numerical method validated the likelihood of such switching involving solely kinetic changes in epigenetic factors. Our results suggest that epigenetic heterogeneity regulates the mechanisms and kinetics of phenotypic robustness of cell fate reprogramming. The occurrence of tunable switches capable of modifying the nature of cell fate reprogramming from pathological to physiological might pave the way for new therapeutic strategies to regulate reparative reprogramming in ageing and cancer.\n\nAuthor summaryCertain modifications of the structure and functioning of the protein/DNA complex called chromatin can allow adult, fully differentiated cells to adopt a stem cell-like pluripotent state in a purely epigenetic manner, not involving changes in the underlying DNA sequence. Such reprogramming-like phenomena may constitute an innate reparative route through which human tissues respond to injury and could also serve as a novel regenerative strategy in human pathological situations in which tissue or organ repair is impaired. However, it should be noted that in vivo reprogramming would be capable of maintaining tissue homeostasis provided the acquisition of pluripotency features is strictly transient and accompanied by an accurate replenishment of the specific cell types being lost. Crucially, an excessive reprogramming to pluripotency in the absence of controlled re-differentiation would impair the repair or the replacement of damaged cells, thereby promoting pathological alterations of cell fate. A mechanistic understanding of how the degree of chromatin plasticity dictates the reparative versus pathological behaviour of in vivo reprogramming to rejuvenate aged tissues while preventing tumorigenesis is urgently needed, including especially the intrinsic epigenetic heterogeneity of the tissue resident cells being reprogrammed. We here introduce a novel method that mathematically captures how epigenetic heterogeneity is actually the driving force that governs the routes and kinetics to entry into and exit from a pathological pluripotent-like state. Moreover, our approach computationally validates the likelihood of unlocking chronic, unrestrained pluripotent states and drive their differentiation down the correct path by solely manipulating the intensity and direction of few epigenetic control switches. Our approach could inspire new therapeutic approaches based on in vivo cell reprogramming for efficient tissue regeneration and rejuvenation and cancer treatment.

systems biology↗

bioRxiv2018

Noise-induced bistability in the quasineutral coexistence of viral RNA under different replication modes

Evolutionary and dynamical investigations on real viral populations indicate that RNA replication can range between two extremes given by so-called stamping machine replication (SMR) and geometric replication (GR). The impact of asymmetries in replication for single-stranded, (+) sense RNA viruses has been up to now studied with deterministic models. However, viral replication should be better described by including stochasticity, since the cell infection process is typically initiated with a very small number of RNA macromolecules, and thus largely influenced by intrinsic noise. Under appropriate conditions, deterministic theoretical descriptions of viral RNA replication predict a quasineutral coexistence scenario, with a line of fixed points involving different strands equilibrium ratios depending on the initial conditions. Recent research on the quasineutral coexistence in two competing populations reveals that stochastic fluctuations fundamentally alters the mean-field scenario, and one of the two species outcompetes the other one. In this manuscript we study this phenomenon for RNA viral replication modes by means of stochastic simulations and a diffusion approximation. Our results reveal that noise has a strong impact on the amplification of viral RNA, also causing the emergence of noise-induced bistability. We provide analytical criteria for the dominance of (+) sense strands depending on the initial populations on the line of equilibria, which are in agreement with direct stochastic simulation results. The biological implications of this noise-driven mechanism are discussed within the framework of the evolutionary dynamics of RNA viruses with different modes of replication.

systems biology↗

bioRxiv2017

Viral replication modes in single-peak fitness landscapes: a dynamical systems analysis

Positive-sense, single-stranded RNA viruses are important pathogens infecting almost all types of organisms. Experimental evidences from mutant distributions and amplification kinetics of viral RNA suggest that these pathogens may follow different RNA replication modes, ranging from the stamping machine replication (SMR) to the geometric replication (GR) modes. Despite previous theoretical works have focused on the evolutionary dynamics of RNA viruses amplifying their genomes with different strategies, few is known in terms of the bifurcations and transitions involving error thresholds (mutation-induced dominance of mutants) and lethal mutagenesis (mutation-induced extinction of all sequences). Here we analyze a dynamical system describing the intracellular amplification of viral RNA genomes evolving on a single-peak fitness landscape focusing on three cases considering neutral, deleterious, and lethal mutants spectra. In our model, the different replication modes are introduced with parameter : with {gtrsim} 0 for the SMR and = 1 for the GR. We analytically derive the critical mutation rates causing lethal mutagenesis and error catastrophe, governed by transcritical bifurcations that depend on parameters , k1 (replicative fitness of mutants), and on the spontaneous degradation rates of the sequences,{epsilon} . For the lethal case the critical mutation rate involving lethal mutagenesis is [Formula]. Here, the SMR involves lower critical mutation rates, being the system more robust to lethal mutagenesis replicating closer to the GR mode. This result is also found for the neutral and deleterious cases, but for these later cases lethal mutagenesis can shift to the error catastrophe once the replication mode surpasses a threshold given by [Formula].

systems biology↗