Biology subjects

Ram, Y.

Publications and source records attributed to Ram, Y..

16 recordsLinked to original sources

bioRxiv2026

Inferring viral proteins that act as public goods during coinfection

Interactions among individuals in structured populations can alter fitness effects of mutations and reshape evolutionary processes. In many systems, including bacteria, yeast, and viruses, such interactions often result in public goods: gene products that are costly to produce yet exploitable by others. During viral coinfection of the same cell, gene products from one genome may complement deleterious mutations in another, allowing defective genomes to persist. Yet it remains difficult to infer which proteins are shareable from population sequencing data, because mutation, selection, drift, and complementation are intertwined. Here, we developed a quantitative framework to infer protein-specific public goods in the RNA bacteriophage MS2, which encodes only four proteins. We analyzed experimental evolution data generated under two multiplicity-of-infection (MOI) regimes: low MOI, where coinfection is rare, and high MOI, where coinfection is common. We first compared empirical mutation patterns between regimes and then applied a Wright-Fisher model combined with simulation-based Bayesian inference using neural posterior estimation. In a two-stage strategy, gene-specific fitness effects were inferred from low-MOI data and subsequently used to estimate protein sharing under high-MOI conditions. Across two statistical inference frameworks, lysis emerged as the strongest public-good candidate, replicase and coat showed an intermediate signal, and maturation showed the weakest evidence for sharing. Together, our results show that viral proteins differ markedly in their propensity to act as public goods. More broadly, they illustrate how coinfection can generate density-dependent selection, a general feature of social evolution that may shape evolutionary dynamics.

evolutionary biology↗

bioRxiv2026

Evolution of Menopause and the Mamas Boy Hypothesis

Post-reproductive lifespan is an evolutionary puzzle. In most mammals female fertility tracks survival, yet humans and a few toothed whales show survival after reproduction ends. Explaining when and why post-reproductive lifespan evolves is central to understanding the evolution of ageing, social structure, and intergenerational helping across species. Kinship-dynamics theory predicts that when males are philopatric, a females local relatedness--especially to male descendants--increases with age, potentially favoring late-life helping over continued reproduction. We develop an age-sex-structured kin-selection model to test whether a rare menopause-inducing modifier allele can invade an initially non-menopausal population through its direct effects on survival and fecundity and its indirect effects on relatives. We consider two evolutionary pathways: stop early, where reproduction ceases earlier with little change in lifespan, and live long, where lifespan extends beyond reproduction under disposable-soma trade-offs. Parameterized with demographic, dispersal, and helping-effect estimates from eight mammalian taxa, the model predicts empirically plausible ages of reproductive cessation and post-reproductive representation in humans and killer whales, but no invasion across plausible cessation ages in non-menopausal taxa. Global sensitivity analyses identify male dispersal and the effect of post-reproductive help on male survival as determinants of whether menopause evolves, motivating the "mamas boy hypothesis": menopause is most strongly favoured by selection when late-life care increases the survival and lifetime fitness of philopatric sons and grandsons.

evolutionary biology↗

bioRxiv2026

Collective Posterior Inference from Highly Variable Empirical Replicates

High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime without incurring prohibitive computational costs or careful hyperparameter tuning. Here, we introduce a new method for fast and robust collective posterior inference from multiple independent replicates using a robust product-of-experts aggregation scheme that automatically mitigates the influence of outliers. Evaluating it on synthetic and empirical evolutionary datasets, we find it achieves state-of-the-art estimation accuracy and computational efficiency, including inference from noisy observations. Our method is compatible with any SBI framework, providing a scalable, plug-and-play solution for inference from noisy multiple-replicate datasets.

bioinformatics↗

bioRxiv2025

Segmental copy number amplifications are stable in the absence of selection

Copy number variants (CNVs) are DNA duplications and deletions that cause genetic variation, underlying rapid adaptive evolution. CNVs often confer selective advantages, but can also incur fitness costs. Evolution of Saccharomyces cerevisiae in nutrient-limited chemostats recurrently selects for amplifications of nutrient transporter genes. However, their fate upon return to a non-selective environment remains unknown. To investigate CNV fitness and stability upon removing the original selection pressure, we studied 15 CNV lineages (11 segmental, 4 whole-chromosomal amplifications) selected in nitrogen-limited chemostats. CNV stability was monitored using fluorescent reporters during propagation in nutrient-rich batch cultures for 110-220 generations. All aneuploid lineages showed rapid CNV loss and reversion to a single-copy genotype, whereas segmental amplifications were remarkably stable- one of 11 strains reverted. Pairwise fitness competitions in rich media revealed strong fitness defects associated solely with CNVs that reverted; reversion led to increased fitness. Using simulation-based inference to estimate reversion rates and fitness effects, we determined negative selection as the primary driver of CNV loss. Whole-genome sequencing revealed that reversion of aneuploids and a segmental amplification left no evidence of prior CNV existence, rendering revertant genomes indistinguishable from the single-copy ancestor. Detailed characterization of a partial revertant identified chromosomal translocation, suggesting that extant CNVs can undergo structural diversification. Our findings provide novel evidence that most segmental CNVs adapted to nitrogen limitation are stable upon removal of selection, but costly gene amplifications are readily reversible. Together, these highlight the importance of CNVs in both long-term genome evolution and rapid, reversible adaptation to transient selection.

evolutionary biology↗

bioRxiv2024

DNA replication errors are a major source of adaptive gene amplification

Copy number variants (CNVs)--gains and losses of genomic sequences--are an important source of genetic variation underlying rapid adaptation and genome evolution. However, despite their central role in evolution little is known about the factors that contribute to the structure, size, formation rate, and fitness effects of adaptive CNVs. Local genomic sequences are likely to be an important determinant of these properties. Whereas it is known that point mutation rates vary with genomic location and local DNA sequence features, the role of genome architecture in the formation, selection, and the resulting evolutionary dynamics of CNVs is poorly understood. Previously, we have found that the GAP1 gene in Saccharomyces cerevisiae undergoes frequent and repeated amplification and selection under long-term experimental evolution in glutamine-limiting conditions. The GAP1 gene has a unique genomic architecture consisting of two flanking long terminal repeats (LTRs) and a proximate origin of DNA replication (autonomously replicating sequence, ARS), which are likely to promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we performed experimental evolution in glutamine-limited chemostats using engineered strains lacking either the adjacent LTRs, ARS, or all elements. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. We find that although GAP1 CNVs repeatedly form and sweep to high frequency in strains with modified genome architecture, removal of local DNA elements significantly impacts the rate and fitness effect of CNVs and the rate of adaptation. We performed genome sequence analysis to define the molecular mechanisms of CNV formation for 177 CNV lineages. We find that across all four strain backgrounds, between 26% and 80% of all GAP1 CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, a distal ARS can mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination mechanisms still mediate gene amplification following de novo insertion of retrotransposon elements at the locus. Our study demonstrates the remarkable plasticity of the genome and reveals that template switching during DNA replication is a frequent source of adaptive CNVs.

evolutionary biology↗

bioRxiv2023

Phenotype switching of the mutation rate facilitates adaptive evolution

The mutation rate plays an important role in adaptive evolution. It can be modified by mutator and anti-mutator alleles. Recent empirical evidence hints that the mutation rate may vary among genetically identical individuals: empirical evidence from bacteria suggests that the mutation rate can be affected by translation errors and expression noise in various proteins. Importantly, this non-genetic variation may be heritable via a transgenerational epigenetic mode of inheritance, giving rise to a mutator phenotype that is independent from mutator alleles. Here we investigate mathematically how the rate of adaptive evolution is affected by the rate of mutation rate phenotype switching. We model an asexual population with two mutation rate phenotypes, non-mutator and mutator. An offspring may switch from its parental phenotype to the other phenotype. We find that switching rates that correspond to so-far empirically described non-genetic systems of inheritance of the mutation rate lead to higher rates of adaptation on various fitness landscapes. These switching rates can maintain within the same individuals both a mutator phenotype and pre-existing mutations, a combination that facilitates adaptation. Moreover, non-genetic inheritance increases the proportion of mutators in the population, which in turn increases the probability of hitchhiking of the mutator phenotype with adaptive mutations. This in turns facilitates the acquisition of additional adaptive mutations. Our results rationalize recently observed noise in the expression of proteins that affect the mutation rate and suggest that non-genetic inheritance of this phenotype may facilitate evolutionary adaptive processes.

evolutionary biology↗

bioRxiv2023

Mutation rate, selection, and epistasis inferred from RNA virus haplotypes via neural posterior estimation

RNA viruses are particularly notorious for their high levels of genetic diversity, which is generated through the forces of mutation and natural selection. However, disentangling these two forces is a considerable challenge, and this may lead to widely divergent estimates of viral mutation rates, as well as difficulties in inferring fitness effects of mutations. Here, we develop, test, and apply an approach aimed at inferring the mutation rate and key parameters that govern natural selection, from haplotype sequences covering full length genomes of an evolving virus population. Our approach employs neural posterior estimation, a computational technique that applies simulation-based inference with neural networks to jointly infer multiple model parameters. We first tested our approach on synthetic data simulated using different mutation rates and selection parameters while accounting for sequencing errors. Reassuringly, the inferred parameter estimates were accurate and unbiased. We then applied our approach to haplotype sequencing data from a serial-passaging experiment with the MS2 bacteriophage. We estimated that the mutation rate of this phage is around 0.2 mutations per genome per replication cycle (95% highest density interval: 0.051-0.56). We validated this finding with two different approaches based on single-locus models that gave similar estimates but with much broader posterior distributions. Furthermore, we found evidence for reciprocal sign epistasis between four strongly beneficial mutations that all reside in an RNA stem-loop that controls the expression of the viral lysis protein, responsible for lysing host cells and viral egress. We surmise that there is a fine balance between over and under-expression of lysis that leads to this pattern of epistasis. To summarize, we have developed an approach for joint inference of the mutation rate and selection parameters from full haplotype data with sequencing errors, and used it to reveal features governing MS2 evolution.

evolutionary biology↗

bioRxiv2022

Cultural transmission, competition for prey, and the evolution of cooperative hunting

Although cooperative hunting (CH) is widespread among animals, its benefits are unclear. When rare, CH may allow predators to escape competition and access "big prey" (BP). However, a lone CH predator cannot such catch food. Cultural transmission may allow CH to spread fast enough that cooperators can find hunting partners, but competition for BP may increase. We construct a one-predator, two-prey model in which the predators either learn to hunt "small prey" (SP) alone, or learn to hunt BP cooperatively. The predators first learn vertically and then choose partners from which they learn horizontally with probability H. CH predators only catch the BP if their partner is cooperative. We find that without horizontal learning, CH cannot evolve when initially rare. Together, a high probability of horizontal learning and competition for the SP allow CH to evolve. However, CH can only fix in the predator population if the BP is very abundant. Furthermore, a mutant that increases horizontal learning can invade whenever CH is present but not fixed, because horizontal learning allows predators to match their strategies, avoiding the situation in which a cooperator cannot find a partner. While competition for prey is important for determining the degree of CH that evolves, it is not enough for CH to emerge and spread; horizontal cultural transmission is essential. Future models may explore factors that control how horizontal transmission influences cooperative predation, and vice versa. Lessons from our model may be useful in conservation efforts and wildlife reintroduction programs.

evolutionary biology↗

bioRxiv2021

Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these two parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use the observed CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based Bayesian likelihood-free inference approaches. We tested the suitability of two evolutionary models: a standard Wright-Fisher model and a chemostat growth model. We evaluated two likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in yeast as 10-4.7 -10-4 per cell division, and a selection coefficient of 0.04 - 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our estimates using barcode lineage tracking and pairwise fitness assays. Our results are consistent with a high beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining their outsized importance in rapid adaptive evolution. More generally, our study demonstrates the utility of novel simulation-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data.

evolutionary biology↗

bioRxiv2021

Epigenetically Inherited Mutation Rates Predicted to Maximize Adaptation Rates

Mutation rate plays an important role in adaptive evolution due to its effect on the rate of appearance of both beneficial and deleterious mutations and is therefore subject to second-order selection. The mutation rate varies between and within species and populations, increases under some stresses, and can be modified by mutator and anti-mutator alleles. It may also vary among genetically identical individuals: empirical evidence from bacteria suggests that the mutation rate can be affected by translation errors and expression noise in various proteins. Importantly, this non-genetic variation may be heritable via a transgenerational epigenetic mode of inheritance, giving rise to mutator phenotype switching that is independent from mutator alleles. Here we investigate mathematically how the rate of adaptive evolution on rugged, complex fitness landscapes is affected by the rate of mutation rate phenotype switching. Motivated by recent experimental results of mutation rate variation, we model an asexual population with two mutation rate phenotypes, non-mutator and mutator. An offspring may switch from its parental phenotype to the other phenotype. Thus, the mutation rate can be interpreted as a genetically inherited trait when the switching rate is low, as an epigenetically inherited trait when the switching rate is intermediate, or as a randomly determined trait when the switching rate is high. We find that intermediate switching rates maximize the rate of adaptation on rugged fitness landscapes. This is because an intermediate switching rate can maintain within the same individuals both a mutator phenotype and pre-existing mutations, a combination that facilitates the crossing of fitness valleys. Further, intermediate switching rates allow the population to quickly revert to a low mutation rate after adaptation is achieved, avoiding the accumulation of deleterious mutations linked to mutator alleles. Our results rationalize recently observed noise in the expression of proteins that affect the mutation rate and suggest that non-genetic inheritance of this phenotype may facilitate evolutionary adaptive processes.

evolutionary biology↗

bioRxiv2020

Oblique Transmission, Conformity, and Preference in the Evolution of Altruism

The evolution of altruism is frequently studied using models of non-random assortment, including kin selection. In genetic kin selection models, under certain assumptions including additive costs and benefits, the criterion for altruism to invade a population is Hamiltons rule. Deviations from Hamiltons rule occur when vertical transmission has cultural and genetic components, or when costs and benefits are combined multiplicatively. Here, we include oblique and vertical cultural transmission and genetic transmission in four models--two forms of parent-to-offspring altruism, sibling-to-sibling altruism, and altruism between offspring that meet assortatively--under additive or multiplicative assumptions. Oblique transmission may be conformist (anti-conformist), where the probability that an individual acquires a more common cultural variant is greater (less) than its frequency. Inclusion of conformist or anti-conformist oblique transmission may reduce or increase the threshold for invasion by altruism relative to Hamiltons rule. Thresholds for invasion by altruism are lower with anti-conformity than with conformity, and lower or the same with additive rather than multiplicative fitness components. Invasion by an allele that increases the preference for altruism does not depend on oblique phenotypic transmission, and with sibling-to-sibling altruism, this alleles invasion threshold can be higher with additive rather than multiplicative fitnesses.

evolutionary biology↗

bioRxiv2020

Non-Vertical Cultural Transmission, Assortment, and the Evolution of Cooperation

Cultural evolution of cooperation under vertical and non-vertical cultural transmission is studied, and conditions are found for fixation and coexistence of cooperation and defection. The evolution of cooperation is facilitated by its horizontal transmission and by an association between social interactions and horizontal transmission. The effect of oblique transmission depends on the horizontal transmission bias. Stable polymorphism of cooperation and defection can occur, and when it does, reduced association between social interactions and horizontal transmission evolves, which leads to a decreased frequency of cooperation and lower population mean fitness. The deterministic conditions are compared to outcomes of stochastic simulations of structured populations. Parallels are drawn with Hamiltons rule incorporating assortment and effective relatedness.

evolutionary biology↗

bioRxiv2020

Multiple regions of primate orofacial sensorimotor cortex encode bite force and gape

The precise control of bite force and gape is vital for effective breakdown and manipulation of food inside the oral cavity during feeding. Yet the role of the orofacial sensorimotor cortex (OSMcx) in the control of bite force and gape is still largely unknown. The aim of this study was to elucidate how individual neurons and populations of neurons in multiple regions of OSMcx differentially encode bite force and gape when subjects (Macaca mulatta) generated different levels of bite force at varying gapes. We examined neuronal activity recorded simultaneously from three microelectrode arrays implanted chronically in the primary motor (MIo), primary somatosensory (SIo), and cortical masticatory (CMA) areas of OSMcx. We used generalized linear models to evaluate encoding properties of individual neurons and utilized dimensionality reduction techniques to decompose population activity into components related to specific task parameters. Individual neurons encoded bite force more strongly than gape in all three OSMCx areas although bite force was a better predictor of spiking activity in MIo versus SIo. Population activity differentiated between levels of bite force and gape while preserving task-independent temporal modulation across the behavioral trial. While activation patterns of neuronal populations were comparable across OSMCx areas, the total variance explained by task parameters was context-dependent and differed across areas. These findings suggest that the cortical control of gape may rely on computations at the population level whereas the strong encoding of bite force at the individual neuron level allows for the precise and rapid control of bite force. Significance StatementBiting a piece off an apple requires precise sensorimotor control and coordination of bite force and gape by multiple brain regions. The cortical representations of bite force and gape by individual neurons and large populations of neurons across connected motor and somatosensory areas in orofacial cortex is unknown. Here we showed that bite force was more strongly encoded than gape by individual neurons in primary motor, somatosensory, and cortical masticatory areas. Moreover, bite force was more effectively represented in motor versus somatosensory cortices. At the population level, bite force and gape were distinguishable particularly when gape was randomized from trial-to-trial. The results are important for understanding neurophysiological processes underlying masticatory dysfunctions that may occur in aging, stroke, and Alzheimers disease.

neuroscience↗

bioRxiv2017

Generation of Variation and Mean Fitness Increase: Necessity is the Mother of Genetic Invention

Generation of variation may be detrimental in well-adapted populations evolving under constant selection. In a constant environment, genetic modifiers that reduce the rate at which variation is generated by processes such as mutation and migration, succeed. However, departures from this reduction principle have been demonstrated. Here we analyze a general model of evolution under constant selection where the rate at which variation is generated depends on the individual. We find that if a modifier allele increases the rate at which individuals of below-average fitness generate variation, then it will increase in frequency and increase the population mean fitness. This principle applies to phenomena such as stress-induced mutagenesis and condition-dependent dispersal, and exemplifies \"Necessity is the mother of genetic invention.\"

evolutionary biology↗

bioRxiv2017

Evolution of vertical and oblique transmission under fluctuating selection

Vertical and oblique cultural transmission of a dichotomous phenotype is studied under constant, periodic cycling, and randomly fluctuating selection. Conditions are derived for the existence of a stable polymorphism in a periodically cycling selection regime. Under such a selection regime, the fate of a genetic modifier of the rate of vertical transmission depends on the length of the cycle and the strength of selection. In general, the evolutionarily stable rate of vertical transmission differs markedly from the rate that maximizes the geometric mean fitness of the population. The evolution of rules of transmission has dramatically different dynamics from the more frequently studied modifiers of recombination, mutation, or migration.

evolutionary biology↗

bioRxiv2017

Key Issues Review: Evolution on rugged adaptive landscapes

Adaptive landscapes represent a mapping between genotype and fitness. Rugged adaptive landscapes contain two or more adaptive peaks: allele combinations that differ in two or more genes and confer higher fitness than intermediate combinations. How would a population evolve on such rugged landscapes? Evolutionary biologists have struggled with this question since it was first introduced in the 1930s by Sewall Wright.\n\nDiscoveries in the fields of genetics and biochemistry inspired various mathematical models of adaptive landscapes. The development of landscape models led to numerous theoretical studies analyzing evolution on rugged landscapes under different biological conditions. The large body of theoretical work suggests that adaptive landscapes are major determinants of the progress and outcome of evolutionary processes.\n\nRecent technological advances in molecular biology and microbiology allow experimenters to measure adaptive values of large sets of allele combinations and construct empirical adaptive landscapes for the first time. Such empirical landscapes have already been generated in bacteria, yeast, viruses, and fungi, and are contributing to new insights about evolution on adaptive landscapes.\n\nIn this Key Issues Review we will: (i) introduce the concept of adaptive landscapes; (ii) review the major theoretical studies of evolution on rugged landscapes; (iii) review some of the recently obtained empirical adaptive landscapes; (iv) discuss recent mathematical and statistical analyses motivated by empirical adaptive landscapes, as well as provide the reader with source code and instructions to implement simulations of adaptive landscapes; and (v) discuss possible future directions for this exciting field.

evolutionary biology↗