BioSystems

Mammalian spermatogenesis occurs in the seminiferous tubules, which exhibit unique spatiotemporal differentiation patterns known as cellular association patterns. In mice, these patterns can be regarded as one-dimensional wavetrains that consistently propagate inward from both ends, resulting in one or more "sites of reversal." Segmented wavetrain pattern, in which the wave propagation direction spatially switches, was observed in our previous three-species reaction-diffusion model for interspecific species difference in spermatogenic waves (Kawamura et al., 2021). However, the biological mechanisms of the formation of sites of reversal and of this directional bias, as well as the principle of pattern formation, remain unknown. Here, we refined our previous model to match the actual biological spatiotemporal scale and examined its dynamics through extensive numerical simulations. The modified model frequently generated segmented wavetrain patterns, corresponding to the sites of reversal, but without directional bias. We systematically examined possible biological mechanisms for the bias and found that tubule elongation, especially near the rete testis, most effectively accounts for the bias among the tested. Extensive simulations revealed that the segmented pattern is numerically stable, emerges more frequently in longer domains, and shows an exponential segment size distribution with a lower limit for the stably existing segment length. These explorations imply that locally emerged unidirectional wavetrains serve as building blocks to generate the stable segmented wavetrains through their interactions. HighlightsO_LISegmented wavetrains reflect sites of reversal in seminiferous tubules. C_LIO_LISegmented patterns frequently emerge but show no inherent directional bias. C_LIO_LITubule elongation may contribute to inward propagation near the rete testis. C_LIO_LISegmented wavetrains are numerically stable and more frequent in longer domains. C_LIO_LIInteractions of local unidirectional wavetrains generate stable segmented structures. C_LI

In multicellular organisms, various cellular structures exhibit cytoplasmic sharing, where cells remain interconnected. While essential for development and function in contexts such as germ cell formation and insect early embryos, the physical basis of cell volume regulation in these systems remains poorly defined. Germline cysts are formed by interconnected sister cells via intercellular bridges. In mice, germline cysts form during gametogenesis in fetal ovaries and testes. In mouse fetal female cysts, cells with numerous bridges preferentially differentiate into oocytes by selectively increasing their volume, a process that may be mediated through cytoplasmic flow. This volume bias may be influenced by hydrostatic pressure within the cytoplasm. Here, we theoretically investigate how the mechanical properties of cells affect cytoplasmic pressure and volume distribution within interconnected cells. Our soap-bubble model revealed that cells with more bridges exhibit increased volume when they have large cell-cell contact areas, as observed in fetal cysts. We found that incorporating cell cycle (including cell growth and cell division) significantly enhances the likelihood of volume bias in favor of cells with more bridges. These theoretical findings suggest that intrinsic mechanical properties, coupled with cell cycle, establish robust cyst development in fetal female germline cysts. Our findings also provide insights into the volume dynamics observed in adult male germline cysts, which are characterized by smaller cell-cell contact areas. Impact statementA theoretical model demonstrates how mechanical properties and cell cycle dynamics regulate volume distribution in germline cysts, providing a physical basis for oocyte differentiation.

Phenotypic diversity is often thought to arise from the evolutionary modification of developmental processes. However, developmental processes are tightly coupled in space and time, with each process beginning from conditions set by the one before it. While we know from dynamical systems theory that initial conditions can significantly affect a systems out-come, their importance as a source of phenotypic evolvability has been largely overlooked. Here we show for the first time, that phenotypic evolution can proceed through changes in developmental initial conditions while the underlying developmental process remains conserved. Somitogenesis is the process by which vertebral precursors, known as somites, are periodically patterned in the pre-somitic mesoderm (PSM). Somitic count (total number of somites) is thought to diversify through the evolution of components of somitogenesis such as the tempo of the segmentation clock or the mechanisms driving axial morphogenesis. Using two closely related species of Lake Malawi cichlid fishes that differ in vertebral counts, we show that somite count evolution has happened without changes to somitogenesis itself, but instead, by altering the size of the PSM at the onset of this process. This work will expand what we consider developmental drivers of phenotypic evolution and highlight the importance of comparative studies to understand the diversification of phenotypes.

During embryogenesis, as cells proliferate and assemble into tissues, they undergo a sequence of transitions between distinct molecular states eventually giving rise to a cellular population consisting of an appropriate distribution of specific functional cell types. Recent progress on the dynamics underlying decision-making in developmental landscape makes it feasible to start analysing the amount of information involved in constructing such systems. To explore this we introduce the notion of potency of a developmental landscape and attempt to calculate it for two development systems of current interest, in-vitro differentiation of epiblast-like cells into neural and mesodermal progenitors and the worm vulva patterning system. Our approach integrates concepts from developmental biology, information theory and dynamical systems to estimate both the number and identity of signalling regimes that give rise to distinguishable temporal response patterns.

Animals sense and integrate complex external cues to make developmental decisions that help them better survive and adapt to their natural habitats. Under environmental adversity, nematodes can enter an alternative developmental pathway to form a diapautic and stress-resistant stage, termed the dauer larvae. While dauer formation has been well characterized in Caenorhabditis elegans, how environmental factors influence analogous stages in other nematode species remains largely unexplored. This study examines how symbiotic bacteria, temperature, and pheromones affect the formation of the infective juvenile (IJ), a dauer-like stage, of the insect-parasitic nematode Steinernema hermaphroditum. In contrast to C. elegans, where dauer entry is promoted by heat, IJ development in S. hermaphroditum development is enhanced by reduced temperature. Moreover, the presence and absence of live symbiotic bacterium Xenorhabdus griffiniae functions as an ON-and-OFF switch that regulates the host IJ formation. Crude pheromone extracts from S. hermaphroditum liquid culture do not robustly induce IJ formation in a dose-responsive manner, unlike the potent pheromone-driven dauer entry observed in C. elegans. Nutrient-rich liver-kidney media that mimics host insect environment showed IJ entry induction in a pheromone-dependent manner. These data suggest that external cues, such as temperature, microbial diet, and pheromone, are perceived differently by S. hermaphroditum in comparison to that of C. elegans, reflecting species-specific adaptations to distinct ecological niches and life history strategies.

Studies of appendage regeneration in vertebrates have shown that the fundamental building block of any regenerative tissue is a blastema. The blastema is a cone-shaped accumulation that forms at the site of amputation post wound-healing and is the result of a highly coordinated process involving a cluster of cells capable of growth, migration, and differentiation. Although several key signaling pathways involved in regeneration have been identified, which cellular processes they control and how these processes are coordinated across space and time are not yet fully understood. This study introduces a computational tool to examine how the outgrowth results from the interaction of two tissue layers: the bulk (mesenchyme) and the overlying epithelium. We developed a novel hybrid agent-based modeling framework and an accompanying parameter inference pipeline to uncover the cellular properties in the epithelium and the mesenchyme driving the formation of a normal regenerative blastema with a morphology similar to that observed in experiments. Using our model, we report two conditions for blastema formation: retained local softening of the epithelial layer at the site of injury, which was confirmed experimentally with atomic force microscopy (AFM) measurements, and the involvement of the Wnt signaling pathway in the directed migration of mesenchyme cells towards the distal tip. Taken together, this combined experimental-theoretical approach provides a framework for understanding how the Wnt signaling pathway influences the formation of the early blastema at multiple levels of organization and how key cellular behaviors contribute to its formation. Author SummaryA small number of tetrapods have retained the ancestral ability to regenerate tissues and even limbs. Indifferent of species or tissue, the decisive initial stage of limb regeneration is the formation of a specialized structure called the blastema, a heterogeneous mass of mesenchymal cells, in a relatively short timescale of 2-14 days post injury or amputation. To study the mechanical and cellular conditions for limb blastema formation in the axolotl model organism, we developed a novel hybrid agent-based modeling framework and accompanying kinetic parameter inference pipeline. By recapitulating blastema morphometrics of healthy and stalled regenerative states, our model finds two conditions for blastema formation: retained local softening of the epithelial layer at the injury site post wound-healing, which we confirmed with atomic force microscopy measurements, and that the Wnt signaling pathway plays a role in the migration of mesenchyme cells to the distal tip in order to produce the blastema.

Developing effective antimicrobial strategies requires a predictive understanding of bacterial responses to multiple stress conditions which often result in multiple phenotypes. A microbes survival and proliferation depend on its ability to manage concurrent, dynamically varying stressors within its microenvironment. However, time-resolved predictive models that capture multi-phenotype responses are lacking, and single-phenotype models often fail to accurately replicate a microbes reaction to mixed stress conditions. In this work, we develop a mechanistic in-silico model of multi-stress response in Escherichia coli K12 and use it to characterize phenotype dynamics in iron-limited and hydrogen peroxide containing environments. Specifically, we replicate the iron and oxidative stress response networks in E. coli using a system of ordinary differential equations and applied a multi-phenotype parameterization scheme that leverages multi-measure empirical data, augmented metric-based sensitivity analysis, sequential parameter estimation, and ensemble modeling. Our approach resulted in robust models with a 93% accuracy when compared to experimental datasets across 20 stress-response categories, outperforming traditional single-phenotype approaches (80-87%). Analysis of posterior parameter distributions revealed that multi-phenotype optimization eliminates heavy-tailed distributions characteristic of poorly constrained fits and shifts parameter posteriors from boundary-concentrated to centrally localized forms, indicating improved identifiability. Simulation outcomes confirmed key features of E. colis iron metabolism, showing that moderate peroxide stress in an iron-rich environment creates significant adaptation challenges, leading to a bacteriostatic phenotype. The model provides insights into biochemical mechanisms important to E. colis temporal response to varying iron availability, with implications for ecological dynamics and pathogenesis. Our parameterization approach highlights the effectiveness of a combination of optimization methods and ensemble modeling in developing predictive models that are robust across multiple phenotypes. Results demonstrate that data structure, specifically the integration of multiple phenotypes and response outputs, proves to be as critical if not more critical than data volume for achieving well-constrained parameter estimates and robust predictions across experimental conditions. Author SummaryUnderstanding bacterial stress response is crucial for developing strategies to control bacterial populations, particularly as antibiotic resistance poses a growing threat, with "superbugs" potentially triggering a global health crisis. While mathematical models offer powerful tools to study biological systems, many struggle to predict cellular behavior across multiple phenotypes due to the complexity of responses. Iron metabolism is vital for bacterial survival, particularly under oxidative stress, leading to various bacterial growth dynamics. This work uses mathematical modeling to explore how E. coli manages multiple stressors, focusing on iron metabolism and oxidative stress. By applying a novel combination of optimization and ensemble modeling methods, we improved model accuracy by nearly 16%, enabling predictions of E. colis varied response to single, dual, and dynamic stress environments. Our approach offers a valuable tool for understanding and combating bacterial persistence, with future studies able to expand its use to determine how bacterial communities respond to multiple stressors.

Genes with similar expression profiles often exhibit similar functional properties. An "integrated similarity score" (ISS) is developed by combining different expression similarity measures through weights, obtained using biological information, for improving gene similarity. The expression similarity measures are converted to the common framework of positive predictive value using functional annotation. A fitness function, called "fitness function using functional annotation of genes" (FFFAG), is also developed by minimizing the difference between functional similarity value and the ISS. The FFFAG is used to determine the weight combination of different similarity measures in ISS. In addition, an existing similarity measure, called TMJ (integrated similarity measure by multiplying Triangle and Jaccard similarity), is also modified to incorporate biological knowledge involving functional annotation. The results demonstrate that ISS is superior to individual similarity measure to find similar gene pairs. Further, the ISS predicts the functional categories of 40 unclassified yeast genes at p-value cutoff of 10-10 from 12 clusters. The associated code is accessible at http://www.isical.ac.in/[~]shubhra/ISS.html.

Intratumor heterogeneity (ITH) arises from the combined effects of genetic alterations, clonal interactions, and environmental constraints, and plays a central role in therapeutic resistance and disease progression. While ITH has been extensively documented in empirical tumor data, the scientific debate regarding the biological mechanisms underlying this heterogeneity remains complex, highlighting the need for cancer evolution models that are sufficiently flexible and sophisticated to reproduce the observed behaviors and to give insights on the unobserved ones. Here, we present a stochastic modelling framework for tumor evolution that integrates genotypic inheritance with phenotype driven functional traits and resource mediated competition. Mutational events are associated with functional capabilities such as altered proliferation, increased mutation rates, limit evasion potential or enhanced control over shared resources, allowing multiple genotypes to converge on similar phenotypes. The model explicitly tracks subclonal lineages while incorporating environmental constraints that modulate growth and competition.The framework is defined through a mathematically rigorous construction and is accompanied by an efficient simulation algorithm. To facilitate exploration and reproducibility, we provide an open-source graphical user interface that allows users to configure model parameters, run simulations, and inspect clonal genealogies and population dynamics without requiring direct interaction with the underlying code. Using this model, we illustrate how ecological feedbacks can shape clonal dynamics over time, supporting an interpretation in which early tumor growth is dominated by stochastic expansion, while later evolution increasingly reflects selection for traits that alleviate environmental constraints. Rather than constituting a new evolutionary paradigm, this behaviour demonstrates how well-documented biological patterns can emerge naturally from a unified stochastic and ecological description. Overall, our approach offers a flexible and extensible platform for investigating how chance, functional traits, and environmental interactions jointly govern tumor heterogeneity. Author summaryNot all cancerous cells are created equal: inside the same tumor, different populations of cells exist at the same time, fighting for the same resources and influencing the way the disease evolves and reacts to treatments. These groups of cells have different behaviour and abilities thanks to different genetic mutations, which might give them an advantage or bring their population to disappearance. We have built a mathematical model that mimics the evolution of a tumor over time, simulating a competition between its different populations of cells. Our simulated experiments show that tumors evolve in two distinct phases: at first, cells that grow and divide more quickly have an advantage. Once the space and nutrients are limited, cells that can survive with fewer resources have an advantage and can potentially take over the race. We use these simulations to argue that the evolution of a tumor doesnt depend on the shape of the space it expands in, but rather on the availability of nutrients.

Mass-conserved reaction-diffusion systems are used as mathematical models for various phenomena such as cell polarity. Numerical simulations of this system present transient dynamics in which multiple stripe patterns converge to spatially monotonic patterns. Previous studies indicated that the transient dynamics are driven by a mass conservation law and by variations in the amount of substance contained in each pattern, which we refer to as "pattern flux". However, it is challenging to mathematically investigate these pattern dynamics. In this study, we introduce a reaction-diffusion compartment model to investigate the pattern dynamics in view of the conservation law and the pattern flux. This model is defined on multiple intervals (compartments), and diffusive couplings are imposed on each boundary of the compartments. Corresponding to the transient dynamics in the original system, we consider the dynamics around stripe patterns in the compartment model. We derive ordinary differential equations describing the pattern dynamics of the compartment model and analyze the existence and stability of equilibria for the reduced ODE with respect to the boundary parameters. For a specific parameter setting, we obtained results consistent with previous studies. Moreover, we present that the stripe patterns in the compartment model are potentially stabilized by changing the parameter, which is not observed in the original system. We expect that the methodology developed in this paper is extendable to various directions, such as membrane-induced pattern control.

Histidine kinases (HKs) are part of many signaling pathways, by being implicated in two components systems (TCS). Using autophosphorylation and phosphotransfer to a response regulators (RR), they enable organisms to adapt to their environment. Most HKs are transmembrane proteins with a sensing domain outside of the cell and two catalytic domains called HisKA and HATPase. HATPase is required for interaction with the ATP and HisKA contains the phosphorylated histidine residue. HKs are involved in various environmental adaptation mechanisms, like light sensing or biochemical changes. Studying their diversity is therefore important to better understand how cells interacts with their environment. There exist incomplete HKs (iHKs) lacking either the HisKA or HATPase domain. Some iHKs with an HATPase domain possess a section of their sequence where an HisKA domain could be expected. These iHKs may contain "true" HKs, with unknown HisKA domain, that could fill gaps in various signaling pathways. In this study we analyzed 869 964 sequences of iHKs having an HATPase domain but lacking an HisKA domain. We identified 18 HisKA-like profiles and did multiple meta-studies to assessed their HisKA-like characteristics. We found that their 3D structures matched the structure of known HisKA domains. We saw that the genomic context of the genes associated to these profiles contained genes implicated in signal transduction pathways. We cross-validated some of our profiles with curated annotations, as well as with a "negative dataset" made of non-HK proteins. We believe that our work could help improve the annotation of regulation pathways in prokaryotes.

Severe global declines in coral populations have driven growing demand for human intervention and restoration. One goal of restoration is to repopulate reef ecosystems through outplanting, which requires detailed understanding of target systems. However, long term ecological and reproductive data from interventions remain scarce. An exception to this are the critically endangered Caribbean corals, Acropora palmata and A. cervicornis, which have been central to restoration efforts in the region. These species serve as a unique case study due to the abundance of published data spanning ecology, and reproductive biology. In the wild, these species can cross to form an F1 hybrid, A. prolifera, though it is rarely used in restoration. It remains unclear whether A. prolifera is an evolutionary dead-end competing with its parents, or a potential bridge enabling genetic exchange via backcrossing. To evaluate benefits and risks of restoration among Caribbean Acropora, we developed a two-dimensional agent-based simulation using reproductive and ecological data to model realistic reef dynamics. Our model suggests the hybrid can facilitate introgression between parentals without outcompeting them. Yet, such introgression is too limited for large-scale or beneficial ancestry transfer except under ecologically unrealistic conditions or timescales significantly longer than those relevant for management. Thus, our model suggests that the risks of genetic swamping may be overstated, whereas hopes for adaptive introgression are also low, underscoring the value of simulations for generating long-term ecological and evolutionary insights relevant to coral restoration.

Exploring the emergence of spatio-temporal patterns due to nonlinearities in gene expression is a relatively new development. In this work, we explore the effect of resource constraint on gene regulatory motif from both equilibrium and spatio-temporal standpoint, taking into consideration the degradation class of resource, protease. We have demonstrated that protease-tagged degradation can cause an emergent bistability to form in the system in a steady-state scenario. Instead of a graded linear response in protein synthesis, two Saddle-node bifurcations caused by protease competition provide a switch-like response with hysteresis, where two drastically differing protein concentrations can coexist. We next turn our attention to spatio-temporal analysis: we extend our study for a two-dimensional sheet of cells with diffusible protein molecules and report the stationary patterns.To investigate the reasons behind these non-homogeneous stationary patterns, we investigate the traveling wave solution and observe that a stationary pattern is formed by the traveling wave solution. Considering that proteases play a major role in the regulation and expression of genes in a variety of diseased scenarios, the repercussions of this spatial patterning caused by protease competition can be extensive in gene regulatory systems.

The spatial structure of populations may promote the emergence and maintenance of cooperation. Cooperation in the prisoners dilemma is favored under specific update rules in evolutionary graph theory models with one individual per node of a graph, but this effect vanishes in models with well-mixed demes connected by migrations under soft selection. In contrast, experiments and models involving cycles of growth, merging and dilution have shown that spatial structure can favor cooperation. Here, we reconcile these findings by studying deme-structured populations under growth-merging-dilution dynamics, corresponding to a clique (fully connected graph) under hard selection. We obtain analytical conditions for the cooperator fraction to increase during deterministic logistic growth, and to increase on average under dilution-growth-merging cycles, in the weak selection regime. Furthermore, we analytically express the fixation probability of cooperators under weak selection, yielding a criterion for cooperative mutants to have a higher fixation probability than neutral ones. Finally, numerical simulations show that stochastic growth further promotes cooperation. Overall, hard selection is essential for cooperation to be promoted in deme-structured populations.

AO_SCPLOWBSTRACTC_SCPLOWWe study an extension of an environmentally mediated epidemiological model that incorporates direct human-to-human transmission. While the original formulation accounted for environmental exposure, it did not include direct transmission between individuals. Allowing both transmission routes to interact leads to significant qualitative changes in the system dynamics. The analysis reveals multiple dynamical regimes governed by environmental and combined threshold quantities. The stability of the disease-free equilibrium is controlled by an environmental threshold, whereas a combined reproduction number determines the onset of multistability. For certain parameter ranges, endemic equilibria coexist with the disease-free equilibrium, giving rise to backward-type bifurcation behavior and sensitivity to initial conditions. Moreover, the direct transmission rate acts as an organizing parameter by inducing the emergence of an environmental-free equilibrium when exceeding its classical threshold. These results highlight how environmentally coupled transmission mechanisms can generate rich dynamics in low-dimensional models.

BackgroundThe Proliferation of Multi-drug resistant (MDP) ESKAPE pathogens threatens to compromise the efficacy of standard antibiotic pharmacopoeia. Current antimicrobial stewardship strategies predominantly rely on reactive antibiograms selecting therapeutic agents based on immediate phenotypic susceptibility. This approach, while clinically expedient, often inadvertently selects for cross-resistance, driving the evolutionary trajectory toward pan-drug resistance. A paradigm shift is required toward predictive strategies that exploit evolutionary trade-offs, specifically Collateral Sensitivity (CS), where the acquisition of resistance to one agent induces hypersensitivity to another. MethodsWe developed a computational graph-theory framework to map the evolutionary trajectories of three critical Gram-negative pathogens: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Drawing upon validated CS interaction matrices from experimental evolution literature, we constructed directed weighted graphs where nodes represent antibiotics and edges represent evolutionary sensitivity trade-offs. A closed-loop cycle optimization algorithm was deployed to identify pathogen-specific "Trap Loops" sequences of three or more antibiotics that force the pathogen into a state of high sensitivity. These loops were validated via stochastic in-silico clinical trials simulating 18 months of treatment, explicitly modeling clinical error and biological noise. ResultsThe model identified distinct, optimal cycling protocols for each pathogen. For E. coli, an Aminoglycoside-Beta Lactam loop (Gentamicin to Cefuroxime to Fosfomycin) demonstrated sustained suppression of resistance accumulation in silico. For K. pneumoniae, a novel Rifampicin to Doxycycline to Colistin loop was identified. For P. aeruginosa, a Tobramycin to Ciprofloxacin to Piperacillin sequence proved optimal. Stochastic simulations demonstrated that while standard reactive care resulted in progressive resistance accumulation (Normalized Resistance > 2.5), the graph-optimized protocols suppressed resistance within the therapeutic window (Normalized Resistance < 0.2) for the duration of the simulation. ConclusionWe demonstrate, through computational modeling, that antibiotic resistance trajectories can be strategically constrained by optimizing the temporal sequence of existing agents. This study provides a computational framework to inform the transition from reactive prescribing toward precision evolutionary steering. These protocols are intended to complement, not replace, clinical judgment and local antibiograms, particularly regarding pharmacokinetic constraints.

Historically, venom potencies have been assessed using measures of lethality, such as the median lethal dose (LD50). However, venoms may be selected primarily for their ability to rapidly incapacitate rather than cause mortality, meaning LD50 may not capture the efficacy of venoms in an ecological and evolutionary context. To capture this context, recent studies have adapted measures that assess venoms ability to rapidly incapacitate, such as the median effective dose (ED50). However, while ED50 values are expected to provide a more proximate assessment of ecological variation in venom potency, it is unknown whether historically available LD50 values are still useful proxies of ecologically relevant potency or whether they capture independent axes of venom variation. Here, we test the relationship between LD50 and ED50 in spider venoms by experimentally estimating LD50 and ED50 for 12 species and collating additional potency data for 40 species retrieved from the literature. We observed an isometric relationship between LD50 and ED50 in both analyses, showing these potency measures are both strongly coupled, with an increase in paralysis efficiency associated with a similar increase in lethality. Our results suggest that the functional aspects of venom potency, paralysis and lethality, are intrinsically linked, and due to this strong mechanistic coupling, historically available LD50 values may be used to compare general venom potencies in spiders, provided that they are based on the same prey model.

We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA addresses this limitation by embedding the search domain into a toroidal geometry, thereby eliminating artificial boundaries and enabling continuous cyclic exploration. Beyond boundary handling, TSA uses winding numbers to capture the history of agent movement across periodic dimensions, which are exploited to adaptively refine local search. A modified sigmoid control function regulates the transition between global and local search. Performance of TSA is evaluated on a collection of unimodal and multimodal benchmark functions at various dimensions. It consistently outperforms established metaheuristics. Notably, TSA demonstrates exceptional robustness to increasing dimensionality, maintaining fast convergence and low variance where competing methods deteriorate. To assess real-world applicability, we apply TSA to an inverse problem from mathematical oncology. With both synthetic and clinical data, TSA reliably recovers physiologically plausible parameters with greater stability and predictive accuracy than competing algorithms. These results demonstrate that TSA is a powerful and robust tool for large-scale global optimization in computational modelling applications. Striking Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/709766v1_ufig1.gif" ALT="Figure 1"> View larger version (131K): org.highwire.dtl.DTLVardef@1b2c994org.highwire.dtl.DTLVardef@d045a8org.highwire.dtl.DTLVardef@18d296corg.highwire.dtl.DTLVardef@9a972d_HPS_FORMAT_FIGEXP M_FIG C_FIG Image generated with Google Gemini.

A common assumption in molecular evolution is the fixed selection nature of a mutation, for instance, a neutral mutation is selectively neutral for all individuals who carry the mutation, and so forth a deleterious or beneficial mutation. Our recent work challenged this presumption, postulating that individuals with a specific mutation exhibit a fluctuation in fitness, short for FSI (fluctuating selection among individuals). Moreover, an intriguing phenomenon called selection-duality emerges, that is, a slightly beneficial mutation could be a negative selection (the substitution rate less than the mutation rate). It appears that selection-duality is bounded: the low-bound is the generic neutrality where the mutation is neutral by the means of fitness on average, while the up-bound is the substitution neutrality where the substitution rate equals to the mutation rate. In this paper, we conducted a thorough theoretical analysis to evaluate how many generations needed for a selection-duality mutation to be fixed in a finite population. A striking finding is that the mean fixation time of a selection-duality mutant, including the generic neutrality and the substitution neutrality, is approximately identical, which is considerably shorter than the case of strict neutrality without FSI. One may further envisage that the fast-fixation nature of selection-duality mutations could result in a considerable genetic reduction at linked sites.

The budding yeast S. cerevisiae, a foundational model for cell cycle studies, exhibits a complex phase organisation (G1, S, G2/M) governed by checkpoints ensuring faithful cellular inheritance. However, the existence of a distinct G2 phase in yeast remains debated, with some advocating for a prometaphase instead. To address this issue, we developed a discrete-event, qualitative, and possibilistic model, the first one to our knowledge, to integrate organelle-level components (replication forks, sister chromatids, mitotic spindle, bud) while remaining parsimonious. Unlike molecular-centred or overly complex whole-cell models, this approach bridges broad systemic and finer mechanistic scales. Our results demonstrate that the model faithfully recapitulates cell cycle progression and supports the dispensable G2 phase. This possibilistic model inspired from recent applications in ecology advocates in favor of the necessity of prometaphase. This study thus provides a unifying and flexible framework to resolve long-standing ambiguities in yeast cell dynamics, while avoiding the pitfalls of excessive complexity or reductionism.