PLOS Computational Biology: New Articles

Population dynamics of generalist and specialist strategies under feast-famine cycles

2026-04-30T14:00:00Z

by Rintaro Niimi, Chikara Furusawa, Yusuke Himeoka

Microbial populations exhibit a broad spectrum of nutrient utilization strategies, ranging from those utilizing diverse nutrients, called “generalists,” to those highly adapted to specific nutrients, called “specialists.” Identifying the conditions for the diversification of nutrient utilization strategies is one of the central questions in ecology. Previous theoretical studies have shown that trade-offs among different resource utilization functions in which cells cannot utilize broad types of substrates at nearly optimal efficiency are crucial for the emergence of diverse strategies. Additionally, in natural settings, nutrient availability often fluctuates over time, imposing another trade-off on the cells; cells that grow rapidly under nutrient-rich conditions tend to have a higher death rate under nutrient-poor conditions, leading to a growth-death trade-off. This additional trade-off can contribute to the emergence of diverse strategies. Here, we introduce a mathematical model that simultaneously incorporates the resource-use trade-off and the growth-death trade-off. Nutrient supply was modeled as discrete stochastic events, mimicking temporal changes in nutrient availability. We show that the phenotype with a higher ratio of growth rate to death rate dominates the population; that is, the strength of the growth-death trade-off plays a crucial role in the emergence of distinct strategies. We also found that a sparse and uncertain nutrient supply favors specialists, increasing their temporally averaged abundance. Our findings highlight the crucial role of temporal environmental variation and the resulting growth-death trade-off in driving diversification of microbial nutrient utilization strategies.

Learning the bistable cortical dynamics of the sleep-onset period

2026-04-30T14:00:00Z

by Zhenxing Hu, Manaoj Aravind, Xu Lei, J. Nathan Kutz, Jean-Julien Aucouturier

Humans just don’t fall asleep like a log – or step-function. Rather, the sleep-onset period (SOP) exhibits dynamic and non-monotonous changes of electroencephalogram (EEG) with high, and so far poorly understood, intra- and inter-individual variability. Computational models of the sleep regulation network have suggested that the transition to sleep can be viewed as a noisy bifurcation at a saddle node which is determined by an underlying control signal or “sleep drive”. However, such models do not describe how internal control signals in the SOP can produce rapid switches between stable wake and sleep states, nor how these state-space changes are translated in the macroscopic EEG. Here, we propose a minimally-parameterized stochastic dynamical model, in which one slowly-varying control parameter drives the wake-to-sleep transition while exhibiting noise-driven bistability. We provide a procedure for estimating the parameters of the model given single observations of experimental sleep EEG data, and show that it can reproduce a wide variety of SOP phenomenology. Using the model to analyze a pre-existing sleep EEG dataset, we find that the estimated model parameters correlate with subjective sleepiness reports. These results suggest that the bistable characteristics of the SOP can serve as biomarkers for tracking intra- and inter-individual variability of sleep-onset disorders.

Cell type annotation for scATAC-seq via DNA large language model and graph domain adaptation

2026-04-30T14:00:00Z

by Yan Liu, Sheng Guan, He Yan, Long-Chen Shen, Ji-Peng Qiang, Guo Wei

Single-cell ATAC-seq (scATAC-seq) enables the exploration of chromatin accessibility at single-cell resolution, offering critical insights into gene regulation. Accurate cell type annotation is a fundamental prerequisite in scATAC-seq analysis. While cross-modality annotation methods leverage scRNA-seq data for label transfer, they often suffer from modality mismatch and signal distortion. Intra-modality annotation, which utilizes only scATAC-seq reference data, has gained attention for its biological consistency. However, existing methods are limited by insufficient sequence representation and lack of neighborhood modeling during domain adaptation. To address these limitations, we propose scLLMDA, a novel framework for scATAC-seq cell type annotation via DNA large language model and graph-based domain adaptation (GDA). scLLMDA uses a pretrained DNA-specific language model to generate contextual embeddings of peak sequences, which are then integrated with accessibility information to represent individual cells. We construct similarity-based cell graphs for both source and target datasets, and apply a graph neural network to align domains while preserving local structural context. Our approach captures rich sequence semantics and neighborhood dependencies, enabling more accurate and robust cell type annotation across datasets. Extensive experiments on multiple benchmarks demonstrate that scLLMDA outperforms existing methods in accuracy. The source code and implementation of scLLMDA are publicly available at: https://github.com/sheng-guan-2001/scLLMDA.

Evolutionary Kuramoto dynamics unravels origins of chimera states in neural populations

2026-04-30T14:00:00Z

by Thomas Zdyrski, Scott Pauls, Feng Fu

Neural synchronization is central to cognition. However, incomplete synchronization often produces chimera states, where coherent and incoherent dynamics coexist. Recent studies have suggested that these chimera states could be important in human cognitive organization. In particular, chimera states have been suggested as a regulator of cognitive integration and regulation with varying quality as humans age. While previous studies have explored such chimera states using networks of coupled oscillators, it remains unclear why neurons commit to communication or how chimera states persist. Here, we investigate the coevolution of neuronal phases and communication strategies on directed, weighted networks where interaction payoffs depend on phase alignment and may be asymmetric due to unilateral communication. The graph structure enables us to apply a game-theoretic model of Kuramoto-like oscillators to brain connectomes, and the asymmetry captures biochemical differences between communicative and non-communicative neurons. Combined, these two generalizations enable us to apply the computationally-tractable game-theoretic model of Kuramoto models to realistic brain networks and analyze the role of connectome structure on neuron communication. We find that both connection weights and directionality influence the stability of communicative strategies—and, consequently, full synchronization—as well as the strategic nature of neuronal interactions. Applying our framework to the C. elegans connectome, we show that emergent payoff structures, such as the staghunt game, control population dynamics. We demonstrate that weighted, directed connectivity in the Caenorhabditis elegans (C. elegans) connectome is sufficient to generate robust chimera states modulated by payoff asymmetries. Our computational results demonstrate a promising neurogame-theoretic perspective, leveraging evolutionary graph theory to shed light on mechanisms of neuronal coordination beyond classical synchronization models.

Evaluating the utility of amino acid similarity-aware kmers to represent TCR repertoires for classification

2026-04-30T14:00:00Z

by Hannah Kockelbergh, Shelley C. Evans, Liam Brierley, Peter L. Green, Andrea L. Jorgensen, Elizabeth J. Soilleux, Anna Fowler

Insights gained through interpretation of models trained on the T-cell receptor (TCR) repertoire contribute to advances in understanding of immune-mediated disease. This has the potential to improve diagnostic tests and treatments, particularly for autoimmune diseases. However, TCR repertoire datasets with samples from donors of known autoimmune disease status generally include orders of magnitude fewer samples than TCR sequences. Promising TCR repertoire classification approaches consider relationships between non-identical TCR sequences. In particular, kmer methods demonstrate strong and stable performance for small datasets. We propose a TCR repertoire representation that considers the relationships between amino acids within kmers flexibly and efficiently. XGBoost and logistic regression models are trained and tested on kmer representations of TCR repertoire datasets including samples from patients with coeliac disease as well as donors with previous cytomegalovirus infection. XGBoost models outperform logistic regression, indicating that interactions may be crucial for discriminative ability. We find that a reduced alphabet based on BLOSUM62 can lead to a model with slightly stronger XGBoost testing performance than other kmer features. Though it remains unclear whether there is an amino acid encoding that can substantially improve TCR repertoire classification with reduced alphabet kmers, evidence that this representation enables faster training of XGBoost models in comparison to kmer clusters suggests that our reduced alphabet approach permits wider exploration of amino acid similarity in practice. Finally, we detail motifs which are important in each top-performing XGBoost model and compare them to TCR sequences previously associated with each immune status. We highlight the challenge of interpreting non-linear TCR repertoire classification models trained on kmers which, if overcome, could lead to biomarker discovery for autoimmune diseases.

Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach

2026-04-30T14:00:00Z

by Matthew Herbert Ning, Haoqi Sun, Brice Passera, Duygu Bagci Das, Brandon Westover, Alvaro Pascual-Leone, Emiliano Santarnecchi, Mouhsin M. Shafi, Recep A. Ozdemir

Background

Substantial variability in individual responses to intermittent theta-burst stimulation (iTBS) limits its clinical efficacy, yet neurophysiological mechanisms underlying this variability remain unclear. While most machine-learning studies have focused on modeling behavioral or clinical effects of repetitive transcranial magnetic stimulation (rTMS), the few studies examining neurophysiological outcomes utilized limited feature sets in single-visit settings, which captured only inter-subject variability and most importantly lacked independent validation sets.

Methods

To address these gaps, we employed supervised machine learning models that integrated baseline resting-state EEG (rsEEG) features and baseline transcranial magnetic stimulation (TMS)-evoked measures, including motor-evoked potentials (MEPs) and TMS-evoked potentials (TEPs), to predict neurophysiological responses to a single iTBS session applied over the primary motor cortex in two independent test-retest studies of healthy adults. We also employed statistical and reliability analysis to understand the statistical relationship between resting state EEG and responses to iTBS.

Results

Internal cross-validation within the training cohort yielded promising binary classification performance (accuracy: 81%), identifying coarse-grained multiscale distribution entropy of rsEEG as the most robust predictor of local cortical excitability changes indexed by the 100–131ms window of TEPs. However, predictive performance markedly declined upon external validation (accuracy: 69%), reflecting unstable relationships between predictors and outcomes likely driven by substantial intra- and inter-individual variability of iTBS-induced changes in neurophysiological outcomes.

Conclusions

These findings emphasize that while EEG complexity measures can capture baseline brain states relevant for neuromodulation to a certain degree, the inherent instability of single-session iTBS effects significantly constrains model generalizability and underscores the necessity of test-retest paradigm to avoid overly optimistic performance estimates. Future studies with multi-session and individualized stimulation protocols are urgently needed to better characterize neurophysiological mechanisms underlying rTMS effects and ultimately enhance its therapeutic potential.

Stochastic intracellular calcium dynamics show preserved structures identified by deep learning classification

2026-04-29T14:00:00Z

by Jaesung Choi, Athokpam Langlen Chanu, Shakul Awasthi

Intracellular calcium ions (Ca²⁺) exhibit diverse dynamical behaviors linked with cellular physiological states related to health and disease. While deterministic models predict how biochemical parameters create distinct dynamical regimes — steady states, oscillations, bursting, chaos, and multiple periodicity — real biological systems are inherently stochastic due to finite molecular populations. Previous studies using conventional statistical measures demonstrated that increasing intrinsic fluctuations render these dynamical states increasingly indistinguishable, particularly for chaotic and multiple-periodicity patterns. This raises whether parameter-dependent organizational principles persist under realistic noise levels to remain biologically meaningful and computationally detectable. We address this using a large-kernel convolutional neural network (LKCNN) designed to capture global dynamical features across noise levels. Using chemical Langevin equations to generate synthetic training data with realistic intrinsic fluctuations, the LKCNN achieves ~90% accuracy in classifying eight distinct dynamical states despite noise levels that visually obscure distinctions. Validation with experimental Ca²⁺ data from pancreatic β-cells as well as other cells, including WT-HEK293, STIM-KO, and ORAI TKO, achieves 96.8% accuracy, confirming generalizability beyond synthetic datasets, substantially outperforming conventional baselines (Support Vector Machine and Random Forest), which achieve only 54.0% and 51.6% accuracy respectively on the same experimental data. These results demonstrate that deterministic organizational signatures persist through realistic biological noise, suggesting parameter-dependent dynamical structures represent robust principles governing cellular function. Our findings establish that sophisticated pattern recognition can bridge theoretical deterministic dynamics and noisy biological reality, offering a framework for extracting meaningful dynamical information from inherently stochastic oscillatory biological processes.

Computational insights on the molecular interplay between KRas (G12D mutation) and SOS1 modulated by the inhibitor BI-3406

2026-04-29T14:00:00Z

by Juan Zeng, YiXuan Lan, Fei Xia

Ras proteins are prominent oncogenes, with KRas mutations found in approximately 80% of cancer cells harboring Ras mutations. The mechanism by which Ras mutations cause cancer remains unclear. Human Son of Sevenless (SOS) promotes the GDP-to-GTP exchange in the inactive GDP-bound Ras (RasGDP) by interacting with RasGDP conformation, thereby leading to the development of human cancer. Elucidating the Ras-SOS interaction mechanism can guide the drug design for Ras and SOS proteins. Based on our previously sampled special structure KRasGDP·Mg²⁺_S1.2, this study constructs a functional ternary complex (KRasGDP·Mg²⁺)·SOS1·(KRasGTP·Mg²⁺). Furthermore, the KRas-SOS1 interactions regulated by the KRas G12D mutation and the SOS1 inhibitor BI-3406 that reportedly exhibits synergistic effects with G12D-mutant Ras inhibitors, are investigated through molecular dynamics (MD) simulations. The findings reveal that the G12D mutation and BI-3406 both affect the KRas-SOS1 interaction via the Switch-II (SW2) region of KRas. The negatively charged Asp12 has a repulsive effect on KRas, particularly on SW2, altering the interfacial electrostatic landscapes and diminishing the binding affinities by approximately 25 kcal/mol for both KRasGDP·Mg²⁺ and KRasGTP·Mg²⁺. BI-3406 forms a hydrogen-bond bridge between SW2 and SOS1 in wild type (WT) KRas, interrupting the interactions among the N-terminal residues of SW2 and SOS1. Moreover, BI-3406 was found here to attenuate the binding affinity of both WT and G12D-mutant KRasGDP·Mg²⁺ to SOS1, Interestingly, BI-3406 hardly affects the binding affinity of WT KRasGTP·Mg²⁺, while enhances the binding affinity of G12D-mutant KRasGTP·Mg²⁺. The change of binding affinity makes the catalytic pocket of SOS1 prefer to KRasGTP·Mg²⁺ and inhibits the growth of G12D-mutant KRas-driven tumors. These mechanistic insights provide valuable information for designing SOS1-co-targeting inhibitors to potentiate antitumor efficacy against G12D-mutated KRas.

Polyandry: A threat or an opportunity for the sterile insecttechnique?

2026-04-29T14:00:00Z

by Marine A. Courtois, Louise van Oudenhove, Suzanne Touzeau, Frédéric Grognard, Ludovic Mailleret

The sterile insect technique (SIT) is a pest control strategy based on the mass release of sterilized males to disrupt natural reproduction and suppress wild populations. However, its effectiveness can be challenged by biological factors such as female multiple mating and sperm use bias. While multiple mating is widespread among many insect species, the mechanisms governing sperm use remain poorly understood. In this study, we develop and analyze a compartmental mathematical model based on differential equations to investigate the overall impact of multiple mating on SIT efficiency. We further analyze the effect of sperm use biases with an agent-based model, calibrated on Drosophila suzukii, allowing the exploration of different scenarios: preferential use of first vs last sperm, of fertile vs sterile sperm, and mixed sperm use. Our results highlight how multiple mating and sperm use biases influence SIT effectiveness. In the longer term, multiple mating is disadvantageous as it requires additional releases of sterilized males to control the pest population. However, in the shorter term, it can be beneficial by disrupting further female reproductive output by “defertilizing” females mated with wild males. This study provides new information on how the way sperm is processed after mating can impact sterile insect control strategies, highlighting the limited influence of these biological processes depending on the release efforts that can be deployed.

Semi-parametric empirical bayes method for multiplet detection in snATAC-seq with probabilistic multi-omic integration

2026-04-29T14:00:00Z

by Yuntian Wu, Haoran Hu, Wei Chen, Johann E. Gudjonsson, Lam C. Tsoi, Xiaoquan Wen

Multiplets arise when multiple cells are captured within the same droplet during single-cell sequencing, producing hybrid molecular profiles that can distort downstream analyses. Detecting multiplets in single-nucleus ATAC-seq (snATAC-seq) data is particularly challenging due to the sparsity and overdispersion of chromatin accessibility measurements. Moreover, computational approaches that jointly leverage evidence across multiple features and data modalities are highly desirable for multiplet detection. We introduce SEBULA, a semi-parametric empirical Bayes framework for multiplet detection in snATAC-seq data. SEBULA models the singlet background directly from observed chromatin accessibility signals using fragment-level information from snATAC-seq data. This approach avoids reliance on synthetic doublets and produces classification probabilities that enable direct false discovery rate control. We further extend SEBULA to integrate complementary evidence from additional features and modalities, such as simultaneously measured gene expression profiles. Across simulations and seven multimodal datasets with hashing-based ground truth, SEBULA demonstrates improved sensitivity and specificity compared with existing snATAC-seq methods. The evidence integration framework achieves comparable or superior performance relative to state-of-the-art multiomic approaches while maintaining computational efficiency.

Individual and population level uncertainty interact to determine the performance of outbreak surveillance systems

2026-04-29T14:00:00Z

by Callum R. K. Arnold, Alex C. Kong, Amy K. Winter, William J. Moss, Bryan N. Patenaude, Matthew J. Ferrari

Background

Outbreak detection frequently relies on imperfect individual-level case diagnosis. Both outbreaks and cases are discrete events that can be misclassified and uncertainty at the case level may impact the performance of outbreak alert and detection systems. Here, we describe how the performance of outbreak detection depends on individual-level diagnostic test characteristics and population-level epidemiology, and describe settings where imperfect individual-level tests can achieve consistent performance comparable to “perfect” diagnostic tests.

Methodology

We generated a stochastic SEIR model to simulate daily incidence of measles (i.e., true) and non-measles (i.e., noise) febrile rash illness. We modeled non-measles sources as either independent static (Poisson) noise, or dynamical noise consistent with an independent SEIR process (e.g., rubella). Defining outbreak alerts as the exceedance of a threshold by the 7-day rolling average of observed test positives, we optimized the threshold that maximized outbreak detection accuracy across sets of noise structures and magnitudes, diagnostic test accuracy (consistent with either a perfect test, or proposed rapid diagnostic tests), and testing rates.

Conclusions

The optimal threshold for each diagnostic test typically increased monotonically with testing rate. With static noise, outbreak detection with RDT-like and perfect tests achieved accuracies of 90%, with comparable delays to outbreak detection. With dynamical noise, the accuracy of perfect test scenarios was superior to those achieved with RDTs (≈ 90% vs. ≤ 80%). Outbreak detection accuracy declined as dynamical noise increased and leads to permanent alert status with RDT-like tests at very high noise. The performance of an outbreak detection system is highly sensitive to the structure and the magnitude of the background noise. Depending on the epidemiological context, outbreak detection using RDTs can perform as well as perfect tests.

CASER: A semi-supervised model with multi-omics data integration prioritizes cancer-associated epigenetic regulator genes

2026-04-28T14:00:00Z

by Hao Li, Chaohuan Lin, Liyu Liu, Jie Lyu, Zhen Feng

Prioritizing a reliable list of cancer-associated epigenetic regulators (cERs) is critical for cancer diagnosis and discovery of drug targets. While various cERs have been proposed to play important roles as cancer drivers, we anticipate that further cERs can be identified through computational analyses. In this study, we introduce a semi-supervised machine-learning approach based on tri-training model, termed Cancer-ASsociated Epigenetic Regulator identification (CASER). CASER integrates a wide range of multi-omics-derived features, including mutational, genomic, epigenetic, and transcriptomic data, to prioritize cERs as well as the four functional subtypes of cERs. When evaluated against an independent gene set, CASER demonstrates superior predictive performance compared to various other supervised machine-learning and deep semi-supervised models. CASER identified novel cERs that demonstrated cancer-driving potential and essentiality for cell survival. These novel cERs were comparable to established cancer driver genes and outperformed existing approaches for cER prediction. CASER identified dozens of novel cERs, of which six candidate cERs were shown to have roles in altering cell proliferation in four cancer cell lines. Furthermore, the prioritized cERs, particularly dual-role cERs, are more associated with anti-cancer medicines, underscoring their potential as therapeutic targets in cancer. Our study can offer valuable insights of cERs for future functional studies, advancing the understanding of their role in cancer biology.

Bifurcation of neural firing patterns driven by potassium dynamics and neuron–electrode geometry during high-frequency stimulation

2026-04-28T14:00:00Z

by Yue Yuan, Junyang Zhang, Chen Wang, Hao Yan, Ning Zhang, Kun Zhang, Zheshan Guo, Zhaoxiang Wang

High-frequency stimulation (HFS), the basis of deep brain stimulation, elicits diverse neuronal responses, yet the mechanisms remain unclear. Classical conduction block theories cite sodium channel inactivation and axonal failure but cannot explain the abrupt, reproducible firing transitions observed in vivo. Here, we combine single-unit recordings from rat CA1 neurons with a biophysically detailed multi-compartment model to examine how HFS shapes axonal excitability. The results show that neuronal responses are governed by two coupled factors: the electrode–axon geometry and peri-axonal extracellular potassium ([K⁺]_o) dynamics. Small changes in either parameter reliably triggered bifurcation-like transitions between tonic, clustered, and low-rate regular firing. Conduction block preceded initiation failure with increasing electrode-axon distance, whereas elevated [K⁺]_o shifted membranes between excitable and non-excitable states. This unified bifurcation framework extends the conduction block hypothesis, recasts axons as nonlinear elements, and provides mechanistic insights to optimize electrode placement, stimulation tuning, and closed-loop neuromodulation strategies.

QoALa: A comprehensive workflow for viral quasispecies diversity comparison using long-read sequencing data

2026-04-28T14:00:00Z

by Nakarin Pamornchainavakul, Declan C. Schroeder, Kimberly VanderWaal

The concept of viral quasispecies refers to a constantly mutating viral population occurring within hosts, which is essential for grasping the micro-evolutionary patterns of viruses. Despite its high error rate, long-read sequencing holds potential for advancing viral quasispecies research by resolving coverage limitations in next-generation sequencing. We introduce a refined workflow, QoALa, implemented in the longreadvqs R package. This workflow begins with nucleotide position-wise noise minimization of read alignments and sample size standardization, and extends to viral quasispecies comparison across related samples with integrated visualization capabilities. Benchmarking on simulated SARS-CoV-2 and HIV-1 datasets demonstrated that QoALa consistently outperformed existing error-correction methods in recovering quasispecies composition, particularly in preserving nucleotide diversity and hierarchical population structure. Real raw read samples from five studies of different viruses (HCV, HBV, HIV-1, SARS-CoV-2, and IAV), sequenced by major long-read platforms, were also used to evaluate these approaches. The comparative results provide novel insights into intra- and inter-host diversity dynamics in various scenarios and unveil rare haplotypes not reported in the original studies, underscoring the versatility and practicality of our methodology.

MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C

2026-04-28T14:00:00Z

by Shanyue Wang, Aoyun Geng, Zhenjie Luo, Yazi Li, Junlin Xu, Yajie Meng, Leyi Wei, Quan Zou, Zilong Zhang, Tao Wang, Feifei Cui

Accurate prediction of B-cell epitopes plays a key role in facilitating advancements in vaccines, therapeutics, and diagnostics. In contrast to labor-intensive experimental approaches, computational strategies provide a more economical and efficient means of identifying potential epitopes. Existing methods are often limited by their reliance on experimentally resolved protein structures or by the use of lower-accuracy predicted structures. Sequence-based approaches, while fast, largely fail to capture the 3D spatial context essential for conformational epitopes. With the breakthroughs achieved by AlphaFold3 in predicting protein structures, we present MsgaBpred, the model to apply AlphaFold3-derived structures to B-cell epitope identification. Given only a protein sequence, our model employs a multi-scale graph convolutional network and additive attention to capture complex structural dependencies without relying on experimentally determined structures. The multi-scale design allows for effective modeling of both local and global contexts by aggregating information across different neighborhood ranges. Additionally, we leverage ESM-C, a more expressive protein language model than ESM-2, to enhance feature representation for B-cell epitope prediction. Extensive evaluations across multiple benchmark datasets demonstrate that MsgaBpred achieves competitive and robust performance; notably, it yields a statistically significant improvement in AUC compared to existing state-of-the-art methods. Moreover, the modular and scalable architecture of MsgaBpred holds promise for broader applications, including the structural analysis of other biomolecular entities such as nucleic acids and carbohydrates.

How competition can drive allochronic divergence: A case study in the Marine Midge, Clunio marinus

2026-04-27T14:00:00Z

by Alexander G. G. Jacobsen, Tobias S. Kaiser, Chaitanya S. Gokhale

Synchronizing mating to extrinsic environmental cycles can increase the chance of successful reproduction, and the resulting temporally-assorted mating may precipitate speciation if coupled with divergent selection. This process might be particularly relevant to the marine midge Clunio marinus, which synchronizes its reproduction to different lunar phases. In Roscoff (France) two sympatric populations differ in reproductive timing but are still connected by gene flow. A previous study found a relationship between the timing of reproduction and the depth at which larva live in the intertidal zone. Building on this observation, we ask if the link between reproductive timing and depth could be a mechanism for divergence when coupled with competition-induced density-dependent fitness. We devise an individual-based model replicating the reproductive behavior of C. marinus and find that sympatric divergence can occur, even when we model sexual reproduction with recombination and an explicit genetic basis. Our results suggest this mechanism is a likely hypothesis for the allochronic divergence observed in the Roscoff populations. Additionally, our study provides insights into how density-dependent fitness and competition may play a role in allochronic divergence in general.

Study of Protein-Protein Interactions in Septin Assembly: Multiple amphipathic helix domains cooperate in binding to the lipid membrane

2026-04-27T14:00:00Z

by S. Mahsa Mofidi, Abhilash Sahoo, Christopher J. Edelmaier, Stephen J. Klawa, Ronit Freeman, Amy Gladfelter, M. Gregory Forest, Ehssan Nazockdast, Sonya M. Hanson

Septins are a conserved family of cytoskeletal proteins known for sensing micron-scale membrane curvature via amphipathic helix (AH) domains. While cooperative interactions in septin assembly have been suggested, the molecular mechanisms governing membrane binding and assembly remain unclear. Building on prior findings, we use all-atom molecular dynamics simulations to examine how single and paired extended AH domains, derived from Cdc12, interact with lipid bilayers. We find that a single membrane-bound AH adopts a bent conformation upon membrane association. In solution, a second AH peptide preferentially interacts with the bound peptide through conserved salt bridges, favoring an antiparallel arrangement. Simulations of covalently linked AH tandems confirm the stability of this configuration. When two AH domains are membrane-bound, they induce localized lipid packing defects, reduce tail order, and exhibit slight peptide displacement on planar bilayers. These observations suggest a cooperative AH binding mechanism and are consistent with models in which lipid packing defects facilitate multivalent AH engagement in curved membrane environments. Our findings advance the mechanistic understanding of septin-membrane interactions and highlight the role of cooperative AH domain binding in stabilizing higher-order structures.

Clustering of SARS-CoV-2 membrane proteins in lipid bilayer membranes

2026-04-27T14:00:00Z

by Joseph McTiernan, Yuanzhong Zhang, Siyu Li, Thomas E. Kuhlman, Umar Mohideen, Michael E. Colvin, Roya Zandi, Ajay Gopinathan

The accumulation of viral structural proteins along the endoplasmic reticulum–Golgi intermediate compartment (ERGIC) membrane drives SARS-CoV-2 self-assembly and budding through interactions among proteins, RNA, and the host membrane. The membrane (M) protein, the most abundant structural component, is thought to interact with other proteins and form clusters that induce membrane curvature and initiate virion formation. However, the relative roles of direct and membrane-mediated interactions between M proteins in this clustering process remain unclear. Here, we combine all-atom molecular dynamics (MD) simulations, continuum modeling, and experiments to demonstrate that M–M interactions alone are sufficient to drive clustering in ERGIC-like lipid bilayers, even in the absence of other proteins or RNA. From MD simulations, we quantify the membrane thinning induced by M proteins and the resulting membrane-mediated interaction energy. Integrating these results into a continuum model that describes the evolution of M protein density on a planar membrane, we identify a critical effective interaction energy required for cluster formation at a given protein density. Comparison with atomic force microscopy (AFM) measurements of M protein clusters enables quantitative estimation of the direct and membrane-mediated interaction energies, revealing that direct M–M interactions dominate through an effective oligomerization energy. Together, these findings establish that M protein interactions are sufficient to drive clustering and provide a quantitative framework for understanding the interplay of direct and membrane-mediated forces in coronavirus assembly and budding.

A multi-omics framework for survival mediation analysis of high-dimensional proteogenomic data

2026-04-27T14:00:00Z

by Seungjun Ahn, Weijia Fu, Maaike van Gerwen, Lei Liu, Zhigang Li

Survival analysis plays a crucial role in understanding time-to-event (survival) outcomes such as disease progression. Despite recent advancements in causal mediation frameworks for survival analysis, existing methods are typically based on Cox regression and primarily focus on a single exposure or individual omics layers, often overlooking multi-omics interplay. This limitation hinders the full potential of integrated biological insights. In this paper, we propose SMAHP, a novel method for survival mediation analysis that simultaneously handles high-dimensional exposures and mediators, integrates multi-omics data, and offers a robust statistical framework for identifying causal pathways on survival outcomes. This is one of the first attempts to introduce the accelerated failure time (AFT) model within a multi-omics causal mediation framework for survival outcomes. Through simulations across multiple scenarios, we demonstrate that SMAHP achieves high statistical power, while effectively controlling false discovery rate (FDR), compared with two other approaches. We further apply SMAHP to the largest head-and-neck carcinoma proteogenomic data, detecting a gene mediated by a protein that influences survival time. R package is freely available on CRAN repository and published under General Public License version 3.

Diversity in emergent cell locomotion from the coupling cytosolic and cortical Marangoni flows with reaction–diffusion dynamics

2026-04-27T14:00:00Z

by Blaž Ivšić, Dorijan Vulić, Igor Weber, Piotr Nowakowski, Ana-Sunčana Smith

Cell migration is a fundamental process underlying the survival and function of both unicellular and multicellular organisms. Crawling motility in eukaryotic cells arises from cyclic protrusion and retraction driven by the cytoskeleton, whose organization is regulated by reaction–diffusion (RD) dynamics of Rho GTPases between the cytosol and the cortex. These dynamics generate spatial membrane patterning and establish front–rear polarity through the coupling of biochemical signalling and mechanical feedback. We develop a cross-scale mean-field framework that integrates RD signalling with cytosolic and cortical hydrodynamics to capture the evolution of cell shapes and emergent cellular locomotion. Our model reproduces diverse experimentally observed shape and motility phenotypes with small parameter changes, indicating that these behaviours correspond to self-organized limit cycles. Phase-space analysis reveals that coupling to both cytosolic flow and spatially varying surface tension is essential to recover the full spectrum of motility modes, providing a theoretical foundation for understanding amoeboid migration.

Refining biomarker-based clustering of cardiovascular inflammatory phenotypes in HIV using Recursive Feature Addition: A comparative evaluation approach

2026-04-27T14:00:00Z

by Rachel Mac Cann, Dana Alalwan, Gurvin Saini, Alejandro Abner Garcia Leon, Neeltje A. Kootstra, Padraig McGettrick, Aoife G Cotter, Alan Winston, Peter Reiss, Caroline Sabin, Patrick W. Mallon, on behalf of the UPBEAT-CAD, AIID and COBRA cohorts

Background

People living with HIV remain at elevated risk for a number of non-communicable diseases, including cardiovascular disease (CVD), driven in part by chronic inflammation. While prior studies have identified inflammatory biomarker patterns linked to CVD in people with HIV, it remains unclear which combinations of biomarkers most effectively predict clinical outcomes. We aimed to develop and evaluate a framework for refining biomarker-based clustering approaches to better capture inflammatory patterns associated with a cardiovascular phenotype (CVP) in people with HIV.

Methods

We developed and evaluated three recursive feature addition (RFA) models to enhance biomarker-driven clustering of people with and without HIV. Using a 24-marker initial panel of biomarkers chosen for their links to clinical CVP in people with HIV, we compared three models for selective inclusion of 31 additional, exploratory biomarkers: (1) a stepwise additive model evaluating biomarkers cumulatively based on biological relevance; (2) a stepwise additive model evaluating biomarkers individually; and (3) a greedy forward-backward selection model. Each model was assessed using principal component analysis (PCA), cluster stability, biological coherence and association with a CVP and 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk.

Results

All three RFA models generated three, biomarker-derived clusters. Post RFA cluster biomarker composition, model stability and clinical associations of these clusters differed across models. The individual additive model (Model 2) produced the most distinct separation of inflammatory profiles, incorporating 11 additional biomarkers, including, GDF-15, IFN-λ2 and Thrombopoietin). In this model, Cluster 3 was characterised by heightened innate and adaptive immune activation, the highest CVP prevalence (11%) and the strongest association with CVP (adjusted odds ratio (aOR) 2.3, 95% CI 1.04–5.09).

Conclusion

We demonstrate that an RFA framework using a stepwise, additive model evaluating biomarkers individually to enhance clustering profiles provides optimal unsupervised clustering of exploratory biomarkers to reveal additional associations between inflammatory patterns and CVP in people with and without HIV.

PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data

2026-04-27T14:00:00Z

by Komlan Atitey, Benedict Anchang

Identifying cancer driver genes (CDGs) remains a central challenge in cancer genomics, as frequency-based mutation approaches often miss rare but functionally important regulators. We present PICDGI, a computational framework that predicts driver-like regulatory genes by integrating dynamic gene-gene interaction modeling with single-cell RNA sequencing (scRNA-seq) data. Rather than relying on DNA mutation calls, PICDGI infers functional driver activity from time-resolved expression patterns and latent regulatory influence among genes during tumor progression. Methodologically, PICDGI employs a time-varying state-space model with variational Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling to estimate evolving gene interaction effects. The posterior distributions capture both the magnitude and uncertainty of each gene’s inferred regulatory influence. From these, PICDGI derives a driver coefficient that quantifies the strength and reliability of each gene’s contribution to progression-associated expression dynamics, enabling the prioritization of impactful regulators over neutral passengers. Applied to lung adenocarcinoma (LUAD) scRNA-seq data, PICDGI recovered known oncogenes and tumor suppressors and nominated novel candidate drivers, including JPH1 and CHEK1, which are implicated in calcium signaling, mitochondrial regulation, and DNA repair. These genes exhibit trajectory-aligned activity consistent with tumor evolution and immune-modulatory processes. Comparative analysis using Moran’s I statistics in Monocle 3 showed that PICDGI-prioritized genes display stronger progression-associated dynamics than genes selected by spatial autocorrelation alone. We further validated PICDGI on an independent pediatric acute myeloid leukemia (AML) scRNA-seq cohort, where it consistently recovered known drivers and relapse-associated regulatory programs under fixed model parameters. By integrating interaction-informed dynamic modeling with single-cell resolution data, PICDGI provides a generalizable and biologically grounded framework for identifying rare and context-specific regulatory drivers of cancer progression, with broad applicability across tumor types.

Leveraging mathematical models to predict and control T-cell activation

2026-04-27T14:00:00Z

by Xabier Rey Barreiro, Jose Faro, Alejandro F. Villaverde

T-cell receptor (TCR)-mediated T-cell activation is a key process in adaptive immune responses. The complexity of this process has led to the development of different mathematical models that seek to describe and predict the conditions of antigen-TCR interactions required for TCR triggering and T-cell activation. These models are characterized by describing different sets of sequential molecular interactions and their kinetics, positing the generation of a final product as a necessary and sufficient condition for T-cell activation. Such modeling could provide an effective tool for simulating antigen recognition by T cells and, consequently, aid in the design of effective therapeutic strategies. However, it is necessary to previously assess the predictive capabilities of the proposed models when fitted to experimental data. As a first step towards this goal, in this work we examine the parameter identifiability and sensitivity of the published models of TCR-based T-cell activation. For each model, we consider different, often experimentally measured, output quantities and show how their availability affects the results. These analyses allow us to determine the ability of each model to correctly describe different experimental situations, and to establish to what extent these models can be applied to reliably predict and control T-cell activation by specific therapeutic targets.

STARCall integrates image stitching, alignment, and read calling to enable scalable analysis of in situ sequencing data

2026-04-27T14:00:00Z

by Nicholas J. Bradley, Sriram Pendyala, Katie Partington, Douglas M. Fowler

Fluorescent in situ sequencing involves imaging-based sequencing by synthesis in intact cells or tissues to reveal target nucleotide sequences inside each cell. Often, the target sequences are barcodes that indicate a perturbation (e.g., CRISPR guide or genetic variant) delivered to the cell. However, processing in situ sequencing data presents a considerable challenge, requiring stitching and aligning tens of thousands of images with millions of cells, detecting small amplicon colonies across sequencing cycles, and calling reads. To address these challenges, we introduce STARCall: STitching, Alignment and Read Calling for in situ sequencing, a software package that analyzes raw in situ sequencing images to produce a genotype-to-phenotype mapping for each cell. STARCall improves upon previous solutions by combining stitching and alignment of images into a single step that minimizes both inter-cycle and intra-cycle alignment error. STARCall also improves detection and extraction of sequencing reads, incorporating filters and normalization to combat background fluorophore signal. We compare STARCall to other methods using a diverse set of images that include commonly encountered imaging problems such as variable intensity across channels and cycles and high levels of background. Specifically, this comprises ~250,000 images from a pooled screen of ~3,500 barcoded LMNA variants expressed in U2OS cells and ~1,200 barcoded PTEN variants in induced pluripotent stem cells (iPSC) and iPSC-derived neurons. Overall, STARCall aligned more than 50% of tiles with <1 pixel residual misalignment on all nine image sets, outperforming alternative packages by 14–35%. STARCall also yielded an 8–40% increase in genotyped cells due to improved filtering and normalization methods that address background fluorescence. STARCall can call tools like CellPose to segment cells and CellProfiler to compute cell features from the phenotyping images. STARcall is open-source and freely available, providing a robust solution for the analysis of in situ sequencing data.

Systems biology analysis of vasodynamics in mouse cerebral arterioles during resting state and functional hyperemia

2026-04-27T14:00:00Z

by Hadi Esfandi, Mahshad Javidan, Eric R. McGregor, Rozalyn M. Anderson, Ramin Pashaie

Cerebral hemodynamics is tightly regulated by arteriolar vasodynamics. In this study, a systems biology approach was employed to investigate how the interplay between passive, myogenic, neurogenic, and astrocytic responses shapes arteriolar vasodynamics in small rodents. A model of neurovascular coupling is proposed in which neurons inhibit and dampen the myogenic response to promote vasodilation during activation, and facilitate the myogenic response to promote rapid vasoconstriction immediately post-activation. In this model, inhibition of the myogenic response is mediated by the hyperpolarization of smooth muscle and endothelial cells. Dampening and facilitation of the response are mediated by neuronal production of nitric oxide and release of neuropeptide Y, respectively. We also introduce a model for gliovascular coupling, in which astrocytes periodically inhibit the myogenic response upon detecting an increase in myogenic activity through interactions between their endfeet and arterioles. Our simulations suggest that in the resting state, delays in myogenic autoregulation can intrinsically generate low-frequency (∼0.1 Hz) oscillations in vessel diameter (vasomotion), in the absence of extrinsic neurogenic or systemic rhythmic inputs. In the active state, these oscillations are disrupted by the neurogenic and astrocytic responses. The biophysical model of arteriolar vasodynamics presented in this study lays the foundation for quantitative analysis of cerebral hemodynamics for cerebrovascular health diagnostics and hemodynamic neuroimaging.

One model to rule them all: Unification of voltage-gated potassium channel models via deep non-linear mixed effects modelling

2026-04-27T14:00:00Z

by Domas Linkevicius, Angus Chadwick, Melanie I. Stefan, David C. Sterratt

Ion channels are essential for signal processing and propagation in neural cells. Voltage-gated ion channels permeable to potassium (K_v) form one of the most prominent channel families. Techniques used to model the voltage-dependent gating of K_v channels date back to Hodgkin and Huxley (1952). Different K_v types can display radically different kinetic properties, requiring different mathematical models. However, the construction of Hodgkin-Huxley-like (HH-like) models is generally complex and time consuming due to the number of parameters, their tuning and having to choose functional forms to model gating. In addition to the between-K_v type heterogeneity, there can be significant within-K_v type kinetic heterogeneity between different cells with genetically identical channels. Since HH-like models do not account for such variability, extensions to it are necessary. We use scientific machine learning (SciML), the integration of machine learning methodologies with existing scientific models, and non-linear mixed effects (NLME) modelling to bypass the limitations of HH-like modelling. NLME is a modelling methodology that takes into account both within- and between-subject variability. These tools allowed us to complement the HH-like modelling and construct a unified SciML HH-like model that fits the recordings from 20 different K_v types. The unified SciML HH-like model produced closer fits to the data compared to a set of seven previous HH-like models and was able to represent the highly heterogeneous data from different cells. Our model may be the first step in producing a SciML foundation model for ion channels that would be capable of modelling the gating kinetics of any ion channel type.

Selective observation following betrayal shapes the social inference landscape

2026-04-24T14:00:00Z

by Sangkyu Son, Seng Bum Michael Yoo

Despite limited access to others’ actions and outcomes, humans excel at inferring hidden intentions. Given only partial access, how do they decide what to observe, and how does selective observation shape inference? Here, we examined how choosing what to observe can bias the inference about others’ intentions. Participants played a game where they pursued a fleeing target while a computerized opponent acted competitively or cooperatively. Participants overestimated the opponent’s competitiveness after the opponent acted more competitively than expected, whereas no such bias occurred when the opponent was more cooperative than expected. This asymmetry depended on the sequence of events, resembling hysteresis, a form of path dependence observed in physical systems. We found that these biases became stronger when participants chose to observe the opponent instead of their own avatar, and this choice came at the cost of losing precise control over their avatar. Our findings highlight the trade-off in selecting what to observe, as the resulting inference biases propagate differently depending on the interaction history.

Nine quick tips for software containerization

2026-04-24T14:00:00Z

by David Moreau, Kristina Wiebels

Software containerization has become a cornerstone of modern computational biology, enabling researchers to package code, dependencies, and execution environments in portable and reusable units. Containers support reproducibility, facilitate collaboration, and lower barriers to deploying complex computational workflows across heterogeneous systems. At the same time, inappropriate or superficial use of containers can undermine these benefits, leading to brittle environments, security risks, or false confidence in reproducibility. In this article, we present nine practical and actionable tips for using software containers effectively in computational biology research. Rather than focusing narrowly on container syntax or tooling, we address conceptual decisions that arise throughout the research lifecycle: when containerization is appropriate, how to balance reproducibility with flexibility, how to manage dependencies and data, and how to share containers responsibly. These tips are intended for researchers with varying levels of experience, from those adopting containers for the first time to those maintaining mature, containerized workflows.

Automating population construction and parallel simulation of biophysical models for neuromuscular cells: An inverse approach

2026-04-24T14:00:00Z

by Hojeong Kim

Biophysical modeling and simulation help to promote a comprehensive understanding of the neuromuscular mechanisms underlying muscle force generation and control in normal and pathological states. However, this process is labor intensive and limited to special conditions due to the heterogeneity of neuromuscular cells and the variability in their organization across body parts and ages. We present a methodology to resolve this issue. First, we formulate a building-block approach with an inverse modeling framework for automated population construction and tractable hierarchical analysis under various physiological conditions. Second, we devise a network folder-based approach with a virtual environment technique for efficient parallel simulation that can operate on a multicore computer, a supercomputing system, or a computer network through the internet. Third, we implement the methodology by developing open-source command-line software called pNMS. Finally, we demonstrate that pNMS can replicate experimental and simulation results from different environments and predict the population behaviors of neuromuscular cells depending on their organization and muscle length. With an intuitive, flexible application programming interface, this software tool may offer a solution for promoting efficient investigation and an in-depth understanding of neuromuscular function at cellular resolution under realistic scenarios.

Ten simple rules for mentoring and being mentored while neurodiverse

2026-04-24T14:00:00Z

by Adam B. Smith, Emily G. Adams, Ethan Abercrombie, Noor Bibi, Claire L. J. Bottini, Alissa J. Brown, Cybil N. Cavalieri, Alonwyn L. Clauser, Marlyse C. Duguid, Kasey D. Fowler-Finn, Jenna Hutchen, Victor Leite Jardim, Clarissa S. Rodriguez, Beck M. Swab, Steph Varghese