PLOS Computational Biology: New Articles

Cross-bridge model for predicting muscle short-range stiffness during movement

2026-09-01T14:00:00Z

by Tim J. van der Zee, Surabhi N. Simha, Gregory N. Milburn, Kenneth S. Campbell, Lena H. Ting, Friedl De Groote

Musculoskeletal simulations can offer valuable insight into how the properties of our musculoskeletal system influence the biomechanics of our daily movements. One such property is muscle’s initial resistance to stretch, also known as short-range stiffness, which is key to stabilizing movements in response to external perturbations. Short-range stiffness is poorly captured by existing musculoskeletal simulations since they employ phenomenological Hill-type models lacking activation-dependent stiffness properties. Existing simulations also do not capture the history-dependent reduction in short-range stiffness after muscle shortening, known as muscle thixotropy. While cross-bridge models can reproduce muscle short-range stiffness, it remains unclear which model properties are necessary to capture its history dependence. Here, we tested the ability of various cross-bridge models to reproduce empirical short-range stiffness and its history-dependent changes across a broad range of behaviorally relevant length changes and activation levels, using an existing dataset on 11 permeabilized rat soleus muscle fibers. We quantified muscle thixotropy using the ratio between the observed short-range stiffnesses after and before shortening. We computed the root-mean-square deviation (σSRS) between the predicted short-range stiffness ratio of various muscle models and the measured stiffness ratio. We found that cross-bridge models captured short-range stiffness changes across conditions with both small and large history-dependent stiffness reductions (σSRS ≤ 0.1), but only when including cooperative activation of both thin and thick myofilaments. In contrast, Hill-type models and a cross-bridge model without cooperative myofilament activation underestimated short-range stiffness and did not capture its change across conditions with large history-dependent stiffness reductions (σSRS > 0.2). Similar results were obtained when using a Gaussian-approximated solution method to simulate the cross-bridge distribution, but at an approximately eightfold lower computational cost. We therefore propose to implement Gaussian-approximated cross-bridge models with cooperative myofilament activation into musculoskeletal simulations to improve the prediction of short-range stiffness during movements.

From sequences to strategies: Early detection of new SARS-CoV-2 variants via genetic distance to reduce hospitalizations

2026-09-01T14:00:00Z

by Marika D’Avanzo, Aung Pone Myint, Giacomo Cacciapaglia, Stefan Hohenegger, Francesco Conventi, Marta Nunes

The COVID-19 pandemic highlighted the critical need for robust methods to monitor viral evolution and detect emerging variants of concern (VOCs). This study expanded an unsupervised clustering algorithm, based on Levenshtein distance, to track and predict variant predominance across six European countries from 2020 to January 2024. We also investigated the influence of genetic distances and containment strategies on hospitalization rates. Spike protein sequences were transformed into temporal chains. A deep neural network (DNN) was trained to classify emerging chains as likely dominant, while a CatBoost model assessed important variables, and simulations explored modifying vaccine genetic distance, containment measures, and vaccination coverage. Approximately 5,000 sequences per week enabled early chain detection within four weeks. The DNN achieved high classification performance for identifying future predominant chains within 3–4 weeks of detection. Genetic distance metrics between consecutive chains and between circulating and vaccine strains were among the most informative variables associated with hospitalization patterns. Model-based simulations suggested that scenarios involving improved vaccine matching or stronger containment measures were associated with lower predicted hospitalization burdens. Doubling vaccination coverage alone had minimal effect but showed additional reductions when combined with strict containment. Our findings from this integrated framework highlight the potential relevance of genetic distance metrics and public health interventions when assessing hospitalization risk associated with emerging variants.

malariasimple: An R package for fast simulations of malaria transmission

2026-09-01T14:00:00Z

by Debbie Shackleton, Neil Ferguson, Lucy Okell, Tom Churcher, Pete Winskill

Process-based malaria transmission models are important tools for evaluating intervention strategies, quantifying uncertainty, and informing malaria control policy. Individual-based models such as malariasimulation are computationally demanding, which limits their practicality for applications that require large numbers of simulation runs. In this paper we present malariasimple, a simplified, compartmental model implemented as an R package which approximates the epidemiological structure and parameter definitions of malariasimulation while operating at a fraction of the computational cost. Across a range of transmission intensities and intervention scenarios, malariasimple closely reproduces key outputs of malariasimulation while reducing runtimes by up to 99.6%. Its computational efficiency enables full Bayesian parameter inference, allowing estimation of complete posterior distributions. malariasimple provides a fast, flexible, and mechanistically consistent addition to the Imperial College London Malaria Model framework, bridging the gap between computational efficiency and epidemiological realism. The malariasimple R package is freely available for download at https://github.com/mrc-ide/malariasimple.

Mapping spatial colleague connectivity patterns from individual-level registry data to inform regional pandemic interventions

2026-08-31T14:00:00Z

by PingPing Song, Sake J. de Vlas, Tom Emery, Luc E. Coffeng

A concern in infectious disease modelling is how accurately population mixing is incorporated, as it shapes the type and frequency of contacts through which infection spreads, and consequently, estimated intervention effectiveness. Although synthesizing mixing patterns from diary-based surveys is an established framework, geographical information is poorly or sparsely captured. Here we propose a generalizable workflow to quantify geographical connectivity from job registry data covering over 8 million Dutch working population. The derived colleague connectedness shows heterogeneous spatial patterns, quantified from the number of connections per municipality triplet, two residential municipalities and one shared workplace municipality. We illustrate the epidemiological relevance of this spatial connectivity by using SARS-CoV-2 Omicron as an example: a two-fold increase in within-province connections was associated with a 3.7-day earlier (95% CI: 0.6 to 6.6 days) Omicron onset, and between-province connectivity was associated with a 2.5 days earlier (95% CI: -1.0 to 6.2 days) onset. Based on our estimates of spatial connectivity, we quantified the number of colleague connections that would be removed in case of regional mobility restrictions such as a lockdown: locking down the whole province Zeeland would remove 2.6% of colleague links at the national level while the city Amsterdam alone would remove 10.0%. In future modelling studies, these highly fine-grained spatial connectivity data could be used as spatial mixing matrices to more explicitly capture the connectedness and dependency between regions to inform more tailored policy measures.

Population morphology implies a common developmental blueprint for Drosophila motion detectors

2026-08-31T14:00:00Z

by Nikolas Drummond, Arthur Zhao, Alexander Borst

T4 and T5 neurons are the first direction-selective neurons in the visual pathway. They are the most numerous cell types in the fly brain (~6000 within each optic lobe) and, as a population, their compact dendritic arbours span the entire visual field. They are classified into four subtypes (a, b, c, and d). Each subtype encodes one of four orthogonal motion directions (up, down, forwards, backwards). Crucially, the dendrites of these neurons are oriented inversely to the functional direction of motion which they encode. This dendritic orientation is what ultimately determines their functional directional encoding. The development of these neurons is well characterised up to the point of neuropil innervation. However, the full population of these neurons innervate their target neuropil prior to the emergence of directionality within their dendrites. As it stands, development prior to the emergence of dendritic orientations, and the adult oriented dendrite are both well understood, but the key components relating to the emergence of orientation itself are missing. Recent whole-brain electron microscopy (EM) connectomes of Drosophila melanogaster provide an unprecedented level of resolution and completeness when considering the morphology of neurons. Utilising this, we isolate the dendritic arbour of every T4 and T5 neuron within a female adult Drosophila brain, made available through FAFB-FlyWire. In doing so we are able to rigorously quantify the morphology of these dendrites in order to understand their similarities and differences. In doing so we aim to shed light on the origins of dendritic directionality. We reason that either this emerges through a tightly controlled, subtype specific mechanism, or is the result of a subtype agnostic mechanism and external factors. In the former case, we would expect evidence of this in differences between the morphological structure of individual dendrites between T4 and T5, and their subtypes. Our analysis however reveals a high degree of structural similarity between T4 and T5, and within their subtypes. Particularly, the geometry of branching, section orientation, and tree-graph structure of these dendrites show only minor variability, with no consistent separation between T4 and T5, or their subtypes. These results indicate that, despite forming in different neuropils, and serving distinct motion directions, T4 and T5 dendrites follow closely aligned morphological patterns. This suggests a shared mechanism of directed outgrowth, as opposed to symmetry breaking emerging through neuron type or subtype specific mechanisms.

Prospects of HIV elimination among men who have sex with men: A systematic review of modeling studies

2026-08-31T14:00:00Z

by Jacob Aiden Roberts, Alexandra Teslya, Mirjam E. Kretzschmar, Janneke H.H.M. van de Wijgert, Ganna Rozhnova

Men who have sex with men (MSM) remain disproportionately affected by HIV worldwide. This systematic review summarizes the results of mathematical modeling studies that evaluated prospects of HIV elimination among MSM by geographical setting, type of intervention(s), elimination definition, and model characteristics. We searched Embase and PubMed for studies published between July 1, 2016 and September 1, 2025 which used a dynamic mathematical model to assess the impact of interventions on HIV transmission among MSM. Data were extracted on study population, interventions, elimination definitions, model type, model structure, and calibration. Studies were critically appraised for model comprehensiveness in addressing elimination. 135 of the 4,595 records were included. MSM populations in six of the eight Joint United Nations Programme on HIV/AIDS regions were modeled, with 47% of models considering MSM in the USA. Agent-based models (ABMs) were as common as compartmental models overall, with ABMs more frequently used in Western and Central Europe and North America (WCENA), while compartmental models predominated elsewhere. Of the 135 included studies, 41 defined elimination, and they defined it as follows: (i) reduction in HIV incidence/prevalence, or (ii) threshold of HIV incidence/prevalence, or (iii) reproduction number below one. Elimination was achieved in 42 out of 51 modeled scenarios, of which 32 (82.05%) were in WCENA, but the authors of only 28 of these 42 scenarios discussed the real-world elimination feasibility with 10 of these scenarios considered elimination feasible by the original authors. There was also a strong regional divide in the elimination scenarios considered feasible, with 6 (60.00%) in Asia and the Pacific (AP) and 4 (40.00%) in WCENA. Models in which elimination was achieved commonly used combinations of interventions. Modeling efforts to understand HIV elimination prospects outside WCENA should be intensified, and models assessing HIV elimination prospects should account for HIV acquisition outside of the local context. To enhance study comparability and ensure that models contribute effectively to public health policy, an elimination definition based on an HIV incidence threshold would be the most valuable. By identifying gaps in current studies, we recommend novel research directions for modeling to inform a coordinated global response for HIV elimination among MSM.

Model Context Protocol: The unexpected catalyst of a bioinformatics interoperability revolution

2026-08-31T14:00:00Z

by Nathan C. Sheffield

Economic factors promoting vaccine nationalism in the face of viral evolution

2026-08-31T14:00:00Z

by Ari S. Freedman, Bjarke Frost Nielsen, Chadi M. Saad-Roy, Bryan T. Grenfell, C. Jessica E. Metcalf, Simon A. Levin

The increasing interconnectedness of the modern world calls for globally equitable solutions to combat pandemic challenges. However, we have seen a tendency in recent decades for high-income countries to resort to “vaccine nationalism,” hoarding vaccine production to the detriment of lower-income countries. In addition, vaccine nationalism can prove detrimental to hoarding countries in the long term, as inequitable global vaccine distribution during the COVID-19 risked exacerbating the rise of harmful immune-escape variants that largely counteracted the original benefits of vaccine hoarding. Thus, vaccine hoarding may create a problem of time preference for a vaccine-producing country, where countries heavily discounting the future would opt for vaccine hoarding while countries lightly discounting the future would opt for vaccine sharing. Using a novel modeling framework integrating epidemiological, evolutionary, and economic processes, we demonstrate how high temporal discounting, low levels of outgroup prosociality, and high vaccine-distribution costs for low-income countries can promote vaccine-hoarding tendencies. We further show how these factors interact with epidemiological and evolutionary parameters to incentivize vaccine sharing in different ways: in some parameter regimes, vaccine sharing helps by reducing variant infections, while in others, vaccine sharing helps by reducing the probability of initial variant emergence. As a result, the optimal fraction of vaccines a country should share in our model is a bimodal function of the pathogen’s transmissibility. We thus provide a nuanced, model-based exploration of how various factors may contribute to vaccine nationalism’s emergence, emphasizing the need for international organizations to coordinate global vaccination responses to future pandemics.

ASPIRE: Accurate alternative splicing prediction from limited RNA sequencing data and a minimal gene set

2026-08-28T14:00:00Z

by Ran Eisenberg, Efraim Rahamim, Eli Kopel, Miri Danan-Gotthold, Erez Y. Levanon, Ofir Lindenbaum

Alternative splicing is a fundamental biological mechanism that increases protein diversity and regulates critical cellular processes across eukaryotes. Dysregulation of splicing is implicated in a wide range of diseases, including cancer, neurological disorders, and autoimmune conditions. Accurate prediction of splicing metrics such as percent spliced in (PSI) is therefore essential for understanding splicing regulation and improving disease characterization. However, existing approaches typically require high sequencing depth and are thus poorly suited for low-coverage settings such as single-cell RNA sequencing, where sparse read counts limit reliable splicing analysis. Here, we present ASPIRE (Accurate Splicing Prediction from Limited RNA Sequencing), a deep learning framework for predicting alternative splicing metrics from low-depth RNA-seq gene expression data. ASPIRE infers PSI values from gene expression profiles with limited read coverage and incorporates an embedded feature selection mechanism that identifies a minimal, informative subset of genes relevant to splicing regulation. This design enables accurate prediction while reducing reliance on extensive sequencing and mitigating noise introduced by irrelevant or weakly informative genes. By focusing on biologically meaningful features, including RNA-binding proteins, ASPIRE maintains strong predictive performance even under conditions typical of single-cell transcriptomics. We demonstrate that ASPIRE accurately predicts PSI values across a range of sequencing depths, including those characteristic of single-cell RNA-seq, and performs comparably to or better than existing methods in both simulated and real datasets. By enabling robust expression-based splicing inference from sparse data, ASPIRE facilitates the study of alternative splicing at cellular resolution and provides a practical framework for investigating splicing regulation in development, disease, and heterogeneous cell populations.

GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors

2026-08-28T14:00:00Z

by Anchi Sun, Yongjing Hao, Yijie Ding, Jing Chen, Hongjie Wu

G protein-coupled receptors (GPCRs) are central membrane receptors and major therapeutic targets. However, predicting GPCR function remains difficult because experimentally annotated receptors are scarce, functional labels follow a long-tailed distribution, and structural information remains underused. Here, we present GPCR-GO, a relation-aware heterogeneous graph attention framework that integrates structural similarity with protein-protein interactions (PPIs). GPCR-GO uses the Dictionary of Protein Secondary Structure (DSSP) to transform three-dimensional protein structures into residue-level structural descriptors, aggregates these descriptors into protein-level structural vectors, and uses the resulting vectors to define structural-similarity edges. The framework builds a heterogeneous graph linking proteins and Gene Ontology (GO) terms through PPI edges, structural-similarity edges, GO hierarchy edges, and reviewed protein–GO annotations. Relation-aware graph attention aggregates complementary biological signals, whereas graph decomposition, hard negative mining, and semi-supervised learning improve learning under sparse supervision and class imbalance. On the held-out GPCR test split, GPCR-GO outperforms existing methods and achieves F-score (Fmax) values of 0.514, 0.767, and 0.631 on biological process (BP), cellular component (CC), and molecular function (MF), respectively. These results show that structure-derived relations complement curated annotation and support accurate GPCR function prediction under limited supervision.

Disentangling the drivers of heterogeneity in SARS-CoV-2 transmission from data on viral load and daily contact rates

2026-08-28T14:00:00Z

by Billy J. Quilty, Lloyd A. C. Chapman, James D. Munday, Kerry L. M. Wong, Amy Gimma, Suzanne Pickering, Stuart Neil, Rui Pedro Galao, W. John Edmunds, Christopher I. Jarvis, Adam J. Kucharski

SARS-CoV-2 transmission is highly overdispersed, with a minority of individuals responsible for the majority of transmission, though the drivers of this heterogeneity are unclear. Here, we assess the contribution of variation in viral load and daily contact rates to this heterogeneity by combining published viral load estimates and contact survey data in a mathematical model to estimate the secondary infection distribution. Using data from the BBC Pandemic and CoMix contact surveys, we estimate the secondary infection distribution throughout the pandemic in the UK in 2020, and the effectiveness of frequent and pre-event rapid testing for reducing superspreading events. We find that individual heterogeneity in contacts rather than individual heterogeneity in shedding is the main driver of observed heterogeneity in the secondary infection distribution. Our results suggest that everyone testing every 3 days would reduce the reproduction number below 1 and be equivalent in terms of impact on secondary infections to everyone testing only before events with a minimum event size of 10 for pre-pandemic contact levels. This work demonstrates the potential for using viral load and contact data to estimate heterogeneity in transmission and the effectiveness of rapid testing strategies for curbing transmission in future pandemics.

Simple birth-death-mutation models predict some—but not all—aspects of the experimental evolution of antibiotic resistance

2026-08-28T14:00:00Z

by Elin Lilja, Rosalind J. Allen, Bartlomiej Waclaw

Mathematical modelling of antibiotic resistance plays an important role in understanding the mechanisms of resistance emergence and spreading, testing the feasibility of new treatment protocols, and antimicrobial stewardship. However, many assumptions underlying some of the most commonly used mathematical models have not been rigorously tested experimentally. We verify whether one of these models - a birth-death-mutation process - is able to quantitatively predict the outcome of laboratory experiments. We grow bacteria in a bioreactor in conditions that closely resemble the assumptions of the model, and compare the model predictions with experimental observables such as the probability and time to resistance evolution, mutant number distribution, and the genetic composition of the evolved populations. We show that the model fails to reproduce some aspects of the experiments (failing differently for different antibiotics) but that simple modifications of the model significantly improve its predictive power. These modifications give insight into the population dynamics of resistant mutants for each antibiotic tested, and highlight the importance of quantitative modelling for accurate prediction of antibiotic resistance evolution.

Metal binding site alignment enables network-driven discovery of recurrent geometries across sequence-divergent proteins and drug off-targets

2026-08-28T14:00:00Z

by Vetle Simensen, Eivind Almaas

Metal-binding sites (MBSs) are critical determinants of protein stability and biological function, yet methods for comparing their local binding environments lag behind those for whole-structure alignment. Here, we represent MBSs as atomic point clouds surrounding bound metal ligands and align them with a fine-tuned iterative closest point algorithm. Applying this framework to a redundancy-reduced collection of MBSs derived from all metalloproteins in the Protein Data Bank (PDB), we perform pairwise alignments across 23,342 sites to construct a similarity network of metal-binding environments. The resulting network topology recapitulates metal coordination chemistry and enzyme function: links are strongly enriched within metal types and across shared EC subclasses. Conserved metalloenzyme families form cohesive subnetworks; for example, the binuclear ureohydrolase domain appears as two tightly connected components that also capture atypical members such as the dinickel metformin hydrolase. We observe only a moderate global association between protein sequence and MBS geometry, yet many network links connect near-identical binding-site architectures across proteins with low sequence identity, consistent with either divergent evolution with local MBS conservation or candidate cases of molecular convergent evolution. Integrating network proximity with structural evidence of drug binding identifies drugs with enriched connectivity among their targets and predicts 528 drug–off-target combinations across 88 drugs and 151 human proteins, recovering both known off-targets (e.g., ADAM/ADAMTS for matrix metalloproteinase inhibitors) and proposing novel ones. The MBS network thus provides a scalable resource for probing metalloprotein evolution, functional convergence, and the structural basis of drug cross-reactivity.

iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data

2026-08-27T14:00:00Z

by Hongjiang Guo, Tingfang Wu, Wenzheng Wang, Yelu Jiang, Geng Li, Liangpeng Nie, Yunhua Jia, Lijun Quan, Moli Huang, Qiang Lyu

Precise resolution of cellular heterogeneity within complex tissues is fundamental to deciphering disease etiologies from bulk transcriptomic profiles. While computational deconvolution offers a scalable alternative, current deep learning methods predominantly operate as “black boxes,” neglecting the structural constraints of biological laws. This reliance on purely data-driven feature extraction often yields biologically incoherent predictions and limited mechanistic interpretability. iDCF (Interpretable Deconvolution of Cell Fractions) is a novel framework that enforces biological topology onto deep neural networks. The iDCF architecture employs a dual-stream design, synergizing a standard deep network with a knowledge-based sparse neural network (KSNN) explicitly masked by pathway definitions and protein-protein interaction (PPI) networks. In comprehensive benchmarks, iDCF achieves top-tier performance, consistently ranking among state-of-the-art methods in accuracy and robustness. iDCF integrates the SHapley Additive exPlanations (SHAP) framework, bridging the gap between computational inference and biological intuition. The model’s decision logic is governed by established biological mechanisms rather than spurious statistical correlations, validating its reliability. Validations across clinical contexts, including Alzheimer’s disease, ovarian cancer, and diabetes, demonstrate iDCF’s ability to recover disease-relevant cellular dynamics. iDCF offers a high-performance, interpretable, and biologically grounded tool for deconvolving cell-type proportions, facilitating deeper insights into tissue heterogeneity in health and disease.

Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales

2026-08-27T14:00:00Z

by Sara Varetti, Sebastian Goldt, Eugenio Piasini

In vision neuroscience, the temporal dynamics of the sensory stream and of its neural representations are thought to be deeply linked to the function of the hierarchy of cortical areas that deal with object recognition, known as the visual ventral stream. Neural representations that are invariant under identity-preserving object transformations, and therefore allow for efficient learning of object identity, are theorized to emerge from a self-supervised learning process that attempts to extract “temporally stable” features from the sensory input. Conversely, invariance increases along the hierarchy, putatively implying progressively slower neural codes in higher-level areas. Recent neurophysiological evidence shows that indeed, as one moves along this cortical hierarchy, neural representations of dynamic stimuli become slower, and additionally the temporal scales of the within-trial fluctuations of these representations (called “intrinsic timescales”) increase starkly. However, while these timescale hierarchies have been reproduced in biologically grounded recurrent models, their network determinants have remained largely unexplored in image-computable models of the ventral stream. Here we investigate the temporal structure of the neural codes in a noisy, recurrent and adaptive model of the ventral visual stream. We show that, surprisingly, the organization of the representation timescales is set by the broad architectural features of the network, regardless of training, while the ordering of the intrinsic timescales across layers is sensitive to the details of the functions implemented by each layer. Our work underscores the importance of the temporal structure of the neural code as a probe for the link between structure and function in models of the vertebrate visual system.

Leveraging synthetic and genetic data to improve epidemic forecasting

2026-08-27T14:00:00Z

by Dave Osthus, Alexander C. Murph, Emma E. Goldberg, Lauren J. Beesley, William M. Fischer, Nidhi Parikh, Lauren A. Castro

Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improve emerging infectious disease forecasting when little pathogen-specific training data are available. Specifically, we explore two sources of information that may be available near the start of an emerging disease outbreak: synthetic data and genetic information. For this investigation, we conducted an experiment where we trained deep learning models on different combinations of real and synthetic data, both with and without genetic information, to explore how these models compare when forecasting COVID-19 cases for US states. All models are developed with an eye towards forecasting the next pandemic. We find that models trained with synthetic data have better forecast accuracy than models trained on real data alone, and models that use genetic variants have better forecast accuracy compared to those that do not. All models outperformed a baseline persistence model, a benchmark that proved challenging for many real-time COVID-19 case forecasting models, and multiple models outperformed the COVIDHub-4_week_ensemble. This paper demonstrates the value of these underutilized sources of information and provides a blueprint for forecasting future pandemics.

Systematic multivariate analysis of chromatin complex dependencies reveals Set1C/COMPASS as a melanoma-enriched epigenetic vulnerability

2026-08-27T14:00:00Z

by Luisa Quesada Camacho, Mohammad Fallahi-Sichani

Epigenetic dysregulation is a common feature of cancer. It creates vulnerabilities arising from an increased reliance on chromatin-based mechanisms that sustain malignant transcriptional states. While many chromatin regulators are broadly required for cellular viability, others function in a context-dependent manner across distinct oncogenic settings, tissue lineages, and differentiation states. Moreover, chromatin regulators often operate within multi-subunit complexes; thus, epigenetic vulnerabilities emerge from coordinated complex activities. Here, we integrate large-scale genetic dependency maps from human cancer cell lines with curated epigenetic complex annotations to perform a systematic, multivariate analysis of complex-level epigenetic dependencies across cancer lineages. Our analysis reveals that dependencies frequently cluster among functionally related chromatin complexes and that biologically related cancer types (e.g., hematologic malignancies) share similar dependency patterns, consistent with shared underlying epigenetic requirements. Focusing on melanoma, we identify multiple enriched epigenetic complex dependencies, including complexes previously associated with recurrent genetic alterations or melanocyte lineage regulation, as well as a previously unrecognized vulnerability involving the H3K4 methyltransferase complex Set1C/COMPASS. This dependency is not restricted to a specific melanoma differentiation state, but genetic depletion of CXXC1 (a critical complex-specific subunit) suggests that CXXC1-dependent melanoma cells require Set1C/COMPASS activity to maintain global H3K4 trimethylation (H3K4me3) and proliferation. Integrative modeling links Set1C/COMPASS dependency to MYC- and E2F-driven transcriptional programs, which are suppressed upon complex inhibition. Together, this work combines integrative, multivariate analysis of lineage-enriched epigenetic dependencies with genetic perturbation, transcriptional profiling, and single-cell analysis to uncover an enriched epigenetic dependency on Set1C/COMPASS in melanoma cells.

Multiscale modeling of T cell exhaustion: A mathematical framework integrating continuous dynamics with spatial heterogeneity

2026-08-26T14:00:00Z

by Chenghang Li, Yuhong Zhang, Xue Liu, Yipu Qu, Xiulan Lai, Jinzhi Lei

Continuous antigen exposure drives T cells into a progressive state of dysfunction known as exhaustion, enabling tumors to evade immune surveillance and promoting disease progression. Despite its importance, predictive modeling of T cell exhaustion remains a major challenge due to the complexity of its regulatory dynamics. To address this challenge, we developed a mathematical framework that characterizes the dynamic regulation of T cell exhaustion and its impact on tumor-immune interactions. Here, we integrate multi-source data, population dynamics modeling, and agent-based modeling to track the progressive stages of CD8+ T cell exhaustion. Our model demonstrates that immune checkpoint blockade significantly delays exhaustion and promotes the expansion of tumor-reactive T cells compared to untreated conditions. From a pseudo-potential energy perspective, we show that the core mechanism of immunotherapy lies in expanding the tumor-reactive T cell pool, which consequently reduces the overall state of exhaustion within the system. We find that T cell activation and exhaustion signals jointly govern tumor-immune dynamics. Enhancing activation alone without restricting exhaustion can inadvertently accelerate the loss of T cell function. In contrast, combining enhanced activation (via anti-CTLA-4) with suppressed exhaustion (via anti-PD-1) is essential for achieving a sustained antitumor response. Furthermore, spatial simulations confirm that a high-activation and low-exhaustion state effectively restricts tumor spread, maintaining substantially lower tumor densities compared to low-activation, high-exhaustion scenarios. Our framework provides quantitative insights into T cell exhaustion and a theoretical foundation for optimizing combination immunotherapies.

Simulation and inference methods for non-Markovian stochastic reaction networks

2026-08-26T14:00:00Z

by Thomas P. Steele, David J. Warne

Stochastic models of reaction networks are widely used to capture intrinsic noise in complex systems in the life sciences. Typical formulations of these models are based on Markov processes for which there is extensive research on efficient simulation and inference. However, there are complex processes in biology, such as gene transcription and translation, that introduce history dependent dynamics requiring non-Markovian processes to accurately capture the stochastic dynamics of the system. This greater realism comes with additional computational challenges for simulation and parameter inference. We develop efficient stochastic simulation algorithms for well-mixed non-Markovian stochastic reaction networks with stochastic delays that depend on system state and time. Our methods generalize the next reaction method and τ-leaping method to support arbitrary inter-event time distributions while preserving computational scalability. We also introduce a coupling scheme to generate exact non-Markovian sample paths that are positively correlated to an approximate non-Markovian τ-leaping sample path. This enables substantial computational gains for simulation and Bayesian inference through multilevel Monte Carlo and multifidelity schemes. We demonstrate the effectiveness of our approach using several non-Markovian examples, showing substantial gains in both simulation accuracy and inference efficiency. These results extend the practical applicability of non-Markovian models in systems biology and beyond.

The complex swarming dynamics of malaria mosquitoes emerge from simple minimally-interactive behavioral rules

2026-08-26T14:00:00Z

by Antoine Cribellier, Bèwadéyir Serge Poda, Roch K. Dabiré, Abdoulaye Diabaté, Olivier Roux, Florian T. Muijres

Swarming is a widespread collective behavior in animals, often thought to emerge from complex interactions among individuals. Here, we show that mating swarms of malaria mosquitoes can emerge from simple behavioral rules with minimal interaction between individuals. We analyzed two published experimental datasets with three-dimensional flight tracks of Anopheles coluzzii mosquitoes swarming in the lab under simulated sunset conditions and above a visual marker. We found that individuals alternate between straight flight and rapid turning maneuvers known as saccades. These saccades are triggered at the edge of the swarm, and their directions are biased along the sunset direction. This behavior was found consistent between both laboratory datasets, including swarming of solitary individuals; this indicates that inter‑individual interactions may play a limited role within the small-to-medium sized swarms that we studied. We developed a simple agent-based model incorporating three behavioral rules: attraction to the swarm center, alignment normal to the sunset horizon, both driven by environmental cues, and repulsion through short-range collision avoidance. This parsimonious model reproduced key features of the observed laboratory mosquito swarms, including looping flight paths, central density peaks, and directional alignment. Even in the absence of direct inter‑individual interactions, the model generated realistic emergent swarm dynamics, suggesting that social coordination is not required for swarm emergence. Although our lab-based findings remain to be confirmed across more naturalistic conditions, they suggest that the most parsimonious hypothesis behind male mosquito swarming is that it is primarily guided by environmental perception rather than social interaction. This minimal framework offers a new perspective on insect swarming and may apply broadly to the many other insect species that form mating swarms. Understanding the behavioral rules of mosquito swarming could inform strategies for vector control and improve the effectiveness of interventions targeting mosquito reproduction.

Modeling the influences of non-local connectomic projections on geometrically constrained cortical dynamics

2026-08-26T14:00:00Z

by Rishikesan Maran, Eli J. Müller, Ben D. Fulcher

The function and dynamics of the cortex are fundamentally shaped by the specific wiring configurations of its constituent axonal fibers, also known as the connectome. However, many dynamical properties of macroscale cortical activity are well captured by instead describing the activity as propagating waves across the cortical surface, constrained only by the surface’s two-dimensional geometry. It thus remains an open question why the local geometry of the cortex can successfully capture macroscale cortical dynamics, despite neglecting the specificity of Fast-conducting, Non-local Projections (FNPs) which are known to mediate the rapid and non-local propagation of activity between remote neural populations. Here we address this question by conducting a range of investigations using a mathematical model of macroscale cortical activity, in which cortical populations interact both by a continuous sheet and by an additional set of FNPs wired independently of the sheet’s geometry. By simulating the model across a range of external inputs, timescales, and idealized connectome topologies, we demonstrate that the addition of FNPs strongly shape the model dynamics of rapid, stimulus-evoked responses on fine millisecond timescales, but contribute relatively little to slower, spontaneous fluctuations over longer order-of-seconds timescales, which increasingly resemble geometrically constrained dynamics without FNPs. Our results suggest that the discrepant views regarding the relative contributions of local (geometric) and non-local (connectomic) cortico-cortical interactions are context-dependent: While FNPs specified by the connectome are needed to capture rapid communication between specific distant populations (as per the rapid processing of sensory inputs), they play a relatively minor role in shaping slower spontaneous fluctuations (as per resting-state functional magnetic resonance imaging).

Manifold-constrained plasticity enables stable learning in recurrent neural circuits

2026-08-25T14:00:00Z

by Camille Godin, Jean-Philippe Thivierge

The activity of large neuronal populations is often confined to low-dimensional manifolds that can drift over time, posing a challenge for learning rules that assume stable, full-rank representations. Here, we introduce SPLiT (Synaptic Projection Learning with intrinsic Tracking), a synaptic plasticity rule for recurrent neural networks that combines unsupervised manifold tracking with supervised learning. SPLiT uses an online Oja rule to continuously estimate the intrinsic low-dimensional activity subspace and performs normalized least-mean-squares learning in manifold coordinates. In this way, SPLiT keeps synaptic weights aligned with evolving population dynamics. Using a rate-based recurrent network, we show that SPLiT reliably learns time-varying target signals under both constrained dynamics, where activity is restricted to a fixed low-dimensional manifold, and unconstrained dynamics exhibiting changes in the dominant activity subspace. We show that manifold-constrained learning with SPLiT yields faster convergence, less sensitivity to recurrent gain, smaller weight updates, and is more robust to noisy teaching signals. Analytical results show that SPLiT learns the optimal decoder projected onto the instantaneous principal subspace and maintains bounded error under drift. Together, these findings provide a mechanistic account of how synaptic plasticity can leverage the structure of neural manifolds to enable stable and efficient supervised learning despite representational drift.

The multi-omics fallacy in microbiome science

2026-08-25T14:00:00Z

by Rebecca Lewandowski

Artificial intelligence and machine-learning-assisted multi-omics have expanded the scale and ambition of microbiome research, but they have also sharpened an older interpretive problem. Biologically plausible structure is too easily mistaken for biological explanation. This Perspective defines the multi-omics fallacy as claim inflation that occurs when integrating microbial, host, environmental, spatial, and clinical data is assumed to move interpretation from association toward verified mechanism without a corresponding gain in measurement, localization, temporal resolution, functional linkage, or perturbation. The risk is not that computational integration lacks value. It is that predictions, imputations, inferred pathways, feature attributions, and cross-layer networks can acquire mechanistic authority before their biological status has been established. In microbiome science, where stool readouts, taxonomic abundance, inferred function, and predicted metabolites often serve as proxies for host-microbial interaction, this slippage can make uncertain claims appear more complete than the evidence allows. Computational confidence can amplify structured artifact when systematic error becomes learnable. This Perspective proposes a model-to-mechanism burden of proof that distinguishes prediction from explanation, imputation from observation, attribution from causality, cross-layer coherence from mechanism, and diagnostic performance from biological validity. This framework is intended to strengthen, not constrain, computational microbiome science by clarifying which outputs support classification or hypothesis generation and which require direct measurement, localization, temporal analysis, functional validation, or perturbation. Used this way, computational models can help expose uncertainty, prioritize experiments, identify fragile claims, and sharpen biological questions. The result would be a more powerful form of computational microbiome science, one in which models do not stand in for mechanisms but guide the work needed to earn them.

Theory and evidence of amplitude control by frequency detuning in a coupled neuronal oscillator system

2026-08-25T14:00:00Z

by Adam C. Lu, Seyed AmirHossein Ourang, Jeffrey D. Moore

Neuronal oscillator circuits that generate rhythmic movements must operate flexibly and reliably to produce the varied motor patterns that animals exhibit naturally. Rodents rhythmically “whisk” their vibrissae for haptic perception, and they dynamically adjust the whisking range to serve different perceptual goals. Whisking is controlled by a brainstem oscillator circuit that is coupled to breathing, yet how whisking amplitude is modulated remains unknown. Here we propose and evaluate an amplitude control mechanism based on principles of synchronization in coupled oscillators. Specifically, a re-analysis of rat behavioral data demonstrates that whisking exhibits kinematic signatures and phase dynamics of amplification via entrainment with “sniffing”, a mode of high-frequency breathing. A neuronal network model of the whisking oscillator circuit suggests that whisking amplitude can be modulated by shifting the oscillator’s intrinsic frequency relative to the sniffing frequency, analogous to the engineering technique of “detuning”. Based on these results, we propose that detuning between coupled neuronal oscillators may represent a general computational strategy for gain control in nervous systems.

High reelin expression may explain why a subgroup of entorhinal cortex neurons functions as an initial nucleation site of Alzheimer’s disease

2026-08-25T14:00:00Z

by Asgeir Kobro-Flatmoen, Jagir R. Hussan, Peter J. Hunter, Stig W. Omholt

The entorhinal cortex (EC) plays a crucial role in memory functions. Long before the clinical symptoms of Alzheimer’s disease (AD) emerge, it has already undergone significant degeneration, making it a primary site for the onset of the disease. The reasons for this remain elusive. Layer II (LII) neurons of the anterolateral EC are especially prone to display a very early increase in intracellular amounts of amyloid-β peptide (Aβ) and hyperphosphorylated tau protein (p-tau). The expression of the large glycoprotein reelin is extraordinarily high in ECLII neurons compared to most other cortical neurons and proximity ligation assay data and other immunohistochemical data strongly support the notion that reelin binds to Aβ in these neurons. Here, based on the premise that reelin may function as a sink for intracellular Aβ, we show by computational modeling that, in a senescent physiology predisposing to frequent inflammation-driven Aβ42 production bursts over a decades-long period, the intracellular amount of Aβ42-reelin complexes can accumulate to extraordinarily high levels in anterolaterally positioned LII neurons compared to the vast majority of cortical neurons. This is consistent with experimental data showing that intracellular accumulations of Aβ42 positive material ranged from 20 to 80% of total soma volume in EC neurons from patients with idiopathic AD. Based on known tau protein biology, we also show that this extreme intracellular aggregation that overloads the lysosomal degradation machinery, manifesting chronic homeostatic dysregulation, can lead to the production of p-tau fragments prone to aggregation. Together, our findings may contribute to the resolution of why the EC is so strongly associated with the very early etiology of AD.

Double shrinkage transfer causal learning: An application to alzheimer’s disease

2026-08-24T14:00:00Z

by Yunxin Shi, Lulu Pan, Yu Hu, Yongfu Yu, Guoyou Qin

Amyloid-Beta 42 (ABeta42) is a key biomarker of cerebral amyloidosis in Alzheimer’s disease, and estimating its causal effect on subsequent cognitive outcomes is important for understanding disease progression in racial minority populations, such as African Americans. However, causal effect analyses in such populations are often challenged by limited sample sizes, leading to biased or unstable estimates when only target-population data are used. Existing transfer causal learning methods borrow information from a larger related source domain, but may be unstable when source-domain estimation is high-dimensional or when irrelevant covariates introduce noise into transferred nuisance models. We propose a double shrinkage transfer causal learning (DSTCL) estimator that regularizes both the initial source domain estimation and the source-target parameter differences, and then estimates causal effects using a doubly robust framework. We evaluated DSTCL in simulations under varying source sample sizes, inter-domain similarities, covariate dimensions, and other settings, and then applied it to the Alzheimer’s Disease Neuroimaging Initiative dataset using African American participants as the target domain and non-Hispanic White participants as the source domain. In simulations, DSTCL achieved small bias, low mean squared error, and stable confidence interval coverage, generally outperforming competing methods. In the ADNI application, lower baseline ABeta42, reflecting greater amyloid burden, was associated with worse 12-month cognitive performance among African American participants, with DSTCL producing the narrowest confidence interval among the methods compared. These findings suggest that DSTCL provides a practical framework for transfer causal inference in biomedical studies with limited target population.

Mechanochemical modeling of exercise-induced skeletal muscle hypertrophy

2026-08-24T14:00:00Z

by Ingvild S. Devold, Marie E. Rognes, Padmini Rangamani

Skeletal muscle displays remarkable plasticity, adapting its size and strength in response to mechanical loading, especially, from exercise. This process, known as hypertrophy, is fundamental to athletic training and rehabilitation, but is challenging to quantitatively predict due to its multifactorial, multiscale nature. Specifically, skeletal muscle hypertrophy results from an integration of macroscopic mechanical stimuli with the intracellular signaling pathways that govern muscle growth. In this work, we present a multiscale computational model that mechanistically integrates these mechanical and biochemical stimuli and offers a framework for predicting the outcomes of different types of exercise on skeletal muscle growth. The framework couples a transversely isotropic hyperelastic model for tissue-level mechanics with a system of ordinary differential equations representing the IGF1-AKT-mTOR-FOXO signaling pathway, a key regulator of protein synthesis and degradation. We link these scales using a volumetric growth model, where the signaling dynamics inform a growth tensor that drives changes in muscle cross-sectional area. This approach enables the simulation of long-term muscle adaptation, providing a mechanistic tool to investigate how different exercise protocols lead to macroscopic hypertrophy. Simulations from our model capture the temporal dynamics of hypertrophy under varying load protocols and highlight how feedback between protein synthesis and muscle growth regulates the dose-response relationship to prevent unbounded growth. Using muscle geometries derived from the Visible Human dataset, we study how human variations in muscle geometry affect hypertrophy. Finally, we demonstrate that the mechanochemical coupling between muscle geometry and signaling not only predicts macroscopic shape changes but also provides buffering from local signaling heterogeneity. Ultimately, this framework offers a predictive computational tool for optimizing training regimens and understanding the multiscale determinants of muscle adaptations.

How host mobility formulations shape estimates of pathogen dispersal and epidemic risk in non endemic regions

2026-08-24T14:00:00Z

by Charley Presigny, Piero Poletti, Stefano Merler, Manlio De Domenico

The global warming effects of climate change favor the stable residence of invasive vectors, such as the mosquito Aedes albopictus, in temperate areas, like the Mediterranean basin or north America. Thus, favorable weather conditions, together with human mobility, increase the likelihood of large-scale vector-borne disease epidemics in areas where they were historically not endemic. While mathematical modeling acknowledges the relevance of human mobility in the persistence or prevalence of vector-borne diseases in endemic areas, the combined role of host-mediated vector transport and long-range airline travel in the emergence of epidemics in non-endemic areas is poorly understood. In this context, the impact of specific modeling formulations of human mobility on the epidemic predictions remains elusive. To bridge this gap, we compare two alternative models - one based on the force of infection from different source locations and one based on the explicit physical diffusion of individuals - that incorporate intra- and inter-country human mobility (considering air traffic flows), vector dispersal, and human-mediated vector mobility (e.g., vectors in cars/trains), with applications to diseases such as chikungunya, dengue and Zika. Through extensive computational and analytical analysis, we assess how implicit assumptions and the inclusion of different mobility components influence model estimates of epidemic emergence risk, the timing of potential epidemics and the risk of epidemic spread. The comparison between the two frameworks is illustrated using synthetic networks and Italy as a realistic case study. Our findings show that different model formulations of human mobility yield divergent risk assessment, affecting predictions of large scale outbreaks and importation times driven by explained mechanistic difference. Despite these differences, both models show consistent qualitative patterns in the spatial risk and the likelihood of concurrent local outbreaks. Integrating multiple mobility components and carefully identifying assumptions that reflect observed mechanisms are essential for understanding epidemics in currently non-endemic areas.

The perils of omitting omissions when modeling evidence accumulation

2026-08-21T14:00:00Z

by Xiamin Leng, Alexander Fengler, Amitai Shenhav, Michael J. Frank

Response deadlines are commonly imposed in decision-making research to incentivize speedy decisions and sustained attention. This procedure frequently leads to a proportion of trials in which no response is made, and these omissions are often simply removed from the data during analysis. Here we show that this seemingly trivial assumption is in fact quite consequential for parameter estimation. We propose that omissions should instead be treated as observations to inform inference of the underlying generative process that led to their occurrence. Using new tools from likelihood-free inference applicable to a broad class of sequential sampling models (SSMs), we enable fast computation of omission probability without explicit integration, and clarify the degree to which omitting omissions – even in seemingly benign settings – can lead researchers astray. We explore this phenomenon in the setting of SSMs with constant and time-varying boundaries, and show that parameter recovery is improved by incorporating a model of omission probability. We show that the benefits of modeling omission probability are distinct from benefits gained from past approaches of modeling attentional lapses to account for these omissions. Our findings shine a light on the consequences of omitting omission in modeling choice behavior with SSMs, and demonstrate how joint modeling of observed and omitted data can improve parameter inference and therefore the reliability of downstream scientific conclusions.

An in silico framework for dissecting the mechanistic origins of in vivo recorded neuronal activity

2026-08-21T14:00:00Z

by Bjorge Meulemeester, Arco Bast, María Royo, Rieke Fruengel, Su Saka, Foivos Kastrinakis, Marcel Oberlaender

How can we identify the mechanistic origins of the electrophysiological activity that is recorded from neurons in the living brain? A promising strategy for addressing this question is to generate biologically realistic models of in vivo recorded neurons, and simulate how they transform synaptic inputs from the network into their observed neuronal activity. For this purpose, we here provide our approaches for the generation, simulation, and analysis of network-embedded neuron models as an open source, fully documented and freely available software environment: In Silico Framework (ISF). ISF is centered around the concept of achieving “model consensus” about the mechanistic origins of in vivo recorded activity across biologically diverse sets of models. To achieve such model consensus, ISF offers three key workflows. First, ISF enables users to generate models that are equally well constrained by empirical data at subcellular, cellular and network scales, while the set of models as a whole is constructed to exhibit maximally diverse parameters, spanning the full ranges permitted by the empirically observed biological variability at each scale. Second, ISF enables users to identify those subsets of model configurations that predict the in vivo observations without being tuned to do so. Third, for each of those model configurations, ISF enables users to identify which mechanisms at subcellular, cellular and network scales are necessary to predict the in vivo observations, and which mechanisms are dispensable. Thereby, ISF can reveal which mechanisms are common across model configurations, and whether the diversity of model configurations could account for the variability of the in vivo observed activity across animals, cells and trials. In essence, by achieving such model consensus, ISF predicts mechanisms that are robust across biological variability, and which may hence indeed be used in vivo. Finally, ISF enables users to derive model consensus for in silico manipulations, to identify which experimental strategies would be best suited to test the predicted mechanisms in vivo. We exemplify how we have used this iterative in silico - in vivo approach of ISF to dissect the mechanistic origins of sensory responses in the barrel cortex. By making ISF available as a standalone online resource, we believe it will facilitate the generation, simulation and analysis of models that reveal mechanistic origins of in vivo recorded activity beyond the barrel cortex for which it was originally designed.