PLOS Computational Biology: New Articles

A phase-field model for vesicle membranes incorporating area-difference elasticity

2026-04-17T14:00:00Z

by Yihong Liang, Emine Celiker, Ping Lin

This paper presents a phase-field model for simulating the three-dimensional deformation of vesicle membranes, incorporating area-difference elasticity (i.e., the elasticity arising from the difference between the inner and outer lipid leaflets), with constraints on bulk volume and surface area. We develop efficient numerical schemes based on the Fourier-spectral method for spatial discretization and temporal evolution. The model successfully captures a wide variety of steady-state vesicle shapes. The numerical experiments demonstrate that by tuning the simulation parameters, the vesicle can transition from a simple spherical and discocyte shape to complete membrane fission, asymmetric pear shaped structures, as well as complex multi-armed starfish-like and nested configuration. These results highlight the crucial role of area-difference elasticity in determining vesicle morphology.

‘Backpropagation and the brain’ realized in cortical error neuron microcircuits

2026-04-17T14:00:00Z

by Kevin Max, Ismael Jaras, Arno Granier, Katharina A. Wilmes, Mihai A. Petrovici

Neural responses to mismatches between expected and actual stimuli have been widely reported across different species. How does the brain use such error signals for learning? While global error signals can be useful, their ability to learn complex computation at the scale observed in the brain is lacking. In comparison, more local, neuron-specific error signals enable superior performance, but their computation and propagation remain unclear. Motivated by the breakthrough of deep learning, this has inspired the ‘backpropagation and the brain’ hypothesis, i.e., that the brain implements a form of the error backpropagation algorithm. In this work, we introduce a biologically motivated, multi-area cortical microcircuit model, implementing error backpropagation under consideration of recent physiological evidence. We model populations of cortical pyramidal cells acting as representation and error neurons, with bio-plausible local and inter-area connectivity, guided by experimental observations of connectivity of the primate visual cortex. In our model, all information transfer is biologically motivated, inference and learning occur without phases, and network dynamics demonstrably approximate those of error backpropagation. We show the capabilities of our model on a wide range of benchmarks, and compare to other models, such as dendritic hierarchical predictive coding. In particular, our model addresses shortcomings of other theories in terms of scalability to many cortical areas. Finally, we make concrete predictions, which differentiate it from other theories, and which can be tested experimentally.

Double-CRISPR Knockout Simulation (DKOsim): A Monte-Carlo randomization system to model cell growth behavior and infer the optimal library design for growth-based double knockout screens

2026-04-17T14:00:00Z

by Yue Gu, Traver Hart, Luis Leon-Novelo, John Paul Shen

Advances in functional genomic technology, notably CRISPR using Cas9 or Cas12, now allow for large-scale double perturbation screens in which pairs of genes are inactivated, allowing for the experimental detection of genetic interactions (GIs). However, as it is not possible to validate GIs in high-throughput, there is no gold standard dataset where true interactions are known. Hence, we constructed a Double-CRISPR Knockout Simulation (DKOsim), which allows users to reproducibly generate synthetic simulation data where the single gene fitness effect of each gene and the interaction of each gene pair can be specified by the investigator. We adapted Monte-Carlo randomization methods to extend single knockout simulation methods to double knockout designs, which simulate the gene-gene interactions between all possible combinations of the input genes. Using DKOsim, we generated simulated datasets that closely resemble real double knockout CRISPR datasets in terms of Log Fold Change (LFC), GI distribution, and replicate correlation. We further inferred optimal CRISPR library designs by systematically investigating critical experimental parameters including depth of coverage, guide efficiency, and the variance of initial guide distribution. This simulation scheme will help to identify optimal computational methods for GI detection and aid in the design of future dual knockout CRISPR screens.

Systematic prioritization of potential therapeutic targets for glomerulonephritis using multi-omics Mendelian randomization

2026-04-16T14:00:00Z

by Guoqiang Li, Fu Jianhan, Jiashu Gu, Yinhuai Wang, Jiachen Liu, Dong Yang, Dianjie Zeng, Pengcheng Zhao

Glomerulonephritis (GN) is an immune-mediated kidney disorder that causes glomerular injury, progressive renal dysfunction, and end-stage kidney disease. Traditional treatments such as corticosteroids and immunosuppressants are limited by variable efficacy and severe adverse effects, highlighting the need for novel therapeutic targets and personalized strategies. We performed a systematic multi-omics Mendelian randomization (MR) analysis applying established proteomic and transcriptomic quantitative trait loci (pQTL/eQTL) resources to genome-wide association studies (GWAS) of four GN subtypes: acute, chronic, IgA nephropathy, and membranous nephropathy. Bayesian colocalization was used to strengthen causal inference, while independent replication and meta-analysis were conducted using the FinnGen cohort. Mouse knockout phenotypes, drug reposition, and computational pharmacology algorithm were applied to evaluate translational potential. Proteomic-wide MR revealed MTR as protective in chronic GN and HCK as a risk factor for membranous nephropathy, whereas CD302 and CDKN1B showed protective effects. Transcriptomic-wide MR identified candidate genes across GN subtypes: RECQL, BRSK2, and MGP in acute GN; AFM, CFHR5, and EPHB2 in chronic GN; IL6R, MBL2, and PRSS3 in IgA nephropathy; and TIMP4, HCK, and PEAR1 in membranous nephropathy. Bayesian colocalization analysis provided strong support for shared causal variants (PPH4 > 0.8) for HCK, CD302, TIMP4, PEAR1, PARP1, and FHIT. Replication and meta-analysis in the FinnGen cohort provided additional consistency across datasets, while downstream translational annotations highlighted IL6R, MBL2, C5, and CD55 as potential hub targets within immune and complement-related pathways. This integrative multi-omics study provides novel insights into the genetic architecture and therapeutic landscape of GN, identifying potential therapeutic targets that may inform precision nephrology and drug repurposing. Notably, most targets supported by colocalization, mouse knockout phenotypes, and drug repurposing evidence were predominantly identified in membranous nephropathy, suggesting a particularly tractable genetic and therapeutic architecture for this subtype.

Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine

2026-04-16T14:00:00Z

by Raffaele Marchesi, Nicolò Lazzaro, Walter Endrizzi, Gianluca Leonardi, Matteo Pozzi, Flavio Ragni, Stefano Bovo, Monica Moroni, Venet Osmani, Giuseppe Jurman

Integration of multimodal, multi-omics data is critical for advancing precision medicine, yet its application is frequently limited by incomplete datasets where one or more modalities are missing. To address this challenge, we developed a generative framework capable of synthesizing any missing modality from an arbitrary subset of available modalities. We introduce Coherent Denoising, a novel ensemble-based generative diffusion method that aggregates predictions from multiple specialized, single-condition models and enforces consensus during the sampling process. We compare this approach against a multi-condition, generative model that uses a flexible masking strategy to handle arbitrary subsets of inputs. The results show that our architectures successfully generate high-fidelity data that preserve the complex biological signals required for downstream tasks. We demonstrate that the generated synthetic data can be used to maintain the performance of predictive models on incomplete patient profiles and can leverage counterfactual analysis to guide the prioritization of diagnostic tests. We validated the framework’s efficacy on a large-scale multimodal, multi-omics cohort from The Cancer Genome Atlas (TCGA) of over 10,000 samples spanning across 20 tumor types, using data modalities such as copy-number alterations (CNA), transcriptomics (RNA-Seq), proteomics (RPPA), and histopathology (WSI). This work establishes a robust and flexible generative framework to address sparsity in multimodal datasets, providing a key step toward improving precision oncology.

Developmental and aging changes in brain network switching dynamics revealed by EEG phase synchronization

2026-04-16T14:00:00Z

by Dionysios Perdikis, Rita Sleimen-Malkoun, Viktor Müller, Viktor Jirsa

Adaptive behavior depends on the brain’s capacity to vary its activity across multiple spatial and temporal scales. Yet, how distinct facets of this variability evolve from childhood to older adulthood remains poorly understood, limiting mechanistic models of neurocognitive aging. Here, we characterize lifespan neural variability using an integrated empirical-computational approach. We analyzed high-density EEG cohort data spanning 111 healthy individuals aged 9–75 years, recorded at rest and during passive and attended auditory oddball stimulation task. We extracted scale-dependent measures of EEG fluctuations amplitude and entropy, together with millisecond-resolved phase-synchrony networks in the 2–20 Hz range. Multi-condition partial least squares decomposition analysis revealed two independent lifespan trajectories. First, slow-frequency power, variance and complexity at longer timescales declined monotonically with age, indicating a progressive dampening of low-frequency fluctuations and large-scale coherence. Second, the temporal organization of phase-synchrony reconfigurations followed an inverted U-trend: young adults exhibited the slowest yet most diverse switching—characterized by low mean but high variance and low kurtosis of jump lengths at 2–6 Hz and the opposite pattern at 8–20 Hz—whereas children and older adults showed faster, more stereotyped dynamics. To mechanistically account for these patterns, we fitted a ten-node phase-oscillator model constrained by the human structural connectome. Only an intermediate, metastable coupling regime reproduced qualitatively the empirical finding of maximally heterogeneous synchrony dynamics observed in young adults, whereas deviations toward weaker or stronger coupling mimicked the children’s and older adults’ profiles. Our results demonstrate that development and aging entail changes in the switching dynamics of EEG phase synchronization, by differentially sculpting stationary and transient aspects of neural variability. This establishes time-resolved phase-synchrony metrics as sensitive, mechanistically grounded markers of neurocognitive status across the lifespan.

Perceptual prediction error supports implicit process in motor learning

2026-04-15T14:00:00Z

by Xiaoyue Zhang, Wencheng Wu, Kunlin Wei

Error-based learning underlies motor learning, but what specific motor error drives implicit learning, the procedural component of motor skill, is unclear. A typical action consists of a movement and a performance outcome, e.g., grabbing a coffee cup involves a reaching movement and its actual landing of the hand relative to the target cup. While performance error is fundamental for the cognitive component of motor learning, what error, either performance or movement prediction error, underlies implicit motor learning has not been resolved. These two errors are hard to disentangle as the performance outcome is an integral part of the movement. Here we used the classical visuomotor adaptation paradigm, in which people learn to counter visual perturbations by deliberately aiming off the target, to dissociate the performance error from the prediction error. Using a series of behavioral experiments and model comparisons, we revealed that movement prediction error, but not performance error, can parsimoniously explain diverse learning effects. Importantly, despite the perturbation being visual, the movement prediction error is not specified in visual terms, but determined by a perceptual estimate of the hand kinematics. In other words, contrary to the widely-held concept of sensory prediction error, a perceptual prediction error drives implicit motor learning.

How the visual brain can learn to parse images using a multiscale, incremental grouping process

2026-04-15T14:00:00Z

by Sami Mollard, Sander M. Bohte, Pieter R. Roelfsema

Natural scenes usually contain many objects that need to be segregated from each other and the background. Object-based attention is the process that groups image fragments belonging to the same objects. Curve-tracing tasks provide a special case, testing our ability to group image elements of an elongated curve. In the brain, curve-tracing is associated with the gradual spread of enhanced neuronal activity over the representation of the traced curve. Previous studies demonstrated that the tracing speed is higher if curves are far apart than if they are nearby. One hypothesis is that a larger distance between curves permits activity propagation in higher visual cortical areas. In these higher areas receptive fields are larger and connections exist between neurons representing image regions that are farther apart (Pooresmaeili et al., 2014). We propose a recurrent architecture for the scale-invariant tracing of curves and objects. The architecture is composed of a feedforward pathway that dynamically selects the appropriate scale for tracing, and a recurrent pathway for propagating enhanced neuronal activity through horizontal and feedback connections, enabled by a disinhibitory loop involving VIP and SOM interneurons. We trained the network using a biologically plausible reinforcement learning scheme and observed that training on short curves allowed the networks to generalize to longer curves and 2D-objects. The network chose the scale based on the distance between curves and the width of objects, just as in human psychophysics and the visual cortex of monkeys. The results provide a mechanistic account of the learning and execution of multiscale perceptual grouping in the brain.

Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function

2026-04-15T14:00:00Z

by Arthur Fyon, Guillaume Drion

Neurons rely on two interdependent mechanisms — homeostasis and neuromodulation — to maintain robust and adaptable functionality. Calcium homeostasis stabilizes neuronal activity by adjusting ionic conductances, whereas neuromodulation dynamically modifies ionic properties in response to external signals carried by neuromodulators. Combining these mechanisms in conductance-based models often produces unreliable outcomes, particularly when sharp neuromodulation interferes with calcium-homeostatic tuning. This study explores how a biologically inspired neuromodulation controller can harmonize with calcium homeostasis to ensure reliable neuronal function. Using computational models of stomatogastric ganglion and dopaminergic neurons, we demonstrate that controlled neuromodulation preserves neuronal firing patterns while calcium homeostasis simultaneously maintains target intracellular calcium levels. Unlike sharp neuromodulation, the neuromodulation controller integrates activity-dependent feedback through mechanisms mimicking G-protein-coupled receptor cascades. The interaction between these controllers critically depends on the existence of an intersection in conductance space, representing a balance between target calcium levels and neuromodulated firing patterns. Maximizing neuronal degeneracy enhances the likelihood of such intersections, enabling robust modulation and compensation for channel blockades. We further show that this controller pairing extends to network-level activity, reliably modulating the rhythmic activity of central pattern generators. This study highlights the complementary roles of calcium homeostasis and neuromodulation, proposing a unified control framework for maintaining robust and adaptive neural activity under physiological and pathological conditions.

Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?

2026-04-15T14:00:00Z

by Lauren A. White, Tomás M. León

For infectious disease forecasting challenges, individual model performance typically varies across space and time. This phenomenon raises the question: are there properties of the target time series that contribute to a particular season, location, or disease being more difficult to forecast? Here we characterize a time series’ future predictability using a forecastability metric that calculates the spectral entropy of the time series. Forecastability of syndromic influenza hospital admissions for the state of California varied widely across seasons and was positively correlated with peak burden. Next, using archived U.S. state and national forecasts targeting laboratory-confirmed COVID-19 and influenza hospital admissions, we investigated the relationship between forecastability and: (i) population size of the forecasting target, and (ii) forecast performance as measured by mean absolute error, weighted interval score (WIS), and scaled relative WIS. Forecastability increased with increasing population size of the forecasting target, and forecasting performance generally improved with higher forecastability when mitigating the effects of population size across scales. These preliminary results support the idea that some targets and respiratory virus seasons may be inherently more difficult to forecast and could help explain inter-seasonal variation in model performance.

Unveiling gene perturbation effects through gene regulatory networks inference from single-cell transcriptomic data

2026-04-15T14:00:00Z

by Clelia Corridori, Merrit Romeike, Giorgio Nicoletti, Christa Buecker, Samir Suweis, Sandro Azaele, Graziano Martello

Physiological and pathological processes are governed by networks of genes called gene regulatory networks (GRNs). By reconstructing GRNs, we can accurately model how cells behave in their natural state and predict how genetic changes will affect them. Transcriptomic data of single cells are now available for a wide range of cellular processes in multiple species. Thus, a method building predictive GRNs from single-cell RNA sequencing (scRNA-seq) data, without any additional prior knowledge, could have a great impact on our understanding of biological processes and the genes playing a key role in them. To this aim, we developed IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework designed to infer directed, weighted, and signed GRNs directly from unperturbed single-cell RNA sequencing data. IGNITE uses the GRNs to generate gene expression data upon single and multiple genetic perturbations. IGNITE is based on the inverse problem for a kinetic Ising model, a model from statistical physics that has been successfully applied to biological networks. We tested IGNITE on two complementary systems of pluripotent stem cells (PSCs): murine PSCs transitioning from the naïve to formative states, and human PSCs differentiating toward definitive endoderm. These datasets differ in species, developmental trajectory, and single-cell technology (10X vs. Fluidigm C1), providing a stringent test of generalizability. Using only unperturbed scRNA-seq data, IGNITE simulated single and multiple gene knockouts (KOs) and produced predictions consistent with independent experimental observations. In mouse PSCs, IGNITE generated wild-type data highly correlated with experiments and accurately predicted the effects of Rbpj, Etv5, and triple KOs, while in human PSCs it correctly predicted differentiation-promoting and differentiation-blocking perturbations, in agreement with published studies. These results demonstrate that IGNITE robustly captures gene interaction logic across species and technologies, enabling robust in silico perturbation analyses directly from scRNA-seq data.

How muscle ageing affects rapid goal-directed movement: mechanistic insights from a simple model

2026-04-15T14:00:00Z

by Delyle T. Polet, Christopher T. Richards

As humans and other animals age, passive and active muscle properties change markedly, with reduced peak tension, peak strain rate, activation and deactivation rate, and increased parallel stiffness. It is thought that these alterations modify locomotor performance, but establishing causal links is difficult when many parameters vary at once. We developed a simplified model of an elbow joint with two antagonistic Hill-type muscles, and varied the associated muscle parameters combinatorially over a large range. For a given parameter combination, we found optimal joint movements that minimized cumulative squared error to a target while starting and ending at rest. Emergent behaviour from the optimisations compared well to ballistic point-to-point arm movements in humans. Age-associated reductions of maximum isometric force, maximum strain rate and activation rate all had detrimental effects on performance, independent of other parameters. In contrast, deactivation time and passive parallel stiffness had no effect on performance on their own, but pronounced interactive effects with each other. Increasing stiffness reduced joint movement time at fast deactivation rates, but increased movement time at slow deactivation rates. This occurs because antagonist muscles resist the passive tension at rest, but are stretched eccentrically by the agonist, amplifying their active resistive force. Fast-deactivating muscles can avoid this resistive effect, allowing the passive stiffness to amplify accelerating force and enhance performance. In all cases, coactivation emerged as optimal during and after the braking period, and during the acceleration phase when stiffness increased. As deactivation time increased, so too did coactivation levels– but coactivation was not generally associated with a reduction in performance. Our simulations offer evidence that age-related changes in muscle strength, activation time and maximum contraction velocity can reduce ballistic performance in a goal-directed task, but the effects of increased muscle stiffness and deactivation time depend on their relative values.

On the role of L-type Ca²⁺ and BK channels in a biophysical model of cartwheel interneurons

2026-04-15T14:00:00Z

by Matteo Martin, Jonathan E. Rubin, Morten Gram Pedersen

Cartwheel interneurons (CWCs) in the auditory system, which contribute to auditory processing and pathologies, exhibit a range of activity patterns, including bursting, spiking, and complex spiking. Although experiments have shown how these patterns can vary across individual neurons, the field has lacked a computational framework in which to explore the contributions of particular currents to these observations and to generate new predictions about the effects of pharmacological manipulations. We present a conductance-based CWC computational model, which captures the diversity of CWC activity patterns observed experimentally and suggests parameter changes that may underlie differences across cells. Specifically, we show using direct simulations and bifurcation diagrams that one parameter tuning yields a regular spiking phenotype in which the onset of activity, as input current is increased, takes the form of regular spiking and other tuning that gives a complex spiking phenotype in which bursting occurs at the spike onset and regular spiking only occurs over a narrow input range before it gives way to complex spiking. We next investigate the effects of the BK-type potassium current blocker iberiotoxin and the L-type calcium current blocker nifedipine. Our model reproduces the transitions to complex spiking and regular spiking, respectively, observed experimentally when these drugs are administered. In addition to the full model, we present a reduced model that preserves CWC dynamic regimes. We classify the reduced model variables in terms of distinct dynamic timescales and show that the key transitions in dynamic patterns under administration of iberiotoxin and nifedipine can be explained based on equilibria of the averaged dynamics of the slowest model variables, in a regime where the faster model variables exhibit oscillations. Overall, this study predicts how changes in parameters will influence CWC behavior, suggests how bifurcations contribute to changes in CWC dynamics, and provides a theoretical foundation that supports our simulation findings.

Ten simple rules for organising an effective student-led writing retreat

2026-04-13T14:00:00Z

by Nicholas W. Daudt, Claudia Hird, Eleanor R. M. Kelly, Elli E. Leinikki, Gretchen J. McCarthy, Ian S. Dixon-Anderson, Jackson E. Beagley, Jessica B. Moffitt, Joseph S. Curtis, Lindsay M. Wickman, Meghan L. Duffy, Preston L. Maluafiti, Saskia E. Foreman, William Carome, Leah M. Crowe

Nonlinear effects of noise on outbreaks of mosquito-borne diseases

2026-04-13T14:00:00Z

by Kyle J. -M. Dahlin, Karin Ebey, John E. Vinson, John M. Drake

Mosquito-borne diseases are a significant and growing public health burden globally. Predictions about the spread and impact of mosquito-borne disease outbreaks can help inform direct control and prevention measures. However, climate change is expected to increase weather variability, potentially shaping the future of mosquito-borne disease outbreaks globally. In this study, we sought to determine the effects of demographic and environmental noise (stochasticity) on the duration and size of outbreaks predicted by models of mosquito-borne disease. We developed a demographically and environmentally stochastic Ross-Macdonald model to assess how noise affects the probability of an outbreak, the peak number of cases, and the duration of outbreaks at increasing levels of the basic reproduction number (R₀) and environmental noise strength. Increasing environmental noise reduces the risk of endemic disease from 100% down to almost 0%, but the largest outbreaks occur at intermediate environmental noise levels. In this case, if an outbreak dies out, it ends quickly. In the presence of noise, R₀ alone is insufficient to definitively predict whether an outbreak occurs. Surprisingly, our modelling results suggest that the dramatic effect on mosquito populations from increases in the frequency of extreme environmental conditions could reduce the risk of endemic disease and epidemics in some settings.

Rural-to-urban migrant worker mobility shaped measles epidemics in China

2026-04-10T14:00:00Z

by Peihua Wang, Xianwen Wang, Wenyi Zhang, Yong Wang, Sen Pei, Xiao-Ke Xu, Wan Yang

Despite sustained high routine childhood vaccination coverage, measles outbreaks have persisted across Provincial-Level Administrative Divisions (PLADs) in China. Epidemiological evidence suggests that migrant workers substantially contribute to these outbreaks. In this study, we investigated the role of inter-PLAD rural-to-urban migrant workers, who originate from less developed rural regions with potentially lower vaccination coverage and are employed in urban centers, in contributing to measles epidemics in China from 2005 to 2014. We developed a networked metapopulation Susceptible–Exposed–Infectious–Recovered model that incorporated migrant worker mobility around Chinese New Year (CNY) migration periods and year-round general-purpose traveler mobility. By simulating measles transmission dynamics within migrant worker subpopulations, we identified key epidemiological connections between origin and host PLADs. In northern China, migrant workers from Hebei and Shandong were the key contributors to outbreaks in two northern host PLADs, Beijing and Tianjin. In southern China, migrant workers from Anhui and Sichuan were the key contributors across multiple southern host PLADs. Counterfactual modeling suggests that measles epidemics in host PLADs were sustained by susceptibility replenishment through inflows of under-vaccinated migrant workers during the CNY migration periods. Moreover, epidemics in origin PLADs might have been synchronized and facilitated by case importation of exposed and infectious migrant workers returning from endemic host PLADs, and the strength of this seeding effect depended on the volume of migrant worker flows. Traveler mobility showed minimal impact on measles epidemics. Counterfactual modeling of pre-migration vaccination with coverage ranging from 25% to 100% showed national incidence reduction from 33.0% to 50.9%, with significant reduction in host PLADs, and in turn in origin PLADs due to weakened seeding effect. Our findings provide mechanistic insights into the epidemiological role of rural-to-urban migrant workers in measles epidemics, which could support targeted vaccination strategies for improved measles control in China and regions with similar migration dynamics.

Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions

2026-04-10T14:00:00Z

by Shiyue Li, Yiling Wang, Zhanpeng Shu, Ramon Grima, Qingchao Jiang, Zhixing Cao

Biochemical reactions are inherently stochastic, with their kinetics commonly described by chemical master equations (CMEs). However, the discrete nature of molecular states renders likelihood-based parameter inference from CMEs computationally intensive. Here, we introduce an inference method that leverages analytical solutions in the probability generating function (PGF) space and systematically evaluate its efficiency, accuracy, and robustness. Across both steady-state and time-resolved count data, our numerical experiments demonstrate that the PGF-based method consistently outperforms existing approaches in terms of both computational efficiency and inference accuracy, even under data contamination. These favorable properties further enable the extension of the PGF-based framework to model selection—a task typically considered computationally prohibitive. Using time-resolved data, we show that the method can correctly identify complex gene expression models with more than three gene states, a task that cannot be reliably achieved using steady-state data alone.

Dynamic cholinergic signaling differentially desynchronizes cortical microcircuits dependent on modulation rate and network connectivity

2026-04-10T14:00:00Z

by Sibi Pandian, Scott Rich

Acetylcholine (ACh) affects both the intrinsic properties of individual neurons and the oscillatory tendencies of cortical microcircuits by modulating the muscarinic-receptor gated m-current. ACh concentrations have historically been assumed to vary exclusively over long (supra-second) neuromodulatory timescales, conventionally simplified in silico as a set and constant modulatory tone. However, contemporary experimental studies show cortical ACh concentrations change over sub-second timescales associated with cognitive tasks including attention and sensorimotor coordination. More realistic models reflecting dynamic, sub-second fluctuations in cholinergic tone have yet to be computationally studied. Using a new implementation of a time-varying cholinergic signal in computational excitatory-inhibitory (E-I) spiking neuronal networks, we here delineate how the interaction between dynamic cholinergic modulation and network connectivity influences these systems’ oscillatory tendencies. Synchrony in networks with dominant inter-connectivity (strong E-to-I and I-to-E synapses) is largely unaffected by time-varying cholinergic modulation. In contrast, networks with dominant intra-connectivity (strong E-to-E and I-to-I synapses) desynchronize with increasing cholinergic tone in manners diverging from the predictions of analogous systems with constant ACh levels. The rate and mechanism of this desynchronization is highly sensitive to the modulation’s time course and the E-I connectivity strength. This suggests that traditional in silico simplifications of the temporal profile of cholinergic activity may obscure sub-second neuromodulatory effects, which may be particularly relevant to contemporary efforts to optimize neurostimulation therapies influencing cholinergic pathways.

PhageCGRNet: Integrating Chaos Game Representation of Genomes with Convolutional Neural Network for accurate phage host classification prediction

2026-04-09T14:00:00Z

by Ting Wang, Zu-Guo Yu, Jinyan Li, Xuan Lin

Phages (or bacteriophages) play a critical role in microbial communities, and accurately predicting the hosts of phages is essential for understanding the dynamics of these viruses and their impact on bacterial populations. In the prediction of classification of phage hosts, feature extraction is a critical step that directly affects the accuracy of the predictions. Among the techniques used for feature extraction, k-mers are the most commonly employed method. Although many methods based on k-mers have been proposed, these methods typically use only the frequency information of k-mers as features. However, when frequencies are identical, the frequency information of these k-mers becomes less useful. To address this limitation, we propose a novel method called PhageCGRNet, which not only utilizes the frequency information of k-mers but also incorporates the positional information of k-mers. In our method, we represent each genome sequence as a three-dimensional matrix containing k-mers frequency features and positional features, and then utilize the Convolutional Neural Network model to predict the host category. Specifically, we combine the frequency information of k-mers directly extracted from the sequences with the positional information of k-mers obtained using the Chaos Game Representation method to construct the feature matrix, which serves as the input to the Convolutional Neural Network. We conducted experiments on two benchmark datasets, and compared PhageCGRNet with existing advanced methods for phage host classification. The experimental results demonstrate that PhageCGRNet achieves higher accuracy at both taxonomy levels of species and genus on these two datasets compared to other state-of-the-art methods.

A biophysical model of phagocytic cup dynamics: The effect of membrane tension

2026-04-08T14:00:00Z

by Peyman Shadmani, Behzad Mehrafrooz, Abbas Montazeri, David M. Richards

Phagocytosis is a fundamental cellular process by which cells engulf external particles, controlled by receptor–ligand binding and actin-driven membrane dynamics. While a number of mathematical models have been developed to describe this process, they often overlook membrane tension, a key physical parameter known to influence membrane deformation and cytoskeletal behaviour. To address this gap, we present an enhanced mathematical model of receptor motion during phagocytosis that explicitly incorporates the role of membrane tension. Further, we introduce a signalling component that is coupled to receptor dynamics via the membrane tension. We find that including tension results in fundamentally different engulfment behaviour, which is slower than that predicted by models without tension. In particular, unlike in the previous version of this model, we show that tension can lead to stalled engulfment, an experimentally-observed phenomenon known as frustrated phagocytosis. We also find that signalling is able to modify engulfment behaviour, especially at later stages, and is able to alter cup growth to become linear in time without the need for receptor drift as introduced in previous models. These findings offer new insights into the role of membrane tension and biophysical regulation in phagocytosis, with implications for immune function, cell motility and targeted drug delivery.

Ten simple rules for postdoctoral mums to stay competitive in academia

2026-04-08T14:00:00Z

by Belén Fadrique, Selene Báez

In academia, the intersection of the postdoctoral stage, usually highly unstable and decisive to secure a permanent position, and motherhood, is the most prominent culprit of the well-known problem of the decreasing number of female researchers in senior academic positions. The loss of postdoctoral women from the academic path represents an unsustainable loss of talent, leading to unbalanced academic institutions where this phenomenon eventually gets perpetuated. The motherhood challenges for postdoctoral women begin from the moment they plan on getting pregnant and continue well after reincorporation to work after maternity leave. Here, we provide 10 actionable rules for these postdoctoral women approaching motherhood to increase their chances of remaining in the academic career. These rules will help postdoctoral women prepare for the challenge of becoming a mother while working towards their long-term academic goals, and establish a successful relationship with their supervisors and collaborators under the new circumstances. These rules should be complemented by the general effort from colleagues, supervisors, institutions, and academia as a whole, to create a more supportive working environment. It is in the utmost interest of the academic community to improve the retention of postdoctoral mums and promote their progression to more senior positions.

A framework for constructing insect steering circuits

2026-04-08T14:00:00Z

by Robert Mitchell, Barbara Webb

Insects perform a variety of goal-directed navigation behaviours, in which steering is controlled by a comparison between their current and desired heading direction. Recent work has uncovered the details of such a steering circuit in the fruit fly Drosophila melanogaster. Here we analyse the principles behind the neuroanatomy and physiology of this circuit to derive five general rules which can be used to construct a class of steering circuits which operate in the same way. These rules are surprisingly permissive, suggesting that across insect species, steering circuits may have differing wiring while remaining functionally identical. We simulate several examples, including an irregular circuit that conforms to the rules, and circuits that break the rules, and examine their ability to control steering towards a varying goal direction. We argue that the principled approach we apply here could be applied more generally in performing comparative analyses in neuroscience.

Quantifying SARS-CoV-2 Omicron variant spread and the impact of non-pharmaceutical interventions in Newfoundland and Labrador, Canada

2026-04-08T14:00:00Z

by Francis Anokye, Michael W. Z. Li, Steve Walker, Amy Hurford

The highly transmissible Omicron variant of SARS-CoV-2 caused many infections in Newfoundland and Labrador, Canada, and the fraction of infections that were unreported varied as PCR testing capacity was exceeded and eligibility rules changed. Due to these inconsistencies in the testing rate, we developed a mechanistic model that was calibrated to serological data (Dec 2021–May 2022) to estimate underreporting and understand the impact of non-pharmaceutical interventions on transmission. Our model considers the epidemiology of SARS-CoV-2 spread, natural and vaccine-derived immunity, and the booster dose vaccination campaign that was ongoing in Newfoundland and Labrador. We found that during the early spread of the Omicron variant, when the eligibility for tests that were reported in the provincial counts was less restrictive, three or fewer infections were unreported per reported case. After March 17, 2022, when test eligibility was more restrictive, the underreporting rate increased steadily, with an average of 24.2 infections unreported infections per reported case. We found that Omicron transmission was lower when schools were closed (mean control reproduction number, ℛc = 1.98, 95% CI: 1.58–2.37), higher when open (mean ℛc = 2.71, 95% CI: 2.31–3.11), and of the alert levels, the strictest alert level reduced transmission the most (mean ℛc = 2.23, 95% CI: 1.98–2.53). When underreporting rates vary, the impact of non-pharmaceutical interventions, such as alert level systems and school closures, cannot be determined from reported cases. Our findings highlight the value of combining serological data with modelling to determine the impact of non-pharmaceutical interventions during pandemics when surveillance systems are constrained.

Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies

2026-04-07T14:00:00Z

by Shashwat Sridhar, Michaela Vystrčilová, Mohammad H. Khani, Dimokratis Karamanlis, Helene M. Schreyer, Varsha Ramakrishna, Steffen Krüppel, Sören J. Zapp, Matthias Mietsch, Alexander S. Ecker, Tim Gollisch

Retinal ganglion cells, the output neurons of the vertebrate retina, often display nonlinear summation of visual signals over their receptive fields. This creates sensitivity to spatial contrast, letting the cells respond to spatially structured visual stimuli even when no net change in overall illumination of the receptive field occurs. Yet, computational models of ganglion cell responses are often based on linear receptive fields, and typical nonlinear extensions, which separate receptive fields into nonlinearly combined subunits, are often cumbersome to fit to experimental data. Previous work has suggested to model spatial-contrast sensitivity in responses to flashed images by combining signals from the mean and variance of light intensity inside the receptive field. Here, we extend and adjust this spatial contrast model for application to spatiotemporal stimulation and explore its performance on spiking responses that we recorded from ganglion cells of marmosets under artificial and naturalistic movies. We show how the model can be fitted to experimental data and that it outperforms common models with linear spatial integration to different degrees for different types of ganglion cells. Finally, we use the model framework to infer the cells’ spatial scale of nonlinear spatial integration. Our work shows that the spatial contrast model can capture aspects of nonlinear spatial integration in the primate retina with only few free parameters. The model can be used to assess the cells’ functional properties under natural stimulation and provides a simple-to-obtain benchmark for comparison with more detailed nonlinear encoding models.

Correction: The Burr distribution as a model for the delay between key events in an individual’s infection history

2026-04-06T14:00:00Z

by Nyall Jamieson, Christiana Charalambous, David M. Schultz, Ian Hall

Subunit-specific behavioral modulation of sensory tuning in the visual cortex

2026-04-06T14:00:00Z

by Julia M. Mayer, Wiktor F. Młynarski

Activity of sensory neurons is influenced not only by external stimuli but also by the animal’s behavioral state. It is well documented that behavior influences the general properties of neural activity, such as response gain. However, it is not known whether it could affect the sensory tuning of individual neurons in a more refined way and what the functional benefit of such nuanced modulation might be. Here, we investigate this in the mouse visual cortex using the data made available by the Allen Brain Observatory. Our analysis indicates that locomotion can modulate not only the gain of the entire neuronal response, but also more selectively control responses to specific stimuli. This modulation results in changes of neuronal tuning in different behavioral states. Using numerical simulations, we demonstrate that such patterns of gain modulation can multiplex behavioral information in sensory populations without compromising the accuracy of sensory coding. In that way, the visual cortex could instantiate an accurate, joint representation of sensory and movement-related signals and support computations that simultaneously require both types of information.

Exploring neural manifolds across a wide range of intrinsic dimensions

2026-04-03T14:00:00Z

by Jacopo Fadanni, Rosalba Pacelli, Alberto Zucchetta, Pietro Rotondo, Michele Allegra

The rapid surge in the number of simultaneously recorded neurons demands reliable tools to explore the latent geometry of high-dimensional neural spaces. Within such spaces, neuronal activity typically lies on a subspace or manifold characterized by an intrinsic dimension (ID) that is much lower than the total number of recorded units. The ID can provide immediate information about the neural code, such as the minimum number of encoded variables and the relation between collective and individual neural activity. Existing studies rely on disparate and potentially unreliable ID estimators, which can contribute to conflicting reports of high-dimensional vs. low-dimensional manifolds. Here, we propose a robust and versatile pipeline for ID estimation, exploiting a local version of the full correlation integral estimator (lFCI). Being able to simultaneously cope with high dimensionality and non-linearity, lFCI overcomes some major limitations of common ID estimation methods. We prove the strength and accuracy of lFCI by applying it to synthetic benchmark data by Altan et al., 2019, where other methods typically underestimate the ID. We apply lFCI to study neural manifolds arising in recurrent neural networks trained on the 20 tasks of the well-known ‘cog-Task’ battery. Across tasks and training repetitions, lFCI uncovers a consistently low ID, which we show to be fundamentally related to the task structure. Finally, we apply lFCI to a reference experimental dataset by Stringer et al., 2019, comprising visual responses to a large set of natural images, strongly supporting previous reports that responses are organized in a high-dimensional manifold. lFCI has the potential to shed light on the current debate about the geometry of neural codes, and its dependence on structural constraints and computational goals in biological and artificial neural networks.

Correction: A computational framework to study EGFR signaling distribution in egg chambers during dynamic interactions between soma and germline

2026-04-03T14:00:00Z

by Nastassia Pouradier Duteil, Nicole T. Revaitis, Mathew G. Niepielko, Eric A. Klein, Nir Yakoby, Benedetto Piccoli

A Bayesian modelling framework for estimating tick-borne pathogen transmission dynamics at the host-tick interface

2026-04-03T14:00:00Z

by Younjung Kim, Bruno Faivre, Thierry Boulinier, Célia Sineau, Clémence Galon, Sara Moutailler, Laure Bournez, Raphaëlle Métras

Understanding the transmission dynamics of tick-borne pathogens at the host-tick interface is challenged by the presence of multiple pathways for tick infection, including (i) host-to-tick transmission, (ii) tick-to-tick (cofeeding) transmission, and (iii) pre-existing infection through vertical transmission or prior feeding. Assessing parameters governing these pathways is critical for identifying the main transmission drivers and, consequently, key prevention and control points. Here, we developed a Bayesian modelling framework that estimates key parameters describing the probability of each transmission pathway and assesses associated factors, including bird species, tick life stage and engorgement level, by jointly modelling transmission at the host-tick interface using data collected in field studies that sample hosts and their ticks. First, by fitting the model to simulated host-tick infection data, we demonstrated the framework’s ability to recover the parameter values underlying these data. Model performance improved significantly when more information was available on variability in cofeeding probability among individual ticks, highlighting the value of testing all collected ticks and recording their spatial distribution on the host in relation to each other. Second, we fitted the model to field data collected at the bird-tick interface in Northeast France in 2023, focusing on Borrelia garinii, B. valaisiana, and Anaplasma phagocytophilum as case pathogens. For all three pathogens studied, models including cofeeding transmission explained the data significantly better than models that did not. Engorgement level was significantly and positively associated with the probability of bird-to-tick transmission for A. phagocytophilum. Finally, the estimated parameters, such as the probability of A. phagocytophilum infection in birds and the probability of Borrelia or Anaplasma infection in ticks before feeding, were comparable to values from an external dataset, not used for model fitting. Our framework provides a valuable foundation for future research to understand tick-borne pathogen transmission dynamics based on epidemiological and ecological field data collected at the host-tick interface.

Training biologists in Unix command-line skills: From curriculum to interactive online tutorials

2026-04-03T14:00:00Z

by Lucie Khamvongsa-Charbonnier, Robert Aboukhalil, Hélène Chiapello, Thomas Denecker, Pierre Poulain, Denis Puthier, Olivier Sand, Morgane Thomas-Chollier, Claire Toffano-Nioche

As the generation of data in the life and health sciences expands rapidly, there is a growing need for professionals and students in these fields to master core bioinformatics skills, particularly those relating to Unix-like systems, most commonly used in bioinformatics. This paper introduces two key contributions to address this need: (1) A Unix curriculum for life scientists with little or no command-line experience, based on progressive Unix skill levels for bioinformatics and (2) An implementation of this curriculum into a series of interactive online tutorials deployed through Sandbox.bio—an open-source platform for learning bioinformatics that embeds a command line in the browser, which removes barriers related to software installation and access. We performed an overall evaluation of this teaching framework in different contexts. This inclusive, sustainable approach provides widespread access to essential bioinformatics skills for life science students and professionals alike.

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Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies

Correction: The Burr distribution as a model for the delay between key events in an individual’s infection history

Subunit-specific behavioral modulation of sensory tuning in the visual cortex

Exploring neural manifolds across a wide range of intrinsic dimensions

Correction: A computational framework to study EGFR signaling distribution in egg chambers during dynamic interactions between soma and germline

A Bayesian modelling framework for estimating tick-borne pathogen transmission dynamics at the host-tick interface

Training biologists in Unix command-line skills: From curriculum to interactive online tutorials

On the role of L-type Ca²⁺ and BK channels in a biophysical model of cartwheel interneurons