PLOS Computational Biology: New Articles

Multi-ACPNet: A multi-scale sequence-structure feature fusion framework for anticancer peptide identification and functional prediction

2026-03-10T14:00:00Z

by Lu Meng, Lijun Zhou

Anticancer peptides (ACPs) have emerged as promising therapeutic candidates for cancer treatment due to their high efficacy and low propensity for inducing drug resistance. However, existing ACP identification methods primarily rely on peptide sequence features while neglecting spatial structural characteristics. Moreover, few approaches can simultaneously predict the functional activity of ACPs. To address these limitations, this study proposes Multi-ACPNet, a novel dual-function predictor capable of both ACP identification and activity type classification. This model innovatively integrates sequence and structural features through a multi-stage framework. It employs a hybrid Bidirectional Long Short-Term Memory (BiLSTM) and causal convolutional network to capture both long-range dependencies and local sequence patterns, followed by a multi-scale Graph Convolutional Network (GCN) that dynamically fuses local and long-range structural dependencies using residual connections and adaptive weighting. Experimental results demonstrate that Multi-ACPNet achieves outstanding performance, with Accuracy of 0.8140, 0.9536, and 0.8770 on three benchmark datasets for ACP identification. For functional prediction, it attains an AUC of 0.9033, F1-score of 0.8472, and Hamming loss of 0.1303, significantly outperforming state-of-the-art predictors.

Coevolution of host resistance and pathogen exploitation in a propagule-mediated infection model

2026-03-10T14:00:00Z

by Prerna Singh, Justin Sheen, Chadi M. Saad-Roy, Michael Z. Levy, C. Jessica E. Metcalf

Host populations often face infection risk from pathogens that can persist in the environment as free-living propagules. We develop a population-level model to understand how host resistance - defined as reduced susceptibility to infection - evolves in response to the exploitation strategy of a pathogen where transmission occurs exclusively via environmental propagules. Using an adaptive dynamics framework, we analyze how the coevolution of host resistance and pathogen exploitation strategy unfolds under the following fitness costs: reduced survival associated with investment in resistance reflected by additional background mortality for the host; and reduced average lifespan represented by increased infected host mortality for the pathogen. Calculating individual host and pathogen invasion fitness expressions using standard invasion analysis, we track how stable levels of investment in host resistance vary across different infection scenarios. We found that costly resistance is disfavoured when pathogen encounters are excessively high, with maximal resistance selected at intermediate levels of transmission. Coevolutionary feedbacks between host resistance and pathogen exploitation can lead to diverse outcomes, including stable evolutionarily singular strategies and, under weakly accelerating costs, evolutionary branching that generates coexistence in the resistance trait. We further quantify how coevolution shapes the equilibrium density of free propagules, revealing conditions under which coevolution suppresses or amplifies pathogen prevalence in comparison to non-evolving scenarios. Overall, our model framework built on survival-based costs offers testable predictions for environmentally transmitted host-pathogen systems.

Inverse game theory characterizes frequency-dependent selection driven by karyotypic diversity in triple negative breast cancer

2026-03-10T14:00:00Z

by Thomas Veith, Richard J. Beck, Joel S. Brown, Noemi Andor

Chromosomal instability, characterized by pervasive copy number alterations (CNAs), significantly contributes to cancer progression and therapeutic resistance. CNAs drive intratumoral genetic heterogeneity, creating distinct subpopulations whose interactions shape tumor evolution through frequency-dependent selection. Here, we introduce, ECO-K (Ecological-Karyotypes), an inverse game theory framework that quantifies frequency-dependent interaction coefficients among karyotypically defined subpopulations under the assumption that their fitness is frequency-dependent. Applying this approach to serially-passaged, triple-negative breast cancer cell lines and patient-derived xenografts (PDXs), we estimated interaction matrices consistent with the observed time-series dynamics. In one PDX lineage, the inferred matrices consistently assigned large interaction coefficients to a subpopulation characterized by chromosome 1 loss and chromosome 14p gain, suggesting it may act as an ecological hub within the frequency-dependent model. Our framework provides testable predictions of intratumoral ecological dynamics, highlighting opportunities to strategically target key subpopulations to disrupt tumor evolution.

The integrated information Φ of an integrate and fire network

2026-03-09T14:00:00Z

by Miłosz Danilczuk, Marek Pokropski, Piotr Suffczynski

Integrated Information Theory is a theoretical framework proposing that consciousness is a fundamental property of systems capable of integrating information. To bridge the gap between the theoretical concept and the practical use in actual neurobiological systems, we have applied the Integrated Information Theory approach to a simulated network of integrate and fire neurons (IAF). The primary contribution of this study is several empirical findings. Our analysis shows that such a network can possess a non-zero Φ value under certain conditions and parameter settings. Additionally, our research indicates that the complexity of the network’s dynamics doesn’t necessarily correlate with its Φ value. On the other hand, the quantity of integrated information within the network appears to grow with the IAF neurons’ time constant, which reflects their integrative capacity. Furthermore, our examination of the integrate and fire network with internal random fluctuations demonstrates that the integrated information measure, as defined in IIT version 3.0, is not resilient to noise.

Burst firing creates an attractor in synaptic weight dynamics

2026-03-09T14:00:00Z

by Kathleen Jacquerie, Danil Tyulmankov, Pierre Sacré, Guillaume Drion

Neural circuits often alternate between tonic and burst firing, two distinct activity regimes that reflect changes in excitability and neuromodulatory state. While tonic firing produces asynchronous spiking driven by diverse external inputs, collective burst firing consists of rapid clusters of spikes followed by a period of silence, happening synchronously within the network. Synaptic plasticity has typically been studied only in either one of these regimes, leaving unclear how their distinct plasticity dynamics can be combined when circuits alternate between regimes. Here, we use a conductance-based network model endowed with calcium-based or spike-timing–based plasticity rules to examine how synaptic weights evolve across tonic and burst firing regimes. During tonic firing, synaptic weights are driven by the statistics of external inputs, producing a broad distribution across the network. In contrast, during collective burst firing, weights converge to a narrow region in weight space: a burst-induced attractor. We derive the location of this attractor analytically in terms of plasticity parameters and activity statistics, and confirm its emergence across diverse plasticity rules. The attractor reflects the synchronization of plasticity-driving signals during bursts, which homogenizes synaptic dynamics and forces convergence toward shared fixed points. We further show that neuromodulation and synaptic tagging can shift or split the burst-induced attractor, stabilizing selected synapses while weakening others. Together, these results identify burst-induced attractors as a robust emergent property of collective bursting. Alternation between tonic and burst firing provides a biologically plausible context in which heterogeneous, input-driven synaptic configurations formed during tonic activity can be selectively consolidated or down-selected by the burst-induced attractor during subsequent bursts. By showing how they can be analytically predicted and experimentally modulated, our work provides a general computational framework linking firing state transitions, synaptic plasticity, and memory organization.

Computing the effects of excitatory-inhibitory balance on neuronal input-output properties

2026-03-09T14:00:00Z

by Alex D. Reyes

In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neurons. These features emerge from the interaction between excitatory (E) and inhibitory (I) neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. Although these phenomena have been extensively investigated experimentally and theoretically, the principles governing how E and I inputs are integrated remain unclear. Here, probabilistic rules are derived to describe how neurons in feedforward inhibitory circuits combine these inputs to generate stimulus-evoked responses. This simple model is broadly applicable, capturing a wide range of response features that would otherwise require multiple separate models, and offers insights into the cellular and network mechanisms influencing the input-output properties of neurons, gain modulation, and the emergence of diverse temporal firing patterns.

How the dynamic interplay of cortico-basal ganglia-thalamic pathways shapes the time course of deliberation and commitment

2026-03-09T14:00:00Z

by Zhuojun Yu, Timothy Verstynen, Jonathan E. Rubin

Although the cortico-basal ganglia-thalamic (CBGT) network is identified as a central circuit for decision-making, the dynamic interplay of multiple control pathways within this network in shaping decision trajectories remains poorly understood. Here we develop and apply a novel computational framework—CLAW (Circuit Logic Assessed via Walks)—for tracing the instantaneous flow of neural activity as it progresses through CBGT networks engaged in a virtual decision-making task. Our CLAW analysis reveals that the complex dynamics of network activity is functionally dissectible into two critical phases: deliberation and commitment. These two phases are governed by distinct contributions of underlying CBGT pathways, with indirect and pallidostriatal pathways influencing deliberation, while the direct pathway drives action commitment. We translate CBGT dynamics into the evolution of decision-related policies, based on three previously identified control ensembles (responsiveness, pliancy, and choice) that encapsulate the relationship between CBGT activity and the evidence accumulation process. Our results demonstrate two contrasting strategies for decision-making. Fast decisions, with direct pathway dominance, feature an early response in both boundary height and drift rate, leading to a rapid collapse of decision boundaries and a clear directional bias. In contrast, slow decisions, driven by indirect and pallidostriatal pathway dominance, involve delayed changes in both decision policy parameters, allowing for an extended period of deliberation before commitment to an action. These analyses provide important insights into how the CBGT circuitry can be tuned to adopt various decision strategies and how the decision-making process unfolds within each regime.

A reinforcement learning and sequential sampling model constrained by gaze data

2026-03-06T14:00:00Z

by William M. Hayes, Melanie J. Touchard

Reinforcement learning models can be combined with sequential sampling models to fit choice-RT data. The combined models, known as RL-SSMs, explain a wide range of choice-RT patterns in repeated decision tasks. The present study shows how constraining an RL-SSM with eye gaze data can further enhance its predictive ability. Our model allows learned option values and relative gaze to jointly influence the accumulation of evidence prior to choice. We evaluate the model on data from two eye-tracking experiments (total N = 133) and test several variants of the model that assume different mechanisms for integrating values and gaze at the decision stage. Further, we show that it captures a variety of empirical effects, including gaze biases on choice and response time, as well as individual differences in absolute versus relative valuation. The model can be used to understand how learned option values interact with visual attention to influence choice, joining together two major (but mostly separate) modeling traditions.

An integrative analysis reveals the mechanism of plastic stabilizers inducing breast cancer

2026-03-06T14:00:00Z

by Xingfa Huo, Xueqin Duan, Xiaojuan Huang, Linyuan Xue, Lantao Zhao, Yufeng Li, Xiaochun Zhang, Na Zhou

Plastic stabilizers (PSs) are chemical additives that are widely used to inhibit the degradation of plastics. However, their safety concerns and potential carcinogenic risks remain unclear. This study employed network toxicology strategies to elucidate the potential toxic effects and underlying molecular mechanisms of representative PSs, including 2,6-di-tert-butylphenol (2,6-DTB), tert-butylhydroquinone (TBHQ), and 2-(2H-benzotriazol-2-yl)-4,6-di-tert-pentylphenol (UV-328) in breast cancer (BC). Herein, we identified 69 potential genes related to PSs exposure and BC, and optimized five core targets: GSK3B, MAPK14, PARP1, PIM1, and TRDMT1, through subsequent LASSO and SVM algorithms. Based on these core genes, we constructed risk score and nomogram models, both of which revealed that high expression of these five core genes predicts poor prognosis in BC patients. Additionally, molecular docking and dynamic simulations indicated high-affinity interactions between PSs and these core targets (binding energies < -5 kcal/mol). Further correlation analysis with prediction analysis of microarray 50 (PAM50) revealed increased expression of all core genes in the basal-like subtype, especially PIM1 and TRDMT1, which also exhibited the highest risk scores. In vitro, PSs transcriptionally upregulated MAPK14, PIM1, and TRDMT1, with STAT3 mediating their transcription. Importantly, cell counting kit-8 and wound healing assays demonstrated that PSs promote BC cell proliferation and migration. Our research re-evaluates the carcinogenic risks of plastic stabilizers and suggests that PSs may enhance breast cancer progression via targets such as MAPK14, PIM1, and TRDMT1. This study introduces a new approach for evaluating the safety of plastic additives and offers novel insights into the toxicological effects of PSs.

Contrastive learning for passive acoustic monitoring: A framework for sound source discovery and cross-site comparison in marine soundscapes

2026-03-06T14:00:00Z

by Richard Acs, Ali Ibrahim, Hanqi Zhuang, Laurent M. Chérubin

Passive acoustic monitoring (PAM) is a powerful tool for studying marine biodiversity, but large-scale analysis of underwater recordings is constrained by noise, overlapping signals, and limited labeled data. Here, we present a scalable, unsupervised contrastive learning framework for marine soundscapes. Using a large PAM dataset spanning multiple biogeographies, we show that the proposed approach organizes recordings into clusters with well-defined internal structure, as assessed using intrinsic clustering metrics and within-cluster similarity. The resulting clusters reveal recurring acoustic patterns that correspond to broad sound-source categories, including biological sounds such as fish calls and choruses, and anthropogenic sounds such as vessel noise, without explicitly enforcing these distinctions during training. Compared with established approaches, including cepstral features, variational autoencoders, and supervised pipelines, the proposed framework produces embeddings that support more compact and stable unsupervised clustering while preserving fine-scale acoustic variation beyond predefined species labels. By learning a shared representation across recordings from multiple sites and years, we examine the reproducibility of acoustic patterns across locations and identify both site-shared and site-specific sound signatures. Although the method is not designed to recover coarse species labels, it enables label-efficient analysis by reducing reliance on manual annotation and supporting exploratory characterization of complex marine soundscapes. Together, these results highlight multi-positive contrastive learning with a teacher network and acoustically informed augmentations as an effective strategy for scalable, discovery-driven analysis of passive acoustic monitoring data.

Morphological determinants of glycosylation efficiency in Golgi cisternae

2026-03-06T14:00:00Z

by Christopher K. Revell, Martin Lowe, Nicola L. Stevenson, Oliver E. Jensen

The Golgi apparatus has an intricate spatial structure characterized by flattened membrane-bound compartments, known as cisternae. Cisternae house integral membrane enzymes that catalyse glycosylation, the addition of polymeric sugars to protein cargo, which is important for the trafficking and function of the products. The unusual and specific shape of Golgi cisternae is highly conserved across eukaryotic cells, suggesting significant influence in the correct functioning of the Golgi. Motivated by experimental evidence that disruption to Golgi morphology can lead to observable changes in secreted cargo mass distribution, we develop and analyse a mathematical model of polymerisation in a cisterna that combines chemical kinetics, spatial diffusion and adsorption and desorption between lumen and membrane. Exploiting the slender geometry, we derive a non-local non-linear advection-diffusion equation that predicts secreted cargo mass distribution as a function of cisternal shape. The model predicts a maximum cisternal thickness for which successful glycosylation is possible, demonstrates the existence of an optimal thickness for most efficient glycosylation, and suggests how kinetic and geometric factors may combine to promote or disrupt polymer production.

Reconstructing the incidence rate and immune fraction of the population via a single snapshot survey: A case study of COVID-19 in Japan

2026-03-06T14:00:00Z

by Yuta Okada, Hiroshi Nishiura

While the global health burden of COVID-19 continues, multifaceted epidemiological surveillance is required to monitor the epidemic’s dynamics and its population-wide risk. By collecting information that is used in conventional vaccine effectiveness studies through questionnaire surveys, we proposed a simple framework using a population-wide snapshot questionnaire survey to estimate the incidence and protective effect of immunity by natural infection or vaccination against the SARS-CoV-2 JN.1 variant. Our results revealed that in Japan in February 2024, the personal risk of diagnosed infection was substantially higher in younger adults and risk was heterogenous across prefectures. Diabetes mellitus (relative risk 1.8; 95% credible interval [CrI] 1.1, 2.9), neoplastic disorders (5.2; 95% CrI 3.1, 8.6), immunological suppression (2.6; 95% CrI 1.3, 4.6), respiratory diseases (2.2; 95% CrI 1.4, 3.3), and cardiovascular disease (2.3; 95% CrI 1.3, 3.9) were risk factors for diagnosed infection. The highest peak protection after infection was after exposure to pre-XBB.1.5 Omicron variants (52.0%; 95% CrI 33.2, 68.7), whereas the XBB.1.5 monovalent vaccine provided the highest protection (45.1%; 95% CrI 37.8, 52.7) among three vaccine types. Notably, the peak protection of the bivalent Wuhan + Omicron BA.1/5 vaccine was substantially lower than other vaccines (28.7; 95% CrI 17.3, 40.6). By statistically matching the respondent cohort to the 2020 population census, we revealed that the national COVID-19 incidence rate in February 2024 by age group was highest (4.73%; 95% CrI 4.17, 5.38) and lowest (1.19%; 95% CrI 0.94, 1.47) among those aged 20–29 years and 60–69 years, respectively. The force of infection measured by diagnosed infection was high and more heterogeneous in younger groups, whereas younger populations were more concentrated than older populations in low-protection regions. Our framework revealed biological and epidemiological insights into protection and risk of diagnosed infection from past immunizing events and personal attributes during the JN.1-dominant period. Moreover, we proposed a framework for the rapid evaluation of epidemiological dynamics whose application is not limited to COVID-19.

ConNIS and labeling instability: New statistical methods for improving the detection of essential genes in TraDIS libraries

2026-03-06T14:00:00Z

by Moritz Hanke, Theresa Harten, Ronja Foraita

The identification of essential genes in Transposon Directed Insertion Site Sequencing (TraDIS) data relies on the assumption that transposon insertions occur randomly in non-essential regions, leaving essential genes largely insertion-free. While intragenic insertion-free sequences have been considered as a reliable indicator for gene essentiality, so far, no exact probability distribution for these sequences has been proposed. Further, many methods require setting thresholds or parameter values a priori without providing any statistical basis, limiting the comparability of results. Here, we introduce Consecutive Non-Insertion Sites (ConNIS), a novel method for gene essentiality determination. ConNIS provides an analytic solution for the probability of observing insertion-free sequences within genes of given length and considers variation in insertion density across the genome. Based on an extensive simulation study and different real world scenarios, ConNIS was found to be superior to prevalent state-of-the-art methods, particularly when libraries had only a low or medium insertion density. In addition, our results showed that the precision of existing methods can be improved by incorporating a simple weighting factor for the genome-wide insertion density. To set methodically embedded parameter and threshold values of TraDIS methods a subsample based instability criterion was developed. Application of this criterion in real and synthetic data settings demonstrated its effectiveness in selecting well-suited parameter/threshold values across methods. A ready-to-use R package and an interactive web application are provided to facilitate application and reproducibility.

Unique ecology of co-occurring functionally and phylogenetically undescribed species in the infant oral microbiome

2026-03-05T14:00:00Z

by Nicholas Pucci, Amke Marije Kaan, Joanne Ujčič-Voortman, Arnoud P. Verhoeff, Egija Zaura, Daniel R. Mende

Early-life oral microbiome development is a complex community assembly process that influences long-term health outcomes. Nevertheless, microbial functions and interactions driving these ecological processes remain poorly understood. In this study, we analyze oral microbiomes from a longitudinal cohort of 24 mother-infant dyads at 1 and 6 months postpartum using shotgun metagenomics. We identify two previously undescribed Streptococcus and Rothia species to be among the most prevalent, abundant and strongly co-occurring members of the oral microbiome of six-month-old infants. By leveraging metagenome-assembled genomes (MAGs) and genome-scale metabolic models (GEMS) we reveal their genomic and functional characteristics relative to other infant-associated species and predict their metabolic interactions within a network of co-occurring oral taxa. Our findings highlight unique functional features, including genes encoding adhesins and carbohydrate-active enzymes (CAZymes). Metabolic modeling identified potential exchange of key amino acids, particularly ornithine and lysine, between these species, suggesting metabolic cross-feeding interactions that may explain their co-abundance across infant oral microbiomes. Overall, this study provides key insights into the functional adaptations and microbial interactions shaping early colonization in the oral cavity, providing testable hypotheses for future experimental validation.

Topological metrics as evolutionary and dynamical descriptors of conformational landscapes within protein families

2026-03-04T14:00:00Z

by Nikhil Ramesh, S. Banu Ozkan, Eleni Panagiotou

Identifying the key order parameters that connect a protein‘s native structure to its dynamical and evolutionary behavior remains a central challenge. We introduce topological and geometrical metrics—specifically, writhe and Local Topological Energy (LTE)—to investigate these connections. Applying these tools to both present-day and ancestral forms of thioredoxin and β-lactamase, we show that LTE strongly correlates with established dynamical measures such as the Dynamical Flexibility Index (DFI). Remarkably, LTE distributions also track the evolutionary trajectories of these proteins, suggesting that the topological geometry of the native state encodes key aspects of both dynamics and evolution. Through molecular dynamics simulations, we further reveal critical shifts in the topological landscape of proteins, providing a molecular mechanism by which functional evolution proceeds via alterations in conformational dynamics. Extending our analysis to over 100 proteins, we provide the first compelling evidence that topological descriptors derived from static structures can reliably predict dynamical behavior. In general, our findings demonstrate that simple geometrical metrics capture essential features of protein conformational landscapes, offering a powerful new approach to bridging protein structure, dynamics, and evolution.

Quantifying the spatiotemporal dynamics of the first two epidemic waves of SARS-CoV-2 infections in the United States

2026-03-04T14:00:00Z

by Rafael Lopes, Yu Lan, Melanie H. Chitwood, Fayette Klaassen, Joshua A. Salomon, Nicolas A. Menzies, Joshua L. Warren, Nathan D. Grubaugh, Ted Cohen, Nicole A. Swartwood

SARS-CoV-2 infection rates displayed strikingly organized patterns of temporal and spatial spread as new variants were introduced and subsequently transmitted within the United States. While these spatio-temporal “waves” of infection have been described previously, attempts to quantify the speed and extent of these waves have been limited. Here, we estimate and compare the wavefront speed and spatial expansion of the first two major infection waves in the United States, illustrating these dynamics through detailed visualizations. Our findings reveal that the origins of these waves coincide with large gatherings and the relaxation of masking mandates. Notably, we found that the second wave spread more rapidly than the first, possibly driven by multiple introduction events. These analyses highlight regional heterogeneity in epidemic dynamics and underscore the importance of localized public health measures in mitigating ongoing outbreaks.

EEG-Pype: An accessible MNE-Python pipeline with graphical user interface for preprocessing and analysis of resting-state electroencephalography data

2026-03-02T14:00:00Z

by D. Yorben Lodema, Herman J. van Dellen, Willem de Haan, Margot van Hest, Arjan Hillebrand, Edwin van Dellen

Processing of electroencephalography (EEG) data requires multiple steps to remove noise and artifacts and select good-quality data. While powerful open-source toolboxes like MNE-Python exist, their command-line nature can pose a barrier for researchers without programming experience. Here, we present EEG-Pype, an open-source (Apache-2.0 licensed) graphical user interface application using MNE-Python functions. EEG-Pype provides an intuitive workflow tailored for preprocessing of resting-state EEG data, including frequency band filtering, independent component analysis and atlas-based beamforming for source-level analysis. The application supports several common raw EEG input file formats and guides users through a comprehensive pipeline focused on manual bad channel and epoch selection. Manual steps are streamlined using MNE-Python’s interactive plots, resulting in a user-friendly experience. Configuration saving and loading allows for batch (re)runs, while a separate log is also saved, improving reproducibility and documentation. Output can be saved after filtering in canonical frequency bands, ready for further analysis. EEG-Pype includes a module for calculating quantitative EEG measures on preprocessed data, including spectral, functional connectivity and network analysis metrics. This software aims to lower the entry barrier for standardized EEG preprocessing, promoting reproducible research practices among neuroscientists and clinicians without requiring programming knowledge. EEG-Pype can be downloaded from: https://github.com/yorbenlodema/EEG-Pype and is not dependent on a specific operating system.

Combining visual motion and luminance features to enhance the detection of small moving objects in a bioinspired model

2026-03-02T14:00:00Z

by Shuai Li, Aike Guo, Yizheng Wang, Liang Li, Gang Wang, Zhihua Wu

Flying insects demonstrate exceptional proficiency in detecting and pursuing conspecifics and prey within a cluttered environment, inspiring the development of computational models for small object detection. While existing bioinspired models are dedicated to resolving small moving instead of stationary object detection, few studies have systematically explored the role of visual motion in detection. Here, we developed a fly-inspired model on the basis of the hypothesis that combining visual motion features and luminance features is critical for small moving object detection. We thoroughly investigated the effect of feature combination under diverse stimulus conditions. Simulations indicated that the model exhibited hyperacute object detection, a capability not generally believed to emerge on the basis of motion detection. When tested with a moving background in realistic scenarios, the model demonstrated enhanced efficiency and robustness relative to models relying solely on luminance features. This enhancement was independent of whether visual motion was extracted by two- or three-arm motion detectors. The results suggested that small object detectors within the visual systems of flying insects could be optimally tuned to utilize the limited features inherent to tiny objects.

Assessment of dispersion metrics for estimating single-cell transcriptional variability

2026-03-02T14:00:00Z

by Tina Chen, Laurie A. Boyer, Divyansh Agarwal

Single-cell RNA sequencing data enables analysis of transcript levels of single cells across different cell types and conditions. Recent work has highlighted the value of measuring gene-specific transcriptional variability, or noise, within a genetically identical population of cells in addition to mean expression, given that these differences contribute to biological processes including development and disease. However, measuring transcriptional noise remains a challenge. Here, we systematically compared statistical methods by simulating single-cell data by varying both dispersion and count size to assess the relative responsiveness to noise of several commonly used statistical metrics: the Gini index, variance-to-mean ratio, variance, coefficient of variance (CV), CV², and Shannon entropy. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion and is independent of dataset size. In contrast, the Gini index displayed paradoxical behavior in that it increases as dispersion decreases, and Shannon entropy was not scale-invariant. Next, we applied the variance-to-mean ratio (Fano factor) to measure transcriptional variability in single-cell datasets representing different complex systems and cross-platform measurements. Our data show that many genes display transcriptional variability within the same cell type, and that while variation does not correlate with gene characteristics such as transcript level, promoter GC content, or evolutionary gene age, variable genes are often correlated with specific biological processes. Notably, most genes and pathways with highest transcriptional variability as identified by the Fano factor were largely independent of differentially expressed genes and have also been implicated in biological processes related to the system. Thus, our data highlight that choice and application of appropriate models for measuring transcriptional variation in scRNA-seq data can reveal biologically relevant information beyond what is observed from mean expression alone.

Putting BASIL in a BLT: A Bayesian filtering method for estimating the fitness effects of nascent adaptive mutations

2026-02-27T14:00:00Z

by Huan-Yu Kuo, Sergey Kryazhimskiy

The distribution of fitness effects (DFE) of new beneficial mutations is a key quantity that dictates the dynamics of adaptation. The barcode lineage tracking (BLT) approach is an important advance toward measuring DFEs. BLT experiments enable researchers to track the frequencies of ~10⁵ barcoded lineages in large microbial populations and detect up to thousands of nascent beneficial mutations in a single experiment. However, reliably identifying adapted lineages and estimating the fitness effects of driver mutations remains a challenge because lineage dynamics are subject to demographic and measurement noise and competition with other lineages. We show that the commonly used Levy-Blundell method for analyzing BLT data and its improved version FitMut2 can produce biased fitness estimates, particularly if selection is strong. To address this problem, we develop a new method called BASIL (BAyesian Selection Inference for Lineage tracking data), which dynamically updates the belief distribution of each lineage’s fitness and size based on the number of barcode reads. We calibrate BASIL’s model of noise with new experimental data and find that noise variance scales non-linearly with lineage abundance. We test how BASIL and FitMut2 perform on simulated data and on down-sampled data from the original BLT data by Levy et al and find that BASIL is both more robust and more accurate than FitMut2. Our work paves the way for a systematic inference of the distribution of fitness effects of new beneficial mutations from BLT experiments in a variety of scenarios.

Combining seasonal malaria chemoprevention with novel therapeutics for malaria prevention: a mathematical modelling study

2026-02-26T14:00:00Z

by Lydia Braunack-Mayer, Josephine Malinga, Narimane Nekkab, Sherrie L. Kelly, Jörg J. Möhrle, Melissa A. Penny

Vaccines, monoclonal antibodies, and long-acting injectables are being developed to prevent Plasmodium falciparum malaria. These therapeutics may target multiple stages of the parasite life cycle; evidence is needed to articulate their benefits with chemoprevention and prioritise candidates for clinical development. We used an individual-based malaria transmission model to estimate the health impact of combining new therapeutics with seasonal malaria chemoprevention (SMC). Our modelling framework used emulator-based methods with models of pre-liver and blood stage therapeutic dynamics. We evaluated the benefit of combining therapeutics with SMC in children under five by estimating reductions in the cumulative incidence of uncomplicated and severe malaria, relative to SMC or the new therapeutic alone, during and five years after deployment. Results showed that new therapeutics may require extended pre-liver stage duration or multi-stage activity to combine with SMC. For three SMC cycles in a high transmission setting, a pre-liver stage therapeutic with partial initial efficacy (>50%) required a protection half-life >230 days to reduce cumulative severe cases by >23% during interventions, and >5% five years after deployment stopped. Longer protection was needed when combined with four or five SMC cycles. And, combining SMC with a multi-stage therapeutic increased public health impact both during and after deployment. These results indicate that combining SMC with malaria therapeutics active against multiple stages of the parasite life cycle can improve the effectiveness of SMC, and highlight the need for clinical development to prioritise multi-stage therapeutics for improved malaria prevention in children.

PON-Del predictor for sequence retaining protein deletions

2026-02-25T14:00:00Z

by Haoyang Zhang, Muhammad Kabir, Mauno Vihinen

Protein deletions are frequent among both disease-causing and tolerated variants. Several mechanisms at the DNA, RNA and protein levels can lead to deletions. Many deletions are misclassified in the literature and databases, especially when the mRNA is degraded by the cellular quality-control mechanism. We developed a novel predictor for sequence retaining protein deletions, i.e., variants that do not alter the sequence downstream of the deletion site. We collected an extensive dataset of verified protein deletions, each described by a comprehensive set of context, content, position, and gene-based features. We evaluated both statistical and deep learning algorithms and selected a gradient boosting–based approach to develop the PON-Del predictor for short, 1–10 amino acid, sequence-retaining deletions. Variants are typically classified into two categories: either pathogenic or benign. However, there is always a third class of variants: variants of uncertain significance (VUSs), which have been ignored by all previous methods. PON-Del is the first deletion interpretation method that includes VUSs. It provides two outputs, binary and three-state prediction with VUSs. The performance of PON-Del was superior to that of previous methods. The tool is freely available at https://structure.bmc.lu.se/pon_del/.

Viral evolution during primary infection in immunocompromised hosts

2026-02-25T14:00:00Z

by Morgan Craig, Xiaoyan Deng, David V. McLeod

The immune response to viral infection is a delicate balance. By perturbing this balance, immunodeficiencies are expected to influence within-host viral evolution. Indeed, the presence of immunocompromised hosts has been argued to be a source of novel viral variants in some infectious diseases, including SARS-CoV-2. However, these arguments rest upon between-host models and so the role of immunodeficiencies on within-host evolution in primary infections is poorly understood. Using a mechanistic immunological model, here we consider how different immunodeficiencies shape the orchestration of the immune response during primary infection. We study how this alters the viral fitness landscape, thus speeding and slowing viral evolution. We show that during acute infections, while immunodeficiencies in neutrophils and interferon initially speed viral evolution, by the time the infection is cleared, mutations are at lower frequencies than in immunocompetent hosts. In persistent infections, we show that while T cell deficiencies slow viral evolution, interleukin-6 and macrophage deficiencies speed viral evolution. Finally, we show that positive epistatic interactions arising due to the immunological response will accelerate the evolution of viral mutations affecting the ability of virions to evade different aspects of the immune response and to enter host cells.

Hemodynamic impact of acute liver injury on cardiac function: An in silico study via a closed-loop cardiovascular model

2026-02-24T14:00:00Z

by Jiyang Zhang, Zhongyou Li, Lin Feng, Jialu Zhang, Taoping Bai, Wentao Jiang

Acute liver injury and cardiovascular disease interact, forming a mutually exacerbating vicious cycle. However, the dynamic influence of hepatic vascular impedance on cardiac function has not been systematically elucidated. To address this gap, a closed-loop hemodynamic model based on lumped parameters was developed, encompassing the heart, liver, and the systemic arterial and venous circulation. This model was used to analyze how alterations in hepatic vascular impedance influence cardiac function and to provide a theoretical foundation for understanding liver–heart comorbidities. Healthy subjects served as the control group, while acute liver injury was simulated by proportionally increasing hepatic microvascular resistance. Changes in cardiovascular hemodynamic parameters were then systematically compared across conditions. As the severity of acute liver injury increases, the peak aortic flow and total cardiac output significantly decrease, with stroke volume reduced by approximately 17%. The left ventricular end-diastolic volume and stroke work are markedly diminished. Effective arterial elastance increases by about 20.7%, and the left ventricular ejection fraction decreases by approximately 4%. Furthermore, the change in hepatic arterial flow is considerably greater than that in portal vein flow. This closed-loop hemodynamic model reveals that acute liver injury leads to a reduction in preload and an increase in afterload, thereby causing abnormalities in both systolic and diastolic cardiac function. Global sensitivity analysis demonstrated that changes in presinusoidal vascular resistance serve as the major contributors to the resulting cardiac dysfunction. These findings provide a theoretical basis for understanding the interplay between liver and heart, and offer a feasible method for pre-assessing cardiovascular risk in patients prior to liver resection or transplantation.

How sleeping minds decide: State-specific reconfigurations of lexical decision-making

2026-02-23T14:00:00Z

by Tao Xia, Chuan-Peng Hu, Başak Türker, Esteban Munoz Musat, Lionel Naccache, Isabelle Arnulf, Delphine Oudiette, Xiaoqing Hu

Sleep has traditionally been conceptualized as a state of cognitive disconnection, yet emerging evidence indicates that decision-making capacities persist across sleep stages. Here, we elucidate the computational mechanisms underlying real-time lexical decision-making during polysomnographically-verified sleep, using facial electromyography and hierarchical drift diffusion modeling in both healthy individuals and participants with narcolepsy. We found that lexical decision-making was preserved during N1 and lucid REM sleep, but relied on distinct computational strategies: in N1 sleep, both enhanced sensory-motor processing and increased evidence accumulation supported decisions about words, whereas in lucid REM sleep, lexical decisions were driven exclusively by evidence accumulation processes. Cross-state comparisons revealed two fundamental principles: (1) Selective preservation—during N1 sleep, lexical decisions for words were maintained while those for pseudowords were selectively impaired, indicating that cognitive resources during sleep are preferentially allocated to meaningful stimuli; (2) Parallel strategic adaptations—during lucid REM sleep, participants increased their decision thresholds, requiring more evidence before responding, which helped maintain accuracy even though the efficiency of evidence accumulation was reduced. Our findings demonstrate that, rather than a passive decline, sleep involves dynamic and state-specific reconfiguration of the computational mechanisms underlying decision-making, with important implications for understanding consciousness and cognitive flexibility.

Ten simple rules for building a collaborative coding culture

2026-02-23T14:00:00Z

by Austin L. Zuckerman, Sarah Faber, Kelly Shen, Anthony R. McIntosh, Ashley L. Juavinett

Ten simple rules for coordinating a large digital health project: Perspectives from EU and implications for global contexts

2026-02-23T14:00:00Z

by Lucia Sacchi, Blaž Zupan, Silvana Quaglini

Coordinating a large-scale digital health project requires a unique mix of scientific leadership, administrative skill, and human sensitivity. Drawing from our experience leading CAPABLE, a European Horizon 2020 project aimed at improving the quality of life of cancer patients through AI and telemedicine, we present ten practical rules for navigating the complex landscape of multi-partner biomedical research. These rules address challenges such as building balanced consortia, managing timelines and regulatory requirements, ensuring cultural alignment, and promoting long-term impact through dissemination and exploitation. The paper specifically addresses international research projects at the intersection of healthcare and IT and their peculiar challenges, typically connected to the interplay of different actors such as academics, healthcare personnel, and industry partners located in different countries, each from diverse backgrounds and different working practices. Our goal is to provide researchers and project coordinators with concrete guidance to increase the likelihood of success in future large digital health initiatives.

A novel transformer-based platform for the prediction and design of biosynthetic gene clusters for (un)natural products

2026-02-23T14:00:00Z

by Tomoki Kawano, Taro Shiraishi, Tomohisa Kuzuyama, Maiko Umemura

Biosynthetic gene clusters (BGCs), comprising sets of functionally related genes responsible for synthesizing complex natural products, are a rich source of bioactive compounds with pharmaceutical potential. Here, we present a transformer-based framework that models functional domains as linguistic units to capture and predict their positional relationships within genomes. Using a RoBERTa architecture, we trained models on four progressively broader datasets: bacterial BGCs, Actinomycetes genomes, bacterial genomes, and bacterial plus fungal genomes. Evaluation using 2,492 experimentally-validated BGCs from the MIBiG database showed that more than 50% of true domains were ranked first and over 75% within the top 10 candidates. Our models also achieved classification accuracies exceeding 70% for major compound classes including polyketides (PKs) and terpenes. To explore model-guided BGC design, we compared predictions from the BGC-trained and genome-trained models using the BGC for the bacterial diterpenoid cyclooctatin as a case study. The genome-trained model uniquely predicted several domains absent from both the original BGC and the prediction by the BGC-trained model. Heterologous expression of one of those predicted domains in Streptomyces albus, together with the biosynthetic genes for cyclooctatin, yielded an unknown cyclooctatin derivative. This framework not only provides a novel BGC prediction method using machine learning but also facilitates rational design of artificial BGCs. Future integration of transcriptomic, protein structural, and phylogenetic data will enhance the models’ predictive and generative capabilities, supporting accelerated discovery and engineering of natural products.

CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction

2026-02-20T14:00:00Z

by Shumei Zhang, Yicheng Lu, Peixian Li, Junxuan Wu, Guohua Wang, Wen Yang

In cancer research, identifying cancer subtypes and evaluating prognosis are crucial for personalized diagnosis and treatment of cancer. With the advancement of high-throughput sequencing technologies, multi-omics data has become essential for cancer classification and prognostic analysis. By integrating deep learning techniques, it is possible to more accurately identify cancer subtypes, providing a robust basis for personalized treatment of cancer patients. In this study, we propose a convolutional autoencoder prognostic model incorporating a channel attention mechanism (CA-CAE). The model utilizes multi-omics data to predict survival-associated cancer subtypes and identify prognostic genes. We applied CA-CAE to multiple cancer types, successfully identifying subtypes in 15 distinct cancer types and revealing significant survival differences among these subtypes. Moreover, compared to traditional statistical methods and other deep learning approaches, CA-CAE demonstrated superior performance in predicting survival outcomes.

Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain

2026-02-20T14:00:00Z

by Diego Fasoli, Ludovico Coletta, Daniel Gutierrez-Barragan, Silvia Gini, Alessandro Gozzi, Stefano Panzeri

Resting state fMRI signals in mammals exhibit rich dynamics on a fast, frame-by-frame timescale of seconds, including the robust emergence of recurring fMRI co-activation patterns (CAPs). To understand how such dynamics emerges from the underlying anatomical cortico-cortical connectivity, we developed a whole-cortex model of resting-state fMRI signals in the mouse. Our model implemented neural input-output nonlinearities and excitatory-inhibitory interactions within cortical regions, as well as directed anatomical connectivity between regions inferred from the Allen mouse brain atlas. We found that, even if the model parameters were fitted to explain static properties of fMRI signals on the timescale of minutes, the model generated rich frame-by-frame attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we found that empirical mouse fMRI time series exhibited analogous signatures of attractor dynamics, and that model attractors recapitulated the topographical organization of empirical fMRI CAPs. The model established key relationships between attractor dynamics, CAPs and features of the directed cortico-cortical intra- and inter-hemispheric anatomical connectivity. Specifically, we found that neglecting fiber directionality severely affected the number of model’s attractors and their ability to explain CAPs. Furthermore, modifying inter-hemispheric anatomical connectivity strength by decreasing or increasing it from the value of real mouse anatomical data, resulted in fewer attractors generated by cortico-cortical interactions and reduced non-homotopic features of the attractors generated by the model, which were important for better predicting empirical CAPs. These results offer novel theoretical insight into the dynamic organization of resting state fMRI in the mouse brain and suggest that the frame-wise BOLD activity captured by CAPs reflects an emerging property of cortical dynamics resulting from directed cortico-cortical interactions.