Quantitative Biology

Multicellular organisms comprise various types of cells, which are characterized by gene expression through interactions between chromosomal DNA and nuclear proteins. Many cutting-edge methods have been developed to reveal the three-dimensional organization of chromosomes. The detailed analyses of whole chromosomes have begun to uncover structural features specific to several cell types. Here, we show that cell types are instantly and highly accurately classified using conventional DNA staining and a convolutional neural network (CNN). A high-resolution single slice image of the nucleus is sufficient for the accurate classification of both live and fixed cells, including neurons and non-neural cells. These findings suggest that there may be cell-type-specific features decipherable by deep learning in a thin two-dimensional slice of the nucleus.

Genomic DNA is wrapped around core histones to form nucleosomes, which are organized in cells from euchromatin to heterochromatin with distinct genome functions. Although transcription is known to shape chromatin behavior in live cells, it remains unclear how different transcription systems shape chromatin classes and nuclear subcompartments. We developed machine learning-assisted Repli-Histo labeling to classify euchromatin and heterochromatin classes (Classes IA, IB, II, and III) and combined it with single-nucleosome imaging in live cells. Nucleosome motion was progressively constrained from euchromatin to heterochromatin. RNA polymerase II inhibition by THZ1, DRB, or -amanitin increased nucleosome motion in euchromatic Classes IA and IB and in heterochromatin around nucleoli, but not at the nuclear periphery. In contrast, RNA polymerase I inhibition by CX-5461 selectively increased nucleosome motion in Class III heterochromatin around nucleoli. Our study reveals that Pol II and Pol I transcription shape chromatin behavior in distinct chromatin classes and nuclear subcompartments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/736477v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@127137dorg.highwire.dtl.DTLVardef@709a16org.highwire.dtl.DTLVardef@94550corg.highwire.dtl.DTLVardef@5ba6ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundDynamic chromatin behavior, which is related to chromatin accessibility, plays a critical role in various genome DNA functions such as RNA transcription and DNA replication/repair. Previous studies using highly synchronized cells showed that average local chromatin motion, captured by single-nucleosome imaging and tracking on a second time scale, remained almost constant throughout G1, S, and G2 phases in living human cells, although possible effects of prolonged drug treatments for cell-cycle synchronization could not be excluded. ResultsTo avoid possible effects of prolonged drug treatment, we combined single-nucleosome imaging with Fucci probes to visualize cell-cycle progression through G1, S, and G2. Using HeLa and HCT116 cells expressing H2B-HaloTag and Fucci probes, we found that local nucleosome motion remained similar on average throughout interphase, except for elevated motion in early G1. Transcription inhibition similarly increased nucleosome motion throughout interphase. Local nucleosome motion also increased following replication stress or DNA damage. ConclusionOur findings suggest that near-constant chromatin motion supports housekeeping functions under similar physical conditions during interphase. Our findings also suggest that cells can transiently change chromatin motion to perform ad hoc tasks in response to signals from inside and outside the cell, such as DNA damage.

Transcription factors (TFs) play a central role in gene expression and regulation. In recent years, numerous experimental techniques have generated large-scale datasets, alongside computational methods aimed at inferring the role of TF-TF interactions in gene regulation. However, these approaches typically yield global interaction patterns across datasets, which may not accurately reflect local regulatory interactions at specific enhancers. Here, we model transcription using an Ising-type biophysical framework and introduce approximations based on its mean-field representation to infer TF-TF interactions at the level of individual enhancers from expression data, such as STARR-seq or fluorescent protein measurements. We validate our approach using simulated data and evaluate the effect of the strengths of TF-TF and TF-DNA interactions on inference accuracy. We then apply the model to experimental fluorescence data of gap genes for the eve stripe-2 (eve2) enhancer in the fruit fly embryo. The model successfully infers the established roles of the gap genes and predicts the possibility of cooperative and antagonistic interactions among them, which can be experimentally investigated.

In mammalian cells, under normal circumstances, the p53 protein exhibits oscillatory dynamics in response to DNA damage and maintains the cells in a cell-cycle-arrested state. Intriguingly, some cells can escape this cell-cycle-arrested state even after prolonged DNA damage, and often undergo mitotic catastrophe. In this context, the precise role of p53 dynamics and its complex interplay with cell-cycle regulation remain poorly understood. Herein, by constructing a comprehensive network model, we have identified crucial crosstalk regulations between the p53 protein and key cell-cycle regulators that enable some cells to escape cell-cycle arrest during prolonged DNA damage. The model further illustrates a probable cellular mechanism underlying mitotic catastrophe and predicts ways to induce it in a therapeutically relevant manner.

In recent years, the use of multi-model ensemble projections in infectious disease modeling has become an established methodological approach to account for and integrate across uncertainties and structural differences present in individual models. However, the creation of long-term ensemble projections through these coordinated efforts is resource-intensive, demanding the input of multiple research teams and substantial computational power. This typically limits the ability to refine projections, update the selection of plausible epidemic trajectories, or expand the number of scenarios that can be assessed, even as new empirical data become available. To address this challenge, we define an adaptive ensemble approach that, analogously to a multi-model particle filtering method, dynamically selects individual model trajectories based on observed data throughout the epidemic projection period. We demonstrate the effectiveness of this methodology using the U.S. Flu Scenario Modeling Hub (SMH) projections for influenza hospitalizations in the United States during the 2023-2024 and 2024-2025 winter seasons. Our findings show that the adaptive ensemble yields improved predictive accuracy with respect to the original SMH ensemble projections across several scoring rules and geographical resolutions. Furthermore, the adaptive ensemble approach offers two additional applications: i) the dynamic assignment of posterior probabilities to epidemic scenarios, identifying the most plausible scenario, and representing how reality is captured by a combination of scenarios, and ii) the potential use for short-term forecasting. The adaptive ensemble approach is able to identify the most likely scenarios for the 2023-2024 and 2024-2025 U.S. influenza seasons, even in the early stages of the epidemic. It outperforms, retrospectively, a baseline model in short-term forecasting of influenza hospitalizations in the United States during the two seasons across various horizons and scoring rules, showing potential to contribute to real-time collaborative forecasting challenges such as CDC's FluSight. The proposed approach offers an efficient or low-resource strategy to increase the impact of multi-model epidemic projections by providing real-time support to modeling teams, public health authorities, and decision-makers.

Cell polarity, essential for cell development and function, relies on dynamic subcellular distribution of structural and signaling molecules. Tip growth, an extreme form of polar growth, involves unidirectional expansion at the apical region of cells and requires precise spatiotemporal coordination to achieve periodic and directional growth. Understanding their spatiotemporal dynamics is critical for elucidating mechanisms and functions of cell polarity. However, manual quantification of such dynamics is extremely time-consuming, hindering advancements in the field. Current algorithms have limited power and flexibility in analyzing the distribution and dynamics of molecules and structures, particularly for tip-growing cells with oscillatory and dynamic behavior. To address this challenge, we present TipQuant, an automated analysis tool that robustly detects tips and analyzes spatiotemporal dynamics of fluorescently labeled molecules/structures on plasma membranes and in cytoplasm at apices of tip-growing cells, enabling quantitative understanding of signaling and structural components in these systems.

Background and objectiveGene regulatory networks are formed by complex regulatory relationships between transcription factors and their target genes. A systematic understanding of these regulatory relationships is crucial for deciphering the molecular mechanisms that underlie cell state transitions under physiological and pathological conditions. Single-cell expression data can reveal cell-type-specific transcriptional regulation, and computational methods have recently been developed to infer gene regulatory networks from single-cell transcriptomics and prior regulatory knowledge. However, existing methods could not explore the common and specific information in expression correlations and prior regulatory knowledge, which can adversely affect prediction performance. MethodsWe propose a novel method for inferring gene regulatory networks from single-cell RNA sequencing data. The proposed method consists of dual-channel graph neural networks and a weight-shared common graph neural network, enabling effective fusion of prior regulatory knowledge with gene co-expression patterns. Furthermore, we formulate a new computational framework built upon the proposed algorithm, which integrates differential gene expression profiles and regulatory changes to identify key regulators that distinguish different cell states. ResultsExperimental results demonstrate that our method significantly improves the accuracy of regulatory inference across multiple datasets, outperforming other state-of-the-art approaches. Our method also exhibits robustness to noise and missing data. Analysis of two single-cell expression datasets suggests that the proposed framework could help identify key regulators involved in tumor metastasis and drug resistance. ConclusionThese results indicate that the proposed method could advance the understanding of the biological mechanisms underlying diseases by reconstructing single-cell gene regulatory networks and identifying key regulators across different cell states.

Heterochromatin is a membraneless compartment within the cell nucleus. In recent years, a controversy arose on whether heterochromatin organization is driven by liquid-liquid phase separation or not. While many heterochromatin proteins were shown to undergo liquid-liquid phase separation in vitro, other studies reported that this does not happen in cells. Here, we tested the ability of heterochromatin proteins to generate heterochromatin barrier compartments in cells. We found that, while several proteins (H1.0, H1.4, HP1alpha, HP1beta, Mbd1, Mbd2 and MeCP2) form barrier compartments in mouse and/or human cells this differs between cell types. In addition, not all compartments in the same cell form barriers. We established and experimentally validated a model that predicted the ability to form barrier compartments is dependent on the protein accumulation in heterochromatin followed by the competition between compartments for the nucleoplasm pool of the protein and resulted in larger size for the barrier compartments. These findings resolve the existing controversy and rationalize how in cells heterochromatin compartments form and compete to establish dynamic barriers to the entry and exit of its components. HighlightsHeterochromatin barrier formation differs between proteins, cell lines and heterochromatin compartments within the cell. Barrier formation depends on heterochromatin anchors, including ligands and other scaffolds. Barrier compartments are defined by their larger size and higher protein enrichment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/729812v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@a63021org.highwire.dtl.DTLVardef@a20362org.highwire.dtl.DTLVardef@8c2390org.highwire.dtl.DTLVardef@72dde3_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundInfluenza A virus (IAV) is a major public health burden, causing seasonal epidemics and occasional pandemics. Its transmission from avian species to mammals and subsequent spread requires adaptive changes in the viral genome. Understanding these molecular adaptations is essential for pandemic preparedness, and machine learning offers a powerful approach to uncover the evolution and biology of IAV. ResultsOur calibrated WaveSeekerNet model accurately predicted the host source of 8 IAV segments (Macro F1-score: 0.9728), significantly improving the reliability of predicted probabilities, with calibration errors approaching zero. Interpretation showed that avian-adapted IAVs consistently activated G/C content, whereas mammalian-adapted IAVs generally activated A/T content. This distinction was confirmed by codon-level analysis, in which G/C-rich codons were rewarded for the avian hosts and A/T-rich codons for the mammalian hosts. We defined host-adaptive distance to quantify species barriers and proposed it as a risk-assessment metric. We hypothesized the Mammalian Adaptation Zone (MAZ), a zone where the virus is expected to adjust its host-adaptive distance to reach, thereby helping it establish persistent mammalian lineages. The analysis also revealed the Hard Distance of avian-origin viruses (e.g., H5Nx, H9N2), indicating they have not yet established persistent mammalian lineages. Finally, analysis of human H7N9 (2013, China) and non-human mammalian H5Nx (North America) viruses showed that WaveSeekerNet accurately identified key mammalian-adaptive mutations, including PB2-E627K and PB2-D701N. ConclusionsWaveSeekerNet elucidated IAV host-adaptation mechanisms in silico, providing insights into the underlying mechanisms of host adaptation and informing improved surveillance and intervention strategies.

Protein behavior inside cells is dominated by the crowded nature of the intracellular environment. Progress in structure determination of proteins and protein complexes, based on advances in Artificial Intelligence, provides an opportunity for structure-based modeling of cellular phenomena. Such modeling at the atomic resolution has been advanced by the traditional simulation techniques, e.g. molecular dynamics. A recently developed docking-based approach implements Markov Chain Monte Carlo sampling of intermolecular energy landscapes, offering several orders of magnitude faster simulation protocols. The approach allows addressing much longer trajectories of macromolecular systems in the crowded intracellular environment at atomic resolution. The sampling by design avoids low-probability (high-energy) states, which greatly accelerates the simulation process. A notable feature of this docking-based approach is the rigid body approximation of protein structures. The rigid-body approximation had been the primary direction in the protein docking field up until recent developments in deep learning. The rigid-body approach should be quite robust for the higher energy transient interactions that dominate the highly crowded cellular environment, as they likely involve relatively small conformational change. However, it is less applicable to the low-energy protein-protein complexes, especially those involving flexible regions. We addressed this problem by incorporating AlphaFold3 top models of the protein complexes in the mapping of the intermolecular energy landscape, as representative of the low-energy configurations of the protein assembly. By the nature of the AlphaFold predictions, these models involve appropriate conformational change between unbound and bound structures. These low-energy docking poses are combined with the rigid-body docking predictions that cover the multiplicity of the transient interactions. Such combination directly addresses the conformational flexibility of proteins upon binding along with the multiplicity of the transient protein encounters in the crowded cellular environment. SIGNIFICANCEProtein behavior inside cells is dominated by the crowded nature of intracellular environment. A recently developed approach allowed addressing long simulation trajectories of macromolecular systems in such environment at atomic resolution. A notable feature of this approach is the rigid body approximation in representation of the protein structures, which had been popular in the field up until the recent developments in artificial intelligence. However, such approximation is less applicable to stable protein-protein complexes, especially those involving flexible regions. We addressed this problem head-on by incorporating top deep learning-generated models of protein complexes. The new approach directly accounts for the flexibility of protein structures upon binding, along with the multiplicity of the transient protein encounters in the crowded cellular environment.

Mechanistic ordinary differential equation models are widely used in systems biology to represent biochemical networks, population dynamics, cell-state transitions, and other biological processes; however, their predictive value depends critically on accurate parameter estimation from noisy and often sparse experimental data. In this tutorial, we present the Weak-form Estimation of Nonlinear Dynamics (WENDy) method as a forward-solver-free approach that reformulates parameter estimation as a covariance-corrected weak-form regression problem by integrating the model equations against compactly supported test functions. We present the background on the methodology through the lens of the familiar logistic equation, and we demonstrate applications of the method on real experimental data through two systems biology examples: a glycolytic oscillator with relatively dense time-course data and a sparse epithelial-mesenchymal cellstate transition model with multiple experimental replicates. Ultimately, using WENDy, we estimate interpretable biological parameters with uncertainty for systems with noisy and sometimes sparse available experimental data.

Inferring gene regulation from time-course expression profiles is essential for understanding how cells transition between states during development, differentiation, and disease progression. Existing approaches often model expression dynamics with ordinary differential equations (ODEs). However, due to the computational complexity of directly solving these ODE models, most methods rely on finite-difference approximations of temporal derivatives, which can amplify measurement noise, introduce discretization bias, and lead to unstable or biased parameter estimates. To fill this gap, we develop the first computational method to directly learn a linear ODE model for gene regulation inference without relying on finite-difference approximations. We first formulate an optimization problem that directly exploits the closed-form solution of the linear ODE system. We then solve this problem via gradient descent, deriving analytical gradients with respect to the model parameters; these gradients involve matrix exponentials and integrals, which are challenging to directly compute. To make the computation efficient, we further use high-order Taylor approximations of the gradients whose truncation error is on the order of machine precision. In addition, we establish theoretical results demonstrating an inherent, non-vanishing gap between our exact solution and solutions derived from finite-difference approximations, which underscores the theoretical advantages of our approach. Finally, we demonstrate that our method consistently outperforms competing approaches on both simulated data and real-world scRNA-seq datasets in terms of AUROC. Our source codes can be accessed here: https://github.com/EJIUB/ExactLinearODE

Vaccination can be a useful intervention for reducing infectious disease burden. Estimating numbers of vaccine-prevented health outcomes is one approach to quantifying the benefits of vaccination. Here we improve a method described by Foppa et al. (1) that assumes vaccination has only direct effects, that is, it cannot prevent infection or onward transmission of the disease. We rederive this method and derive an improved method that increases estimation accuracy with minimal additional analytical complexity. To evaluate the improved method, we simulated disease outbreaks and compared the accuracy of the two methods for estimating prevented disease outcomes. In 84% of simulations performed over a wide parameter space, the improved method had an equal or smaller estimation error compared to the original Foppa method, with 7.9-fold smaller mean error and 44-fold smaller standard deviation of errors. Our study improves a method for estimating prevented burden when assuming vaccination has only direct effects.

Gene regulatory network inference is widely used to reconstruct large-scale networks and identify functional genes from transcriptome data. Meanwhile, in many biological fields, core regulatory genes have been extensively studied, leading to the establishment of small-scale gene regulatory networks, and novel genes connected to these networks remain to be identified. However, methods for expanding existing gene networks by identifying novel regulatory interactions, rather than reconstructing the entire network, are not well established. Here, we propose a method for gene network expansion that incorporates known regulatory relationships and evaluates each candidate gene individually to infer its regulatory connections to the existing network. Using simulated datasets from the DREAM4 benchmark and the PRECISE-1K experimental dataset, our method outperformed conventional methods by incorporating prior knowledge. In particular, it improved the ability to distinguish true regulatory interactions from indirect associations arising from strong correlations among genes in the existing network. The method also showed strong performance for interactions involving genes with high outdegree or centrality. Furthermore, it maintained stable performance as the size of the existing network increased and was robust to noise in prior information. These results demonstrate that our method provides an effective framework for expanding existing gene regulatory networks by leveraging prior knowledge.

Aedes aegypti is a major vector of arboviruses, and belongs to subfamily Culicinae, a diverse group of mosquitoes with homomorphic sex-determining chromosomes. Males are the heterogametic sex with a dominant male-determining locus (M locus). The M locus and its counterpart m locus are embedded in a region of suppressed recombination, with a large portion of this recombination desert showing significant molecular differentiation despite homomorphy. We developed a mathematical framework to examine M-linked genome editors that specifically target the m-chromosome during spermatogenesis, mimicking the naturally occurring sex ratio distorters (SRDs) in Culicinae that produce male-biased meiotic drives. Unlike previous models for species with heteromorphic sex chromosomes (e.g., X and Y), we incorporate features stemming from the homomorphic nature of the Ae. aegypti sex chromosomes such as varied linkage to the M locus, making the degree of super-Mendelian inheritance readily tunable. We evaluated in silico SRDs with a range of M-linkage and editing efficiencies and established the theoretical foundation for developing highly efficient SRDs that outperform several methods of population suppression. These SRDs can be tuned to mitigate impact on a neighboring population. The framework developed here is suitable for exploring SRD-mediated genetic biocontrol of pests with homomorphic sex chromosomes.

This work focuses on the global and partial identification problem for fractional differential equations. We provide a general numerical procedure based on global and local optimization algorithms with two refinements for biological systems that ensure solution positivity and homogeneous parameter units. The method is applied to a new fractional model of Dengue outbreak called the Fractional Homogeneous Nishiura (FHN) model, calibrated using data of newly infected people in Cape Verde. We show that our identification method yields a better fit between data and model solutions than previous approaches and that our FHN model captures the dynamics of Dengue more closely than existing systems.

BackgroundDNA methylation can be profiled using multiple technologies that vary in resolution, coverage and cost. Yet systematic benchmarks across these methods remain scarce. MethodsWe compared six widely used technologies -- Illumina EPIC array, TWIST, Whole-Genome Enzymatic Conversion (WGEC), Reduced Representation Bisulfite Sequencing (RRBS), long-read genome sequencing (LR-GS) with Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) -- using Genome in a Bottle (GIAB) reference samples and ten samples derived of blood and fibroblast cultures of 5 individuals. We assessed CpG coverage, consistency of differentially methylated cytosine (DMC) detection and genomic annotation, with particular attention to overlapping signals across assays. ResultsDespite major differences in assay design, all technologies consistently identified DMCs enriched in promoter and intronic regions, highlighting these loci as robust hotspots of epigenetic variability. Annotation redundancy strongly influenced initial interpretations, with CpG island-related categories largely disappearing once annotations were collapsed to unique features. Sequencing-based methods (WGEC, TWIST, ONT) achieved the most comprehensive coverage, whereas EPIC arrays reproducibly captured promoter-associated differences despite limited scope. ONT sequencing enabled direct, long-read-based methylation profiling with phasing capability and showed strong concordance with short-read sequencing methods after coverage filtering, but required higher and more uniform coverage to achieve reproducible CpG-level agreement. PacBio methylation profiles showed a coverage-dependent discrepancy, with cross-platform concordance plateauing in GIAB samples despite high mean coverage, indicating residual technology-specific biases beyond simple coverage effects. ConclusionsCross-platform benchmarking yields coherent biological insights when coverage and annotation redundancies are carefully addressed. Practically, EPIC arrays remain valuable for promoter-focused cohort studies, WGEC and TWIST enable genome-wide discovery and ONT provides unique phasing and multimodal potential. This comparative framework can guide method selection and support more robust interpretation of DNA methylation data across diverse platforms.

Single-molecule (SM) imaging (SMI)-based approaches have the powerful ability to capture receptor interactions, which are necessary for cell signaling, in their native live-cell environment. Yet, due to substoichiometric labeling, SMI generally provides only partial information on these interactions. We developed Deep-FISIK, which utilizes graph neural networks and multi-head attention for message-passing, to predict from SMI data the kinetics of homotypic interactions of the full receptor system. The input to Deep-FISIK are the SM detections in SMI experiments, without the need for explicit tracking. Thus, Deep-FISIK is compatible with labeling a higher fraction of receptors in the SMI experiments, increasing the prediction accuracy of the interaction kinetics parameters. The performance of Deep-FISIK is robust in the presence of a variety of deviations from the training data, indicating the applicability of Deep-FISIK to many receptor systems and SMI experiments.

Eukaryotic cells control their size by coordinating growth and division. Fission yeast divide at a reproducible cell size due to regulated activation of the cyclin-dependent kinase Cdk1. The nuclear concentration of mitotic cyclin Cdc13 increases in a time-dependent manner to promote Cdk1 activation as cells grow. Here, we show that interphase Cdc13 is stable against degradation and nuclear export, but is diluted by cell growth. Low glucose reduced cell growth rate but not time-dependent accumulation of Cdc13. Uncoupling the rates of cell growth and Cdc13 accumulation resulted in higher concentrations of nuclear Cdc13 despite reduced cell size. This change coincided with reduced activating phosphorylation of Cdk1-T167 and occurred dynamically during abrupt changes in glucose concentration. Mathematical modeling and experiments showed that cells maintain size homeostasis under these conditions. In contrast to low glucose, poor nitrogen reduced both cell growth rate and Cdc13 accumulation rate. Therefore, Cdc13 accumulation is independent of cell growth rate but can be altered by nutrient-specific mechanisms.