Quantitative Biology

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

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.

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.

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.

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.

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.

Saturated cultures of the pathogenic yeast, Cryptococcus neoformans, arrest as unbudded cells in the G2 phase of the cell cycle. As cells divided and cultures saturated, we found that oxygen levels in the culture medium dropped nearly tenfold. When saturation-arrested cultures were re-oxygenated without adding fresh growth medium, cells immediately formed a bud and then underwent mitosis. Thus, the arrest is due to low oxygen concentration rather than nutrient depletion. Because the G2 arrest was associated with unbudded cells, we asked whether C. neoformans cells have a morphogenesis checkpoint that blocks mitosis until cells can form a bud. Inhibition of budding by treatment with Latrunculin A also led to G2 arrest, and we determined that this arrest is dependent on the CDK inhibitory kinase, Swe1. This finding suggests that C. neoformans possesses a morphogenesis checkpoint analogous to that in the distantly related Saccharomyces cerevisiae. We also demonstrated that Swe1 is required to enforce the hypoxia-induced G2 arrest. We propose that hypoxia inhibits budding in C. neoformans, which in turn triggers a morphogenesis checkpoint to arrest cells in G2 even when nutrients are plentiful.

Background: Accurate identification of differentially methylated regions (DMRs) is fundamental to epigenomic research but remains challenging due to biological variability among replicates, heterogeneous effect sizes, and the tendency of adjacent cytosines to share similar methylation states. Many existing methods aggregate methylation measurements before statistical testing or do not explicitly account for replicate-level variability, contributing to elevated false-positive rates. Results: We developed glmmDMR, a DMR detection framework that combines generalized linear mixed models with a seed-based strategy for reconstructing DMRs from locally high-confidence signals while explicitly modeling replicate-level variability. Using simulated datasets with known ground-truth DMRs, we demonstrate that false-positive detections are more strongly associated with methylation variance among biological replicates than with the magnitude of methylation differences between groups. glmmDMR achieved higher precision than existing approaches while maintaining competitive recall, particularly for subtle methylation differences. Site-level modeling with beta regression provided the strongest overall performance, and seed-based region construction reduced artificial DMR fragmentation, improving recovery of true DMR boundaries and producing more contiguous, biologically interpretable DMRs. Applied to Arabidopsis thaliana ddm1 methylomes and a rice DEMETER-LIKE DNA demethylase mutant (Osdml3a-1), glmmDMR identified biologically meaningful DMRs, revealing widespread TE-associated hypomethylation and subtle TE-family-specific hypermethylation. Conclusions: Replicate-level methylation variance is an important determinant of DMR detection performance, and explicitly modeling this variance improves discrimination of biologically meaningful methylation changes from high-variance signals. By combining variance-aware statistical modeling with seed-based region construction, glmmDMR provides a robust framework for identifying contiguous, biologically interpretable DMRs across diverse methylome datasets.

Disease boundary analysis identifies abrupt changes in health outcomes across geographic boundaries, guiding targeted public health interventions and outbreak surveillance. Current implementations often adopt a Bayesian "wombling" approach and largely rely on Markov Chain Monte Carlo (MCMC) posterior sampling, presenting scalability issues for large-scale disease surveillance. We leverage amortized Bayesian inference (ABI) to accelerate the detection of spatial health disparities between neighboring US counties by embedding neural posterior estimation within a Bayesian areal wombling framework. Exploiting the computational efficiency of ABI, we further introduce the Residual Disparity Elimination Target, a metric for the required reduction in mortality or prevalence for a region to eliminate a significant disparity with its neighbor. We analyze tracheal, bronchus, and lung cancer mortality rates across mainland US counties and achieve results concordant with MCMC analysis while scaling areal wombling to hundreds of outcomes and translating disparity detection into interpretable policy objectives.

The cytoplasm is a crowded and dynamic fluid within which cellular building blocks such as mRNA, proteins, or organelles undergo transport and mixing. Although small things like proteins can eventually mix through diffusion, the high viscosity of cytoplasm means that it should be difficult to obtain significant mixing for structures in the size range of mRNA, multi-protein complexes or organelles. In large amoeboid cells, the cytoplasm undergoes active streaming coupled to cell motility, but this streaming is laminar flow which should not be effective for mixing. In this work we used a combination of live cell tracking of injected beads and computational analysis of motion and mixing in giant amoeba Chaos carolinensis with the initial goal of testing the possibility that large-scale cellular deformations during pseudopod formation might implement chaotic mixing by a Baker-transform like process. Instead, we found that Chaos carolinensis accelerates cytoplasmic mixing using a novel cytoplasmic gel state capture and release strategy. While it was previously thought that the amoeba sol to gel state transitions only occur at the trailing and leading edge of the cell body, our work indicates that these transitions occur frequently throughout the mid-cell region, driving the cytoplasmic mixing of beads and organelles. These results indicate that amoeba achieves nearly complete mixing between 1 and 2 cytoplasmic stream/flow cycle, effectively approximating the Bernoulli mixing regime and thus representing one of the theoretically fastest possible mixers.

We present a composite, compartmental, multi-clock port-Hamiltonian model of cell dynamics learned by a graph-neural-network surrogate. The state pairs abundance deviation qj, the quantity omics assays measure, with oscillatory phasors derived only for pools a rhythmicity gate certifies as periodic. The storage function decomposes over five functional compartments (core clock, redox, energy, signalling, biosynthesis), so passivity is certified compartment by compartment, and the model carries two mechanistically distinct clocks coupled through a zero-net-power signalling port, with the central dogma hard-wired and conserved moieties held as exact invariants. We evaluate it on a real mouse-liver tri-omic circadian dataset assembled from public repositories and report a deliberately mixed verdict. The trained model is passive ([Formula], no violations over three seeds), forecasts held-out trajectory segments (RMSE 0.324 {+/-} 0.0004), and recovers withheld regulatory edges above a permuted null (AUROC 0.94 {+/-} 0.01). Its central prediction -- that cross-omic phase lags follow {Delta}{varphi} = arctan({omega}/kdeg) -- matches the aggregate transcript-to-protein lag measured independently (5.7 vs 4.9 h) but not the gene-to-gene variation, and the internal two-clock cascade is not scoreable on the available cross-cohort metabolome. The framework thus gives a falsifiable, thermodynamically-grounded account of cell dynamics with explicit limits.

The information content of the genome has been extensively analyzed. However, a comparable quantitative framework for DNA methylation is still lacking. Without such quantification, the magnitude of this regulatory and dynamic epigenetic structure remains conceptually imprecise, even though methylation dysregulation is strongly linked to disease-related phenotypes and altered cellular identity. Here we address this gap by applying Shannon information theory to DNA methylation. We first consider methylation marks as binary or probabilistic regulatory states and estimate the theoretical upper-bound information capacity of the human methylome under simplifying assumptions. We then progressively refine this estimate by incorporating biologically relevant constraints, including methylation bias, bimodal methylation distributions, local CpG correlation, genomic regulatory class, and cell-type-discriminative methylation patterns. This approach allows us to distinguish between theoretical methylation capacity, statistical methylation entropy, and biologically interpretable regulatory information. Finally, we consider methylation information from a discriminative perspective, analyzing its contribution to distinguishing cell types and regulatory cellular states. Within this framework, mutual information between methylation patterns and cell identity provides a biologically constrained estimate of methylations role as an epigenetic identity code. Our layered analysis reconciles megabit-scale methylome capacity with compact, biologically interpretable identity signatures. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/735086v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@f0f0fdorg.highwire.dtl.DTLVardef@5d8a1eorg.highwire.dtl.DTLVardef@116debdorg.highwire.dtl.DTLVardef@79530e_HPS_FORMAT_FIGEXP M_FIG C_FIG

The relationship between genotypic and phenotypic variation is determined by the complex interaction of genetic and environmental factors. While statistical methods capable of detecting such interactions exist, an axiomatic mathematical framework that seamlessly describes the combined effects of genetic modifications and environmental exposures on a common scale is lacking. In this report, buffering concepts are used to construct a measurement system that enables the geometric representation of both gene-by-gene and gene-by-environment interactions on the extended complex plane (i.e., as projections on the Riemann sphere). In this manner, any such interaction, or combination thereof, can be precisely defined and quantified as the deviation from the neutral value calculated through the applicable complex transformation. When thus conceptualized, the framework's parameterization defines the "state space" of a given measurable phenotype along both the real and imaginary dimensions, thus establishing an unambiguous and broadly applicable method for determining the phenotypic value expected upon combinatorial changes in genetic and/or environmental variables. Remarkably, by applying these methods, it is possible to quantify the effects of any gene-by-environment interaction using the equation, AGxE=Im([z]obs*zexp)/2, where zobs and zexp are complex numbers representing the observed and expected phenotypes of a given genotype expressed in terms of the buffering parameters, and b.

Cancer cells rewire metabolism to support proliferation. Intriguingly, divergent metabolic choices are made to attain this common goal. Identifying the unique metabolic requirements for a specific cell has profound implications for cancer biology and precision medicine. Genome-scale metabolic models (GEMs) have emerged as powerful tools to systematically characterize, understand, and predict metabolism of cells and tissues. Despite being comprehensive, the current GEMs remain limited in their predictive power. Here, we present a new GEM of human cells, in silico Human Metabolic Essentiality (iHME), that significantly improves the prediction of metabolic dependencies at a reduced computational cost. Wse rationally downsized, curated, and corrected previous models to remove unsupported metabolic redundancies, which led to a slim model containing 4,377 reactions, 3,241 metabolites, and 1,825 genes. When used to reconstruct metabolic networks of 1,103 cancer cell lines, iHME recalled on average 84.6% of experimental essential genes, which is two-fold increase over previous models. Cholesterol biosynthesis was revealed to be the most reliably predicted pathway with alternative dependencies. Finally, we applied the model to reconstruct individualized networks and predict essential gene profiles for 8,384 patient tumor samples. Glucose transporter SLC2A1 (GLUT1) was identified as a context-specific dependency for head and neck cancers and ovarian cancer. Likewise, CDP-diacylglycerol synthase CDS2 was identified for skin cancer. Overall, iHME is a new genome-scale model for prediction of metabolic dependency at higher accuracy and computational efficiency.

Over-activation of mitogen-activated protein kinase (MAPK) signaling underlies numerous inflammatory pathologies that are treated using synthetic glucocorticoids to activate glucocorticoid receptors (GR) and induce expression of dual-specificity phosphatase 1 (DUSP1), which encodes for MAPK phosphatase 1 (MKP1). Despite its importance, the single-cell het-erogeneity of this spatial and temporal pathway has not been fully quantified, several regulatory mechanisms are unclear, and accurate quantitative predictions are not possible based on existing models. To address this challenge, we combined immunocytochemistry (ICC) and single-molecule inexpensive FISH (smiFISH) to quantify endogenous GR transport and DUSP1 transcription dynamics across thousands of single cells following dexamethasone (Dex) stimulation. Using Chemical Master Equations (CME) and likelihood-based inference, we identified clear mechanisms and reaction rates for Dex-driven GR nuclear import; compartment-specific GR degradation; GR-dependent modulation of DUSP1 promoter activation and transcription burst frequencies; DUSP1 transcription, elongation, and transport; and time-dependent and saturation-limited cytoplasmic degradation. Rigorous model comparisons against endogenous, fixed-cell data identifies nuclear GR degradation as the dominant mechanism of receptor clearance, indicates that GR primarily regulates promoter activation, and highlights time-dependent AU-rich element (ARE)-mediated mRNA degradation as a likely mechanism for DUSP1 clearance. With these mechanisms, the fully-parameterized model quantitatively predicts joint distributions of GR translation and decay dynamics, DUSP1 transcription site activity, and nuclear and cytoplasmic mRNA heterogeneity among clonal cells as functions of time and across seven orders of magnitude for Dex induction concentrations. Together, these results show that total DUSP1 mRNA levels emerge from the balance between GR-driven activation and cytoplasmic mRNA decay, with the inferred model quantitatively predicting single-cell distributions across held-out conditions.

The incidence rate ratio R is the standard measure for comparing event rates in clinical trials and epidemiology. In vaccine trials, the vaccine efficacy is VE = 1 - R. When events are rare, the two arm counts are Poisson. The estimator of R is heteroskedastic: its sampling variance changes with the data. So no fixed-width interval covers correctly everywhere. The usual log-Wald interval is undefined at zero events and covers poorly at small counts. Early vaccine and drug-safety readouts fall in exactly this regime. We show that a single reparameterization collapses this bivariate problem to an effective one-parameter family with a quadratic variance function, whose variance-stabilizing transformation is 2 arcsinh(sqrt(R)). The reduction yields a closed-form confidence interval for R. Its two leading errors, a curvature bias and the variability of the estimated scale, each admit a closed-form correction with no tuning constants. In a Monte Carlo study of our seven arcsinh variants and five competitors, the +Curve+Stu variant covers within 0.002 of the nominal 0.95 for about 50 control and 5 treatment events. Its width is on par with the best competitor. It avoids the conservatism and zero-count breakdown of log-Wald and MOVER. For moderate counts, we recommend this interval; for sparser data, our Bar-Lev and Enis count-shift variant is more robust. The result is a ready-to-use, closed-form interval for the low-count regime. We illustrate it on early Covid-19 vaccine-efficacy readouts and provide reference implementations in R and Python.

Stochastic epidemic models are a cornerstone of infectious disease epidemiology and are often used to study intervention scenarios. However, large run-to-run variability can make intervention effects difficult to estimate precisely. We revisit the epidemic Sellke construction, which assigns each individual an infection threshold for the cumulative infection hazard such that, conditional on the thresholds, the epidemic trajectory becomes deterministic. This enables coupling of simulations with and without an intervention, yielding low-variance effect estimates even when outcomes such as final size or peak incidence vary widely between runs. We develop an exact, event-driven implementation that maintains infection and recovery events in priority queues. Cumulative infection-hazard updates require O(log N) time per event, yielding overall complexity O(Elog N) for E events in a population of size N. The implementation achieves computational performance comparable to the classical Gillespie algorithm while naturally accommodating non-Markovian infectious periods and complex infectiousness profiles. We illustrate the approach using distance-dependent spread of avian influenza between poultry farms in the Netherlands and a multilayer population with households, schools, and workplaces. In both examples, coupling enables efficient within-run comparisons of intervention scenarios across stochastic realisations.

The impact of weather on the health of Americans and the American health system is substantial. Using available health and economic data, we developed a data-driven scenario that describes a compounded heat emergency in an archetypal community in the United States. We then characterize the potential human and economic costs of such a heat emergency to demonstrate the widespread impact on health outcomes, health systems, and society.

We develop a mathematical model of Rift Valley Fever integrating mosquito vectors, ruminants, and humans, based on an SEIR-type structure with vertical transmission in vectors. Local data from the Sudanian and especially the Sahelian zones are used to capture the impact of climatic variations on mosquito population dynamics. The mathematical analysis establishes the models positivity, determines the basic reproduction number R0, and demonstrates the local and global stability of the disease-free equilibrium. Sensitivity analysis (PRCC) highlights the most influential parameters, while the stochastic approach using a continuous-time Markov chain confirms the major role of seasonal rainfall. Numerical simulations reveal a peak in animal and human infections around the 9th month, correlating with periods of heavy rainfall. This model provides a relevant tool for surveillance and prevention within a "One Health" approach in Chad.

Cell shape and mechanics are intricately connected and tightly regulated by mechanochemical events including biochemical signaling, cytoskeletal remodeling, and plasma membrane mechanics. While experimental advances in microscopy have shed light on the intricate coordination involved in cell shape change in response to different cues, the ability to conduct three-dimensional simulations in realistic geometries remains an open computational challenge. In this work, we develop a finite-element framework that incorporates advection-diffusion-reaction equations coupled with equations governing the kinematics of a deformable interface representing the cell membrane. We applied this framework to three distinct coupled mechanochemical systems, each governed by geometric partial differential equations, resulting in large deformations of the interface. In all three examples, our simulations revealed the emergence of feedback between cellular signaling, cytoskeletal organization, and cell shape. In our first two sets of simulations, we observed that cell migration and neutrophil protrusion were regulated by membrane tension-mediated feedback. In our final application, we predicted shape changes of a dendritic spine starting from a realistic geometry, and found that the complex shape of the spine gives rise to localized regimes of actin cytoskeleton remodeling not previously observed with idealized geometries. Thus, our finite-element framework allows us to generate new mechanistic insights for biophysical problems.