Quantitative Biology

Chromosome was "self-assembled" into a complex three-dimensional topology in the nucleus, which provided the structural basis for important biological processes such as gene expression regulation and cell differentiation. The current understanding of the three-dimensional organization of the genome is based on high-throughput sequencing and imaging technologies to visualize the conformation of chromatin in its native environment. This study proposes a new research perspective and technical means to use fusion genes to determine the interaction information and three-dimensional structure of chromosomes. This three-dimensional information provided by the fusion gene spectrum not only shows the universality of the spatial layout of chromosomes, but also increases the interpretability of the occurrence of fusion genes. The chromatin conformation capture data of multiple tissues and periods is significantly correlated with the strength of fusion genes. For example, the frequency of trans-interaction between chromosomes 11 and 17 was the highest, and the frequency of fusion gene occurrence corresponding to KMT2A on chromosome 11 was the highest. Therefore, we infer that this structural universality is a common feature of the human genome, which provides a theoretical basis and data for predicting chromosome fusion events.

Oscillatory and saturating dynamics in regulatory gene networks govern processes such as cell cycle timing, circadian rhythms, and inflammatory signaling, and sparse regression methods such as SINDy offer an interpretable method for recovering their governing equations from time-series data. However, these networks are typically governed by Hill-type nonlinearities that become nearly collinear with polynomial candidates over most bounds of an oscillating curve, and placing them in a shared library produces ill-conditioned regressions and unreliable coefficient estimates. Standard approaches such as ridge regularization, library normalization, and orthogonalization can marginally improve conditioning but cannot remove the structural multicollinearity of these systems, in turn limiting our ability to produce accurate parameter estimates. To address this issue, we propose the Regulation-Informed Sparse Identification of Nonlinear Dynamics (RI-SINDy) method, which removes known regulatory terms from the regression and recovers their coefficients directly from an asymptotic drift-balance condition. Because the time averaged derivative of a bounded trajectory approaches zero, the coefficients of regulatory nonlinearities can be computed independently by constraining them to offset the mean contribution of the remaining terms. We demonstrate that this method effectively reduces library collinearity and improves conditioning numbers by 3-45x, substantially improving coefficient recovery and regression accuracy. We test the RI-SINDy method on experimental Hes1 data and on synthetic Goodwin and NF-{kappa}B models. On Hes1, it achieves the lowest trajectory error among the compared methods (Traditional SINDy, SR3, and Nullcline Reconstruction). On the synthetic systems, it recovers the correct sparse structure with lower trajectory error than SR3 and Nullcline Reconstruction, and in a Goodwin noise sweep it recovers all active terms at noise levels up to 15%, where Traditional SINDy and E-SINDy fail to. In conclusion, the RI-SINDy method provides a robust framework to overcome the issue of multicollinearity for system identification in biological systems.

Automated surveillance systems can prove to be extremely useful in detecting outbreaks of respiratory diseases that need further epidemiological investigation. In order for the system to be effective, careful attention has to be paid to the baseline used, absence of the observations, and the use of proper statistical methods. As a solution, the paper describes EpiAlert, a weekly surveillance system based on CDC FluView ILINet. The system is able to transform the number of ILI visits and the total number of visits into a ILI prevalence for the region, create the first reference from the three previous years using a +/- 2-week interval, transform the rate value, and apply two different detectors - CUSUM and EWMA. CUSUM is set at k=0.5 and h=5, whereas EWMA uses {lambda}=0.2 and H=2.7 values. Among the 55 districts, the system gives 45,138 pieces of information about the disease by transforming 44,297 pieces of data into alerts. It is found that there are 2,820 alerts in total (out of which 2,711 alerts used CUSUM, 402 used EWMA, and 293 used both detectors). Additionally, the experiments show that the sensitivity of the device depends greatly on the detector. The latest statistics can be tracked in the report for week 37 of 2026 that recently concluded in West.

Nitrogen dioxide (NO2) is a widespread air pollutant associated with the risk and prevalence of various respiratory infectious diseases. Epidemiological studies have reported associations between NO2 exposure and COVID-19 incidence, and biological evidence indicates that NO2 exposure increases vulnerability to infection. Motivated by these findings and by the scarcity of mechanistic epidemic models that incorporate air pollutant data, here we introduce an NO2- and mobility-informed metapopulation model and study the first COVID-19 wave in Oklahoma. County-level NO2 concentrations enter the infection rates through a first-order parameterization, alongside high-resolution mobility patterns capturing heterogeneous inter-county connectivity. Using numerical simulations, we demonstrate that higher environmental sensitivity produces earlier and more synchronized outbreaks across counties, while lower sensitivity yields delayed, asynchronous trajectories. When fitted to county-level COVID-19 incidence data, our model outperformed the classic SIR model (fitted independently in each county) in over 75% of counties in Oklahoma across all standard error metrics. The improvements were even more pronounced in forecasting tasks across epidemic stages, with county-level win rates against the SIR model of 59.74% (surge), 87.01% (peak), 80.52% (post-peak), and 88.31% (decline) by nRMSE. To isolate the contribution of NO2, we compared our model against a mobility-only model with NO2 removed and homogeneous infection rates across counties. The NO2 term improved county-level forecasts during the surge (94.81% of counties by nRMSE), post-peak (88.31%), and peak (62.34%) stages, though not during the early and decline stages, suggesting that NO2 information can generate better forecasts than mobility alone during periods of high incidence. Our results demonstrate that environmental exposure can enter the transmission structure of a mechanistic epidemic model directly and that doing so yields measurable forecasting gains.

Inverse design has advanced the discovery of molecules and materials. Yet it remains largely unexplored for steering cell fate, where inputs are continuous, combinatorial culture conditions and outputs are distributions of cell states. Human hematopoiesis, in which combinations and doses of growth factors steer stem cells toward each blood lineage, is a demanding test case. While forward models can evaluate discrete conditions, the vast combinatorial space renders searching for a specific, target-producing design an intractable inverse problem. Here, we introduce LabCompass, an inverse-modeling framework that optimizes experimental designs toward candidates expected to produce specified target cell states, using a flow-matching generative model guided by cellular phenotypes predicted by a forward surrogate. We applied LabCompass to primary human CD34+ in vitro cultures, where continuous cytokine and small-molecule parameters govern hematopoietic cell fate. In a lab-in-the-loop setting, we experimentally validated model-driven designs and used these measurements to update the model across four successive loops. LabCompass recovered established lineage-inducing designs, improved predictions with new data, and enriched target populations across lineages. Incorporating new parameters expanded the design space and enriched rare populations beyond initial reach. Across loops, we generated a 78-million-cell spectral flow cytometry dataset and identified distinct culture conditions converging on the same target cell states. Our results establish generative inverse modeling as a compelling paradigm for navigating high-dimensional, continuous experimental spaces, and demonstrate that virtual cell models should be judged not only by how accurately they predict cellular responses, but also by how effectively they guide experiments toward desired biological outcomes.

Mechanistic models of infectious disease transmission provide a framework for understanding and predicting disease dynamics, but statistical inference is often computationally prohibitive. This challenge is especially pronounced for models with high-dimensional structures and many unknown parameters, where trajectory matching may require thousands of evaluations of an expensive likelihood function. We developed a surrogate-assisted optimization framework that uses Bayesian optimization to reduce the number of evaluations of the underlying mechanistic model required for parameter inference. We evaluated the approach using simulated fitting problems based on age-structured susceptible-exposed-infected-recovered models with 4, 8, and 16 age classes and a 210-compartment age-structured rotavirus transmission model. Applying surrogate-assisted optimization substantially reduced the number of likelihood evaluations required to identify good parameter estimates, with larger improvements as model dimensionality increased. In the rotavirus model, the surrogate Bayesian optimization approach reached the benchmark after approximately 0.7 hours, compared with approximately 9.4 hours using traditional Nelder-Mead optimization. These results demonstrate that surrogate-assisted optimization can substantially reduce the computational burden of trajectory matching for complex mechanistic transmission models, particularly for higher-dimensional inference problems.

The circadian clock allows living systems to anticipate environmental daily variations by scheduling cellular mechanisms according to the time of the day. The reciprocal interplay between metabolism and circadian clock is essential for energy homeostasis, and the disruption of metabolic clock inputs contributes to dysfunction of clocks in peripheral tissues. However, the molecular mechanisms through which the feeding/fasting cycle entrains peripheral clocks remain incompletely understood. Here, we investigate experimentally and theoretically the role of AMP-dependent kinase (AMPK), a key fasting sensor, in metabolic regulation of the circadian clock. Using a luciferase reporter driven by the Bmal1 promoter to monitor molecular clock activity in human U2OS cells, and thanks to a signal processing method based on the Hilbert transform to retrieve the instantaneous phase and period of circadian signals, we show that pharmacological activation of AMPK by AICAR significantly elevates Bmal1 promoter activity and markedly lengthens the clock period in a dose-dependent manner. Conversely, the clock period is shortened by the pharmacological inhibition by SR18292 of Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1-alpha). Importantly, the action of AICAR is abolished by FK866, a nicotinamide phosphoribosyltransferase (NAMPT) inhibitor, demonstrating that AMPK-dependent modulation of Bmal1 promoter activity requires NAD+ availability. These experimental results are recapitulated by numerical simulations of our previously published mathematical model, where PGC-1-alpha plays a key role in mediating AMPK activity to the clock. Moreover, the comparison of experimentally measured and theoretically predicted phase response curves, following AMPK activation at different circadian phases, provides an additional validation of the model. By integrating biological experiments with mathematical modeling, our results identify a key mechanism through which metabolic factors can entrain and regulate the circadian clock via NAD+-dependent AMPK-mediated regulation of Bmal1 promoter activity.

Nuclear segmentation in 3D microscopy remains challenging, particularly for low signal-to-noise ratio (SNR) data from timelapse imaging, in vivo experiments, or dense tissue samples. Yet accurate 3D nuclear segmentation is a prerequisite for quantitative single-cell analysis, making this a key bottleneck for high-throughput study of challenging datasets. We present NucLogic, a fast and robust 3D nuclear segmentation algorithm that combines the maturity of 2D segmentation with geometric stitching rules based on nuclear shape, orientation, and intensity, to reconstruct accurate 3D volumes. NucLogic reliably segments challenging datasets including long-term timelapse imaging of organoids, and large dense structures such as the zebrafish brain. Benchmarked against other state-of-the-art segmentation tools, NucLogic matches F1 scores at high SNR, with increasing outperformance as SNR decreases. Importantly, segmentation completes at 20-50% of the computation time, with greater speed gains on larger images. Beyond segmentation, NucLogic includes an intuitive graphical user interface allowing non-experts to segment, quantify, and visualize 3D and 4D data. This enables easy quantification of cell numbers and integration of a variety of both cell-intrinsic properties, such as phenotype, morphology, and local environment metrics, including density and spatial interactions. Together, NucLogic makes quantitative 3D nuclear analysis of previously unmanageable datasets accessible to a broad research community.

Dengue virus (DENV) causes dengue fever, a globally prevalent, epidemic-prone disease with no FDA-approved antivirals. Additionally, the available vaccine for DENV can increase the risk of severe dengue fever for those who have never had a DENV infection due to antibody-dependent enhancement. Thus, there is an urgent need to identify DENV antivirals. Drug repurposing, high-content screening, and machine learning were leveraged to efficiently identify antivirals likely to inhibit DENV replication. Using high-content screening in a DENV infection model, we characterized the morphological patterns of NS4B and envelope (E) protein expression over time and developed a viral pseudotime model that predicts infection progression to aid in phenotypic interpretation of drug effects. We then developed a single cell infection classifier for antiviral efficacy and performed high-throughput drug screening of 960 compounds. We identified four concentration-dependent inhibitors of DENV with nanomolar-to-submicromolar potencies: esomeprazole, GW4064, pralatrexate, and LY411575. With our pseudotime machine learning model trained on data from the time-series assay, we scored the concentration-dependent inhibitors to map their mechanism of action onto time points in the viral life cycle. Our most notable results show that LY411575, a gamma secretase inhibitor, exhibited an IC of 72 nM and reduced percent infection to near the level of the mock infection control. Pseudotime inferences generated mechanistic predictions that were supported by time-of-addition experiments, indicating that LY411575 arrests DENV infection before the development of late-stage infection associated morphologies, while retaining activity when added after inoculation.

Protein labelling using genetically encoded fluorescent protein fusions has revolutionized the study of protein localization and function. However, visualizing endogenous proteins in specific cell types remains challenging, as existing approaches often require overexpression or laborious genetic engineering. To address these limitations, we developed a library of fluorescent protein binders using existing nanobodies and single-chain variable fragments that recognize fluorescent proteins and short peptide tags, allowing for modular, cell type-specific labelling of genetically tagged endogenous proteins. We found that fluorescent protein binders generate background fluorescence in the absence of the target protein, reducing signal specificity. To overcome this limitation, we generated destabilized fluorescent protein binders directed against GFP, ALFA, GCN4 peptide and V5. These binders display increased antigen-dependent stability and reduced background fluorescence, thereby improving the signal-to-noise ratio of endogenous protein visualization. We demonstrate the detection of endogenous proteins in drosophila larvae and embryos, zebrafish embryos, as well as in specific neuronal populations of the adult drosophila brain. In the brain, tissue-specific labelling revealed previously unknown protein expression and subcellular localization in sleep-regulatory neurons. Together, our panel of tag-stabilized fluorescent protein binders provides a versatile, ready-to-use toolkit for background-free, cell type-specific visualization of endogenous proteins.

Host immunity acquired through prior infections is a key factor shaping the spread of emerging variants. However, accounting for complete infection histories in mathematical models is challenging because the number of possible histories grows exponentially with the number of variants. Here, we present a flexible framework for modeling the co-circulation of multiple variants emerging at arbitrary times while incorporating cross-immunity and variant-specific characteristics. We introduce three tractable approximations: truncation, which retains only infection histories up to a given number of strains; one variant at a time, which is exact when variants circulation does not overlap; and two variants at a time, which is exact when at most two strains co-circulate. We systematically evaluate these approximations across a range of cross-immunity levels and inter-variant emergence delays. Truncation approximations perform best under short emergence delays and strong cross-immunity, whereas the other approaches outperform them at longer delays. When applied to SARS-CoV-2 variant dynamics, the second-order truncation and the two variants at a time approximation accurately capture incidence patterns, whereas the other approaches perform well only for specific subsets of variants. These results identify the parameter regimes in which simplifying assumptions remain valid, provide guidance for model selection, and highlight the key role of infection history in shaping epidemic trajectories.

Brazil experienced its worst dengue epidemic in 2024, with an increase in severe outcomes among elderly individuals, highlighting the need for vaccination strategies informed by population immunity. We reconstructed state- and age-specific immune profiles by fitting an age-structured catalytic model to dengue surveillance data from 2000-2025. This inferred immune profile was incorporated into an SIR-type transmission model to project disease burden and to evaluate the public health impact of vaccine allocation strategies. We observed substantial spatial heterogeneity in accumulated immunity, which was closely associated with projected burden and optimal allocation. Strategies prioritizing states with lower historical incidence and larger susceptible populations yielded greater benefits. Under limited vaccine supply, targeting children resulted in the greatest reductions in DALYs, whereas targeting elderly populations achieved smaller DALY reductions but larger reductions in mortality. We further showed that accelerated vaccine supply markedly increased impact, while allocation choices remained critical when availability was constrained.

Complex biological behaviours often emerge when a system is faced with mutually incompatible priorities. In such situations, the field of multi-objective optimisation can provide an informative and predictive theoretical foundation for biology. Here we adopt this paradigm in exploring the rich dynamic behaviour of mitochondria inside cells. Using plant cells as a model system, we use physical modelling to characterise the "morphospace" of possible mitochondrial behaviours, and single-cell microscopy with video analysis and network modelling to characterise collective mitochondrial dynamics. We show that wildtype Arabidopsis mitochondrial dynamics near-optimally resolve a tradeoff between maintaining physical spacing and supporting biomolecular exchange. With existing and new experimental data, we show that these dynamics adapt under mutational and chemical challenges to support a rebalanced, but still near-optimal, resolution to this tradeoff under different densities of the mitochondrial population. We also show how an assumption of multi-objective optimisation supports inference of biological mechanisms before any data are observed, and discuss the potential of this multi-objective optimisation paradigm to form a broader theoretical framework of spatial cell biology.

Quantitative imaging of extrachromosomal DNA (ecDNA) is increasingly important for studying cancer heterogeneity and adaptation, yet automated analysis has been limited by the absence of accessible imaging data, gold standard annotations and adaptable computational tools. Here we establish an open resource for computational ecDNA imaging, integrating 2,986 native-resolution metaphase FISH image sets with manual annotations, standardized benchmarks and open-source quantification frameworks. We use this resource to systematically compare existing and newly developed approaches spanning rule-based computer vision, deep-learning-based segmentation and probabilistic localization. This comparison reveals count-dependent underestimation that distorts ecDNA copy-number distributions and motivates ecCount, a probabilistic localization method developed here to preserve individual ecDNA signals and quantitative burden. ecCount achieves an object-level F1 score of 0.939 on held-out images with minimal count bias. Together, the images, annotations, retrainable models, evaluation tools and guided workflows provide community infrastructure for applying, adapting and improving automated ecDNA quantification across experimental systems.

For the purpose of differential expression (DE) analysis in single-cell RNA-sequencing (scRNA-Seq), phenotype differences between samples are often tested within specific cell types. Cell types are regularly imputed by clustering the same gene expression data which is later used for phenotype testing. This creates the potential for a "double-dipping" or post-selection inference problem resulting in inflated rates of false discoveries. While this selection bias is known to inflate significance in cell-type marker identification, its effect on sample-level phenotype testing, e.g. in patient cohorts, has never been explored despite the growing preponderance of this type of analysis. To address this, we perform an extensive simulation study and demonstrate that naive clustering on uncorrected embeddings can severely inflate the False Discovery Rate (FDR) in the presence of strong phenotypic differences. However, we further show that applying batch-correction methods to remove phenotypic effects prior to clustering resolves the FDR inflation with no obvious loss of power. Finally, we provide measures of phenotypic imbalance that can be applied to real datasets which closely track the false discovery proportion and thus can be used to as part of data exploration to gauge the risk of post-selection inflation of p-values in a particular dataset.

Stimulator of interferon genes (STING) provides an informative model system for developing methods to extract and compare residue-pair fluctuation patterns from molecular dynamics (MD) trajectories. We combined a one-dimensional convolutional autoencoder trained on apo STING with temporal mean pooling, Mahalanobis-distance clustering, and residue-pair similarity networks to compare apo, ligand-bound, and SAVI-associated multi-mutant datasets using 25-dimensional latent descriptors. Networks constructed from clusters capturing state-dependent fluctuation patterns showed lid-dominated organization in the ligand-bound dataset and a1b1-enriched, structurally distributed relationships involving residue 281 in the multi-mutant dataset. These findings identify contrasting residue-pair fluctuation signatures in the analyzed trajectories and provide an interpretable framework for generating hypotheses about ligand-associated and mutation-associated dynamic allostery.

Transcriptome-wide association studies (TWAS) provide an effective framework for identifying genes associated with complex traits. Population-scale single-cell transcriptomic data enable genetically regulated expression (GReX) prediction and TWAS analyses at cell-type resolution, but the predictive performance of existing single-cell TWAS methods remains limited. Here, we develop MOD-scTWAS, a module-based method that jointly models GReX for genes within co-expression modules to borrow information across genes. Starting from a generative model for single-cell gene expression, MOD-scTWAS accounts for the heteroscedasticity and cross-gene correlation of individual-level pseudobulk expression in joint GReX prediction. In cross-validation analyses of the OneK1K dataset, MOD-scTWAS achieved higher mean GReX prediction accuracy than scTWAS across all 14 cell types and increased the number of imputable genes. When applied to TWAS analyses of UK Biobank quantitative hematological traits, MOD-scTWAS identified more significant cell type-gene-trait associations than scTWAS. These results demonstrate the potential of leveraging gene co-expression through joint modeling to improve cell-type-specific GReX prediction and TWAS discovery.

The clonogenic assay is the foundational standard for evaluating cellular radiosensitivity, yet its manual quantification is limited by labor-intensive processing, high inter-observer variability, and a reductive binary scoring system. Here, we present and validate a deep learning-based segmentation pipeline for the automated, high-throughput quantification of mammalian cell survival across diverse radiation qualities (photons, protons, and carbon ions). By benchmarking against expert consensus using Bland-Altman analysis, we demonstrate that this AI framework consistently operates within natural human error margins. Crucially, transitioning to automated whole-well image-based analysis enables the extraction of continuous multidimensional data, including colony area, density, and cellularity. This phenotypic profiling successfully identified and quantified high-cellularity colonies formed by radioresistant BxPC-3 cells under particle irradiation. Coupled with rapid image acquisition platforms, this methodology significantly reduces processing bottlenecks while standardizing the derivation of relative biological effectiveness (RBE). By advancing beyond binary survival metrics to continuous morphological profiling, this framework provides a vital new analytical dimension for investigating structural mechanisms of radioresistance.

Coal-fired power plants remain a major source of fine particulate matter (PM2.5) exposure and mortality in the United States. Announced retirement dates mark when those burdens are expected to end, but rising electricity demand, partly from data centers supporting cloud computing and artificial intelligence, has led utilities and regulators to revisit them. Of 50 coal plants scheduled in January 2024 to retire before 2035, 20 had later or removed dates by January 2026. Projecting coal PM2.5 and mortality among Medicare beneficiaries from the 20 plants for 2026-2035, we estimate the revised schedule would result in 1,256 additional deaths (1,136 to 1,376), raising mortality to 3.1 times the original burden. Eighteen plants operate in balancing authorities with hyperscale data-center demand, and federal orders and utility filings supporting several plants' operation cited data-center growth. Retirement schedules are revised faster than health consequences are assessed, and those delays carry a measurable mortality cost.

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.