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Multiscale Modeling Primer: Focus on Chromatin and Epigenetics.

A central challenge in modern biology is to understand how molecular interactions produce cellular and organismal functions across vast spatiotemporal scales. Nowhere is this challenge more apparent than in the study of chromatin, where meters of DNA compact into a micron-sized nucleus. How this polymer folds is a dynamic process, regulated by epigenetic modifications-chemical changes to DNA and histones that involve only a handful of atoms. These small changes cooperate to produce emergent, higher-order structures that define cellular identity and function. To explain this system, we must integrate static, high-resolution snapshots from techniques like cryo-EM with dynamic, lower-resolution data from microscopy and genomics. Multiscale computational models are essential tools that bridge these experimental gaps and reveal the mechanisms of emergent behavior. However, the communication divide between experimental biologists and quantitative modelers often hampers progress. This primer addresses that gap. It first introduces the fundamental biology of chromatin and epigenetics at an introductory level for non-biologists audiences. We then survey the landscape of computational approaches, from atomistic to systems-level models, and connect them to the experimental data that inform and validate them at an introductory level for non-computationalists. We argue that the next frontier will require us to build integrative models that can predict how molecular perturbations mechanistically alter cellular phenotypes, which will open a new era of chromatin-targeted therapeutics.

Chromatin Dynamics

Integrating multiscale mathematical modeling and multidimensional data reveals the effects of epigenetic instability on acquired drug resistance in cancer.

Biological and dynamic mechanisms by which Drug-tolerant persister (DTP) cells contribute to the development of acquired drug resistance have not been fully elucidated. Here, by integrating multidimensional data from drug-treated PC9 cells, we developed a novel multiscale mathematical model from an evolutionary perspective that encompasses epigenetic and cellular population dynamics. By coupling stochastic simulation with quantitative analysis, we identified epigenetic instability as the most prominent kinetic feature related to the emergence of DTP cell subpopulations and the effectiveness of intermittent treatment. Moreover, we revealed the optimal schedule for intermittent treatment, including the optimal area for therapeutic time and drug holidays. By leveraging single-cell RNA-seq data characterizing the drug tolerance of lung cancer, we validated the predictions made by our model and further revealed previously unrecognized biological features of DTP cells, such as cell autophagy and migration, as well as new biomarker genes of therapeutic tolerance. Our work not only provides a paradigm for the integration of multiscale mathematical models with newly emerging genomics data but also improves our understanding of the crucial roles of DTP cells and offers guidance for developing new intermittent treatment strategies against acquired drug resistance in cancer.

Drug Resistance, Neoplasm

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5​ concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution

Evolution and applications of genome-scale metabolic models in yeast systems biology studies.

Genome-scale metabolic models (GEMs) can be used to simulate the metabolic network of an organism in a systematic and holistic way. Different yeast species, including Saccharomyces cerevisiae, have emerged as powerful cell factories for bioproduction. Recently, with the dedicated efforts from the scientific community, significant progress has been made in the development of yeast GEMs. Numerous versions of yeast GEMs and the derived multiscale models have been released, facilitating integrative omics analysis and rational strain design for different types of yeast cell factories. These advancements reflected the evolution and maturation of yeast GEMs together with a model ecosystem around them. This review will summarize the development and expansion of yeast GEMs and discuss their applications in yeast systems biology studies. It is anticipated that yeast GEMs will continue to play an increasingly important role in pioneering yeast physiological and metabolic studies in coming years.

Systems Biology

Multi-level aggregation analysis of microbiome composition and host gene expression reveals associations with systemic and local immunity.

The human gut microbiome plays a critical role in immune regulation, yet the molecular links between microbiome composition and host gene expression remain incompletely understood. We analyzed associations between host gene expression and microbiome composition in a cohort of 315 healthy individuals, integrating microarray-based gene expression data from three intestinal sites (ileum, transverse colon, and rectum) and six immune cell types with microbiome sequencing data. Using a hierarchical feature aggregation strategy combining principal component analysis, clustering, and covariate correction, we discovered significant associations primarily related to immunity. While microbial profiles were similar across the three intestinal sites, the transverse colon yielded the most "microbiome-host gene expression" associations. Among the immune cell types, CD8+ cells showed the highest number of associations. The first principal component of microbiome composition, reflecting a gradient from commensals (e.g., Ruminococcaceae and Christensenellaceae) to proinflammatory taxa ([Ruminococcus] gnavus and Lachnoclostridium), correlated with the expression of TNF-α-linked genes (HMOX1, CPI17, HSD3B2, and SLC5A1). Among individual genera, Catenibacterium abundance was associated with gene expression in both intestinal and immune cells, including negative associations with MRPS21 (related to mitochondrial function) in the transverse colon and with CD8+ gene programs related to T cell differentiation. These findings align with emerging evidence implicating mitochondrial dysfunction in intestinal inflammation. Our results identify multi-level associations between the gut microbiome and host gene expression, suggesting potential mechanisms by which microbiota shape local and systemic immunity and vice versa. The implicated genes and taxa represent candidates for experimental validation to improve understanding of host-microbiome homeostasis and its disruption in disease.IMPORTANCEThe gut microbiome and immune system are engaged in a complex interplay throughout human life. While most associative studies focus on case-control comparisons-typically examining patients with conditions such as inflammatory bowel disease or metabolic diseases-less is known about the molecular links between the microbiome and immune system in healthy individuals. In this study of a large cohort of healthy individuals, we addressed this gap by applying multiscale modeling to tackle the high dimensionality of host-microbiome data. We identified multi-level associations between microbiome composition and host gene expression in both intestinal tissues and immune cells. These findings offer a valuable reference for understanding baseline host-microbiome communication and highlight molecular candidates-such as TNF-α-related genes and mitochondrial pathways-for future experimental validation.

Humans

Adapting systems biology to address the complexity of human disease in the single-cell era.

Systems biology aims to achieve holistic insights into the molecular workings of cellular systems through iterative loops of measurement, analysis and perturbation. This framework has had remarkable success in unicellular model organisms, and recent experimental and computational advances - from single-cell and spatial profiling to CRISPR genome editing and machine learning - have raised the exciting possibility of leveraging such strategies to prevent, diagnose and treat human diseases. However, adapting systems-inspired approaches to dissect human disease complexity is challenging, given that discrepancies between the biological features of human tissues and the experimental models typically used to probe function (which we term 'translational distance') can confound insight. Here we review how samples, measurements and analyses can be contextualized within overall multiscale human disease processes to mitigate data and representation gaps. We then examine ways to bridge the translational distance between systems-inspired human discovery loops and model system validation loops to empower precision interventions in the era of single-cell genomics.

Humans

Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models.

MOTIVATION: Understanding how the genome encodes the regulatory logic of transcription is a main challenge of the post-genomic era, and can be overcome with the aid of customized computational tools. RESULTS: We report an automated framework for analyzing an ensemble of fits to data of a thermodynamics-based sequence-level model for transcriptional regulation. The fits are clustered accordingly with their intrinsic regulatory logic. A multiscale analysis enables visualization of quantitative features resulting from the deconvolution of the regulatory profile provided by multiple transcription factors interacting with the locus of a gene. Quantitative experimental data on reporters driven by the whole locus of the even-skipped gene in the blastoderm of Drosophila embryos was used for validating our approach. A few clusters of highly active DNA binding sites within the enhancers collectively modulate even-skipped gene transcription. Analysis of variable enhancers' length shows the importance of bound protein-protein interactions for transcriptional regulation. The interplay between activation and quenching enables function conservation of enhancers despite length variations. AVAILABILITY AND IMPLEMENTATION: The transcription factor level data used for performing the reported study is accessible in the input files in Zenodo and GitHub as well the full code. Additional data from formerly FlyEx database will be available under request.

Thermodynamics

dcHiChIP: a comprehensive Nextflow-based pipeline for multiscale analysis of chromatin architecture from HiChIP data.

MOTIVATION: Despite the growing use of HiChIP to investigate protein-directed chromatin architecture, a comprehensive and reproducible pipeline for analysing these datasets-from raw reads to multiscale 3D genome features-remains lacking. Existing tools often focus on isolated components, such as loop calling or matrix generation, but fall short in integrating structural annotation, functional enrichment, and spatial modeling within a unified framework. To address this gap, we developed dcHiChIP, a modular, scalable Nextflow-based workflow that streamlines the analysis of HiChIP data, enabling both routine processing and in-depth exploration of chromatin organization and regulatory interactions. RESULTS: dcHiChIP enables robust and reproducible analysis of HiChIP datasets across multiple scales of chromatin architecture. It accepts raw sequencing data as input and generates high-quality loop calls, domain annotations, and 3D genome models. It also performs functional annotation and motif enrichment analyses. Applied to benchmark CTCF HiChIP datasets, dcHiChIP identifies major chromatin architectural features such as TADs/CCDs, A/B compartments, and chromatin stripes, and offers efficient, end-to-end execution with support for batch processing and workflow resumability. AVAILABILITY: dcHiChIP is publicly available on GitHub at https://github.com/SFGLab/dcHiChIP, with documentation at https://sfglab.github.io/dcHiChIP/. The software version used in this study is archived at Zenodo: https://doi.org/10.5281/zenodo.22030542.

Chromatin

An electrostatic repulsion model of centromere organisation.

During cell division, chromosomes reorganise into compact bodies in which centromeres localise precisely at the chromatin surface1-4 to enable kinetochore-microtubule interactions essential for genome segregation5-8. The physical principles guiding this centromere positioning remain unknown. Here, we reveal that human core centromeres are directed to the chromatin surface by repulsion of centromere-associated proteins - independent of condensin-mediated loop extrusion and microtubule engagement. Using cellular perturbations, biochemical reconstitution, and multiscale molecular dynamics simulations, we show that chromatin surface localisation emerges from repulsion between condensed chromatin and both the kinetochore and the highly negatively charged centromere protein, CENP-B. Together, these elements form a centromeric region composed of two domains with opposing affinities, one favouring integration within the mitotic chromosome and the other favouring exposure to the surrounding cytoplasm, thereby driving surface positioning. Tethering synthetic negatively charged proteins to chromatin was sufficient to recapitulate this surface localisation in cells and in vitro, indicating that electrostatic repulsion is a key determinant of surface localisation. These findings demonstrate that centromere layering is not hardwired by chromatin folding patterns but instead emerges from phase separation in chromatin. Our work uncovers electrostatic polarity as a general and programmable mechanism to spatially organise chromatin.

Journal Article

Analyzing Meiosis in Maize.

Meiosis is central to sexual reproduction and the main source of genetic diversity in plants. Understanding how meiotic processes are regulated has direct relevance to agriculture. As meiotic recombination is the vehicle of plant breeding, gaining the ability to influence recombination patterns can accelerate crop improvement. Maize is a powerful model for studying plant meiosis, thanks to its large chromosomes, well-developed genetics, and the availability of diverse cytogenetic and molecular tools. Insights gained from maize studies can extend to other species. In this review, we describe a variety of approaches for examining meiosis and meiotic recombination in maize. Cytological techniques, including protein immunolocalization and fluorescence in situ hybridization (FISH), enable visualization of chromosome structure and behavior, as well as crossover (CO) formation. Chromatin immunoprecipitation (ChIP) is used in meiosis research to determine locations of recombination proteins, identify recombination sites, and elucidate chromatin features, such as histone modifications. Quantification of COs at specific genomic sites through pollen typing by droplet digital PCR allows precise high-resolution measurement of recombination rates. Combining cytology, protein localization, and molecular assays provides a multiscale picture of meiosis, linking molecular mechanisms to chromosome behavior and, ultimately, to genetic variation.

Journal Article

Relative determination of W-values for alpha particles in tissue-equivalent and other gases.

W (the average energy to form an ion pair) for 5.4 MeV 241Am alpha particles in a Rossi-type tissue-equivalent (TE) gas, argon and methane was determined to an accuracy better than 0.2% using a new automated data handling system. A vibrating reed electrometer and current digitiser were used to measure the current produced by completely stopping the alpha particles in a large cylindrical ionisation chamber. A multichannel analyser, operating in a slow multiscaler mode, was used to store pulses from the current digitiser. The dwell time, of the order of 60 min per channel, was selected with an external timer gate. Current measurements were made at reduced pressures (200 Torr) to reduce ion recombination. The average current, over many repeated measurements, was compared to the current produced in nitrogen and its previously published W-value of 36.39+/-0.04 eV per ion pair. The resulting W-values were, in eV per ion pair, 26.29+/-0.05 for argon, 29.08+/-0.03 for methane and 30.72+/-0.04 for TE gas, which had an analysed composition of 64.6% methane, 32.4% CO2 and 2.7% nitrogen. Although the methane and argon values agree within 0.1% with previously published values, the value for TE is 1.2% lower than the single previously reported value.

Alpha Particles