Transient crosslinking controls the condensate formation pathway within chromatin networks.

The network structure of densely packed chromatin within the nucleus of eukaryotic cells acts in concert with nonequilibrium processes. Using statistical physics simulations, we explore the control provided by transient crosslinking of the chromatin network by structural-maintenance-of-chromosome (SMC) proteins over (i) the physical properties of the chromatin network and (ii) condensate formation of embedded molecular species. We find that the density and lifetime of transient SMC crosslinks regulate structural relaxation modes and tune the sol-vs-gel state of the chromatin network, which imparts control over the kinetic pathway to condensate formation. Specifically, lower density, shorter-lived crosslinks induce sollike networks and a droplet-fusion pathway, whereas higher density, longer-lived crosslinks induce gellike networks and an Ostwald-ripening pathway.

Benchmarking the Immersed Boundary Method for Viscoelastic Flows.

We present and analyze a series of benchmark tests regarding the application of the immersed boundary (IB) method to viscoelastic flows through and around non-trivial, stationary geometries. The IB method is widely used to simulate biological fluid dynamics and other modeling scenarios in which a structure is immersed in a fluid. Although the IB method has been most commonly used to model systems involving viscous incompressible fluids, it also can be applied to visoelastic fluids, and has enabled the study of a wide variety of dynamical problems including the settling of vesicles and the swimming of elastic filaments in fluids modeled by the Oldroyd-B constitutive equation. In the viscoelastic context, however, relatively little work has explored the accuracy or convergence properties of this numerical scheme. Herein, we present benchmarking results for an IB solver applied to viscoelastic flows in and around non-trivial geometries using either the idealized Oldroyd-B constitutive model or the more physcially realistic, polymer-entanglementbased Rolie-Poly constitutive equations. We use two-dimensional numerical test cases along with results from rheology experiments to benchmark the IB method and compare it to more complex finite element and finite volume viscoelastic flow solvers. Additionally, we analyze different choices of regularized delta function and relative Lagrangian grid spacings which allow us to identify and recommend the key choices of these numerical parameters depending on the present flow regime.

Depletion Strategies for Crystallized Layers of Two-Dimensional Nanosheets to Enhance Lithium-Ion Conductivity in Polymer Nanocomposites.

The assembly of long-range aligned structures of two-dimensional nanosheets (2DNSs) in polymer nanocomposites (PNCs) is in urgent need for the design of nanoelectronics and lightweight energy-storage materials of high conductivity for electricity or heat. These 2DNS are thin and exhibit thermal fluctuations, leading to an intricate interplay with polymers in which entropic effects can be exploited to facilitate a range of different assemblies. In molecular dynamics simulations of experimentally studied 2DNSs, we show that the layer-forming crystallization of 2DNSs is programmable by regulating the strengths and ranges of polymer-induced entropic depletion attractions between pairs of 2DNSs, as well as between single 2DNSs and a substrate surface, by exclusively tuning the temperature and size of the 2DNS. Enhancing the temperature supports the 2DNS-substrate depletion rather than crystallization of 2DNSs in the bulk, leading to crystallized layers of 2DNSs on the substrate surfaces. On the other hand, the interaction range of the 2DNS-2DNS depletion attraction extends further than the 2DNS-substrate attraction whenever the 2DNS size is well above the correlation length of the polymers, which results in a nonmonotonic dependence of the crystallization layer on the 2DNS size. It is demonstrated that the depletion-tuned crystallization layers of 2DNSs contribute to a conductive channel in which individual lithium ions (Li ions) migrate efficiently through the PNCs. This work provides statistical and dynamical insights into the balance between the 2DNS-2DNS and 2DNS-substrate depletion interactions in polymer-2DNS composites and highlights the possibilities to exploit depletion strategies in order to engineer crystallization processes of 2DNSs and thus to control electrical conductivity.

Statistical Methods for Microrheology of Airway Mucus with Extreme Heterogeneity.

The mucus lining of the human airway epithelium contains two gel-forming mucins, MUC5B and MUC5AC. During progression of cystic fibrosis (CF), mucus hyper-concentrates as its mucin ratio changes, coinciding with formation of insoluble, dense mucus flakes. We explore rheological heterogeneity of this pathology with reconstituted mucus matching three stages of CF progression and particle-tracking of 200 nm and 1 micron diameter beads. We introduce statistical data analysis methods specific to low signal-to-noise data within flakes. Each bead time series is decomposed into: (i) a fractional Brownian motion (fBm) classifier of the pure time-series signal; (ii) high-frequency static and dynamic noise; and (iii) low-frequency deterministic drift. Subsequent analysis focuses on the denoised fBm classifier ensemble from each mucus sample and bead diameter. Every ensemble fails a homogeneity test, compelling clustering methods to assess levels of heterogeneity. The first binary level detects beads within vs. outside flakes. A second binary level detects within-flake bead signals that can vs. cannot be disentangled from the experimental noise floor. We show all denoised ensembles, within- and outside-flakes, fail a homogeneity test, compelling additional clustering; next, all clusters with sufficient data fail a homogeneity test. These levels of heterogeneity are consistent with outcomes from a stochastic phase-separation process, and dictate applying the generalized Stokes-Einstein relation to each bead per cluster per sample, then frequency-domain averaging to assess rheological heterogeneity. Flakes exhibit a spectrum of gel-like and sol-like domains, outside-flake solutions a spectrum of sol-like domains, painting a rheological signature of the phase-separation process underlying flake-burdened mucus.

Polymer Modeling Reveals Interplay between Physical Properties of Chromosomal DNA and the Size and Distribution of Condensin-Based Chromatin Loops.

Transient DNA loops occur throughout the genome due to thermal fluctuations of DNA and the function of SMC complex proteins such as condensin and cohesin. Transient crosslinking within and between chromosomes and loop extrusion by SMCs have profound effects on high-order chromatin organization and exhibit specificity in cell type, cell cycle stage, and cellular environment. SMC complexes anchor one end to DNA with the other extending some distance and retracting to form a loop. How cells regulate loop sizes and how loops distribute along chromatin are emerging questions. To understand loop size regulation, we employed bead-spring polymer chain models of chromatin and the activity of an SMC complex on chromatin. Our study shows that (1) the stiffness of the chromatin polymer chain, (2) the tensile stiffness of chromatin crosslinking complexes such as condensin, and (3) the strength of the internal or external tethering of chromatin chains cooperatively dictate the loop size distribution and compaction volume of induced chromatin domains. When strong DNA tethers are invoked, loop size distributions are tuned by condensin stiffness. When DNA tethers are released, loop size distributions are tuned by chromatin stiffness. In this three-way interaction, the presence and strength of tethering unexpectedly dictates chromatin conformation within a topological domain.

Modeling identifies variability in SARS-CoV-2 uptake and eclipse phase by infected cells as principal drivers of extreme variability in nasal viral load in the 48 h post infection.

The SARS-CoV-2 coronavirus continues to evolve with scores of mutations of the spike, membrane, envelope, and nucleocapsid structural proteins that impact pathogenesis. Infection data from nasal swabs, nasal PCR assays, upper respiratory samples, ex vivo cell cultures and nasal epithelial organoids reveal extreme variabilities in SARS-CoV-2 RNA titers within and between the variants. Some variabilities are naturally prone to clinical testing protocols and experimental controls. Here we focus on nasal viral load sensitivity arising from the timing of sample collection relative to onset of infection and from heterogeneity in the kinetics of cellular infection, uptake, replication, and shedding of viral RNA copies. The sources of between-variant variability are likely due to SARS-CoV-2 structural protein mutations, whereas within-variant population variability is likely due to heterogeneity in cellular response to that particular variant. With the physiologically faithful, agent-based mechanistic model of inhaled exposure and infection from (Chen et al., 2022), we perform statistical sensitivity analyses of the progression of nasal viral titers in the first 0-48 h post infection, focusing on three kinetic mechanisms. Model simulations reveal shorter latency times of infected cells (including cellular uptake, viral RNA replication, until the onset of viral RNA shedding) exponentially accelerate nasal viral load. Further, the rate of infectious RNA copies shed per day has a proportional influence on nasal viral load. Finally, there is a very weak, negative correlation of viral load with the probability of infection per virus-cell encounter, the model proxy for spike-receptor binding affinity.

The power of weak, transient interactions across biology: A paradigm of emergent behavior.

A growing list of diverse biological systems and their equally diverse functionalities provides realizations of a paradigm of emergent behavior. In each of these biological systems, pervasive ensembles of weak, short-lived, spatially local interactions act autonomously to convey functionalities at larger spatial and temporal scales. In this article, a range of diverse systems and functionalities are presented in a cursory manner with literature citations for further details. Then two systems and their properties are discussed in more detail: yeast chromosome biology and human respiratory mucus.

Computational Modeling Insights into Extreme Heterogeneity in COVID-19 Nasal Swab Data.

Throughout the COVID-19 pandemic, an unprecedented level of clinical nasal swab data from around the globe has been collected and shared. Positive tests have consistently revealed viral titers spanning six orders of magnitude! An open question is whether such extreme population heterogeneity is unique to SARS-CoV-2 or possibly generic to viral respiratory infections. To probe this question, we turn to the computational modeling of nasal tract infections. Employing a physiologically faithful, spatially resolved, stochastic model of respiratory tract infection, we explore the statistical distribution of human nasal infections in the immediate 48 h of infection. The spread, or heterogeneity, of the distribution derives from variations in factors within the model that are unique to the infected host, infectious variant, and timing of the test. Hypothetical factors include: (1) reported physiological differences between infected individuals (nasal mucus thickness and clearance velocity); (2) differences in the kinetics of infection, replication, and shedding of viral RNA copies arising from the unique interactions between the host and viral variant; and (3) differences in the time between initial cell infection and the clinical test. Since positive clinical tests are often pre-symptomatic and independent of prior infection or vaccination status, in the model we assume immune evasion throughout the immediate 48 h of infection. Model simulations generate the mean statistical outcomes of total shed viral load and infected cells throughout 48 h for each "virtual individual", which we define as each fixed set of model parameters (1) and (2) above. The "virtual population" and the statistical distribution of outcomes over the population are defined by collecting clinically and experimentally guided ranges for the full set of model parameters (1) and (2). This establishes a model-generated "virtual population database" of nasal viral titers throughout the initial 48 h of infection of every individual, which we then compare with clinical swab test data. Support for model efficacy comes from the sampling of infection dynamics over the virtual population database, which reproduces the six-order-of-magnitude clinical population heterogeneity. However, the goal of this study is to answer a deeper biological and clinical question. What is the impact on the dynamics of early nasal infection due to each individual physiological feature or virus-cell kinetic mechanism? To answer this question, global data analysis methods are applied to the virtual population database that sample across the entire database and de-correlate (i.e., isolate) the dynamic infection outcome sensitivities of each model parameter. These methods predict the dominant, indeed exponential, driver of population heterogeneity in dynamic infection outcomes is the latency time of infected cells (from the moment of infection until onset of viral RNA shedding). The shedding rate of the viral RNA of infected cells in the shedding phase is a strong, but not exponential, driver of infection. Furthermore, the unknown timing of the nasal swab test relative to the onset of infection is an equally dominant contributor to extreme population heterogeneity in clinical test data since infectious viral loads grow from undetectable levels to more than six orders of magnitude within 48 h.

A hybrid discrete-continuum model of immune responses to SARS-CoV-2 infection in the lung alveolar region, with a focus on interferon induced innate response.

We develop a lattice-based, hybrid discrete-continuum modeling framework for SARS-CoV-2 exposure and infection in the human lung alveolar region, or parenchyma, the massive surface area for gas exchange. COVID-19 pneumonia is alveolar infection by the SARS-CoV-2 virus significant enough to compromise gas exchange. The modeling framework orchestrates the onset and progression of alveolar infection, spatially and temporally, beginning with a pre-immunity baseline, upon which we superimpose multiple mechanisms of immune protection conveyed by interferons and antibodies. The modeling framework is tunable to individual profiles, focusing here on degrees of innate immunity, and to the evolving infection-replication properties of SARS-CoV-2 variant strains. The model employs partial differential equations for virion, interferon, and antibody concentrations governed by diffusion in the thin fluid coating of alveolar cells, species and lattice interactions corresponding to sources and sinks for each species, and multiple immune protections signaled by interferons. The spatial domain is a two-dimensional, rectangular lattice of alveolar type I (non-infectable) and type II (infectable) cells with a stochastic, species-concentration-governed, switching dynamics of type II lattice sites from healthy to infected. Once infected, type II cells evolve through three phases: an eclipse phase during which RNA copies (virions) are assembled; a shedding phase during which virions and interferons are released; and then cell death. Model simulations yield the dynamic spread of, and immune protection against, alveolar infection and viral load from initial sites of exposure. We focus in this paper on model illustrations of the diversity of outcomes possible from alveolar infection, first absent of immune protection, and then with varying degrees of four known mechanisms of interferon-induced innate immune protection. We defer model illustrations of antibody protection to future studies. Results presented reinforce previous recognition that interferons produced solely by infected cells are insufficient to maintain a high efficacy level of immune protection, compelling additional mechanisms to clear alveolar infection, such as interferon production by immune cells and adaptive immunity (e.g., T cells). This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Screening confinement of entanglements: Role of a self-propelling end inducing ballistic chain reptation.

Synthetic and natural nanomaterials with self-propelling mechanisms continue to be explored to boost chain mobility beyond normal reptation in the crowded environments of entangled chains. Here we employ scaling theory and numerical simulations to demonstrate that activating one chain end of a singular or isolated chain boosts entanglement-constrained chain reptation from the one-dimensional diffusive mobility as described by the de Gennes-Edwards-Doi model to ballistic motion along the entanglement tube contour. The active chain is effectively screened from the constraint of entanglements on length scales exceeding the tube size.

Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations.

The recent COVID-19 pandemic has propelled the field of aerosol science to the forefront, particularly the central role of virus-laden respiratory droplets and aerosols. The pandemic has also highlighted the critical need, and value for, an information bridge between epidemiological models (that inform policymakers to develop public health responses) and within-host models (that inform the public and health care providers how individuals develop respiratory infections). Here, we review existing data and models of generation of respiratory droplets and aerosols, their exhalation and inhalation, and the fate of infectious droplet transport and deposition throughout the respiratory tract. We then articulate how aerosol transport modeling can serve as a bridge between and guide calibration of within-host and epidemiological models, forming a comprehensive tool to formulate and test hypotheses about respiratory tract exposure and infection within and between individuals.

Mucus and mucus flake composition and abundance reflect inflammatory and infection status in cystic fibrosis.

BACKGROUND: Mucus hyperconcentration in cystic fibrosis (CF) lung disease is marked by increases in both mucin and DNA concentration. Additionally, it has been shown that half of the mucins present in bronchial alveolar lavage fluid (BALF) from preschool-aged CF patients are present in as non-swellable mucus flakes. This motivates us to examine the utility of mucus flakes, as well as mucin and DNA concentrations in BALF as markers of infection and inflammation in CF airway disease. METHODS: In this study, we examined the mucin and DNA concentration, as well as mucus flake abundance, composition, and biophysical properties in BALF from three groups; healthy adult controls, and two CF cohorts, one preschool aged and the other school aged. BALFs were characterized via refractometry, PicoGreen, immunofluorescence microscopy, particle tracking microrheology, and fluorescence image tiling. RESULTS: Mucin and DNA BALF concentrations increased progressively from healthy young adult controls to preschool-aged people and school-aged people with CF. Notably, mucin concentrations were increased in bronchoalveolar lavage fluid (BALF) from preschool-aged patients with CF prior to decreased pulmonary function. Infrequent small mucus flakes were identified in normal subjects. A progressive increase in the abundance of mucus flakes in preschool and school-aged CF patients was observed. Compositionally, MUC5B dominated flakes from normal subjects, whereas an increase in MUC5AC was observed in people with CF, reflected in a reduced flaked MUC5B/MUC5AC mucin ratio. CONCLUSION: These findings suggest mucus composition and flake properties are useful markers of inflammatory and infection-based changes in CF airways.

Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection.

Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate of infectious virions. We apply this baseline model in the absence of immune protection to explore immediate, short-term outcomes from novel SARS-CoV-2 depositions onto the air-ASL interface. For each RT location, we compute probability to clear versus infect; per infected cell, we compute dynamics of viral load and cell infection. Results reveal that nasal infections are highly likely within 1-2 days from minimal exposure, and alveolar pneumonia occurs only if infectious virions are deposited directly into alveolar ducts and sacs, not via retrograde propagation to the deep lung. Furthermore, to infect just 1% of the 140 m2 of alveolar surface area within 1 week, either 103 boluses each with 106 infectious virions or 106 aerosols with one infectious virion, all physically separated, must be directly deposited. These results strongly suggest that COVID-19 disease occurs in stages: a nasal/upper RT infection, followed by self-transmission of infection to the deep lung. Two mechanisms of self-transmission are persistent aspiration of infected nasal boluses that drain to the deep lung and repeated rupture of nasal aerosols from infected mucosal membranes by speaking, singing, or cheering that are partially inhaled, exhaled, and re-inhaled, to the deep lung.

A PBPK model recapitulates early kinetics of anti-PEG antibody-mediated clearance of PEG-liposomes.

PEGylation is routinely used to extend the systemic circulation of various protein therapeutics and nanomedicines. Nonetheless, mounting evidence is emerging that individuals exposed to select PEGylated therapeutics can develop antibodies specific to PEG, i.e., anti-PEG antibodies (APA). In turn, APA increase both the risk of hypersensitivity to the drug as well as potential loss of efficacy due to accelerated blood clearance of the drug. Despite the broad implications of APA, the timescales and systemic specificity by which APA can alter the pharmacokinetics and biodistribution of PEGylated drugs remain not well understood. Here, we developed a physiologically based pharmacokinetic (PBPK) model designed to resolve APA's impact on both early- and late-phase pharmacokinetics and biodistribution of intravenously administered PEGylated drugs. Our model accurately recapitulates PK and biodistribution data obtained from PET/CT imaging of radiolabeled PEG-liposomes and PEG-uricase in mice with and without APA, as well as serum levels of PEG-uricase in humans. Our work provides another illustration of the power of high-resolution PBPK models for understanding the pharmacokinetic impacts of anti-drug antibodies and the dynamics with which antibodies can mediate clearance of foreign species.

Simulating Dynamic Chromosome Compaction: Methods for Bridging In Silico to In Vivo.

The application of polymer models to chromosome structure and dynamics is a powerful approach for dissecting functional properties of the chromosome. The models are based on well-established bead-spring models of polymers and are distinct from molecular dynamics studies used in structural biology. In this work, we outline a polymer dynamics model that simulates budding yeast chromatin fibers in a viscous environment inside the nucleus using DataTank as a user interface for the C++ simulation. We highlight features for creating the nucleolus, a dynamic region of chromatin with protein-mediated, transient chromosomal cross-links, providing a predictive, stochastic polymer-physics model for versatile analyses of chromosome spatiotemporal organization. DataTank provides real-time visualization and data analytics methods during simulation. The simulation pipeline provides insights into the entangled chromosome milieu in the nucleus and creates simulated chromosome data, both structural and dynamic, that can be directly compared to experimental observations of live cells in interphase and mitosis.

Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery.

Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.

Chain stiffness boosts active nanoparticle transport in polymer networks.

Recent advances in technologies such as nanomanufacturing and nanorobotics have opened new pathways for the design of active nanoparticles (NPs) capable of penetrating biolayers for biomedical applications, e.g., for drug delivery. The coupling and feedback between active NP motility (with large stochastic increments relative to passive NPs) and the induced nonequilibrium deformation and relaxation responses of the polymer network, spanning scales from the NP to the local structure of the network, remain to be clarified. Using molecular dynamics simulations, combined with a Rouse mode analysis of network chains and position and velocity autocorrelation functions of the NPs, we demonstrate that the mobility of active NPs within cross-linked, concentrated polymer networks is a monotonically increasing function of chain stiffness, contrary to passive NPs, for which chain stiffness suppresses mobility. In flexible networks, active NPs exhibit a behavior similar to passive NPs, with a boost in mobility proportional to the self-propulsion force. These results are suggestive of design strategies for active NP penetration of stiff biopolymer matrices.

Molecular Dynamics Simulations to Explore the Structure and Rheological Properties of Normal and Hyperconcentrated Airway Mucus.

We develop the first molecular dynamics model of airway mucus based on the detailed physical properties and chemical structure of the predominant gel-forming mucin MUC5B. Our airway mucus model leverages the LAMMPS open-source code [https://lammps.sandia.gov], based on the statistical physics of polymers, from single molecules to networks. On top of the LAMMPS platform, the chemical structure of MUC5B is used to superimpose proximity-based, non-covalent, transient interactions within and between the specific domains of MUC5B polymers. We explore feasible ranges of hydrophobic and electrostatic interaction strengths between MUC5B domains with 9 nanometer spatial and 1 nanosecond temporal resolution. Our goal here is to propose and test a mechanistic hypothesis for a striking clinical observation with respect to airway mucus: a 10-fold increase in non-swellable, dense structures called flakes during progression of cystic fibrosis disease. Among the myriad possible effects that might promote self-organization of MUC5B networks into flake structures, we hypothesize and confirm that the clinically confirmed increase in mucin concentration, from 1.5 to 5 mg/mL, alone is sufficient to drive the structure changes observed with scanning electron microscopy images from experimental samples. We post-process the LAMMPS simulated datasets at 1.5 and 5 mg/mL, both to image the structure transition and compare with scanning electron micrographs and to show that the 3.33-fold increase in concentration induces closer proximity of interacting electrostatic and hydrophobic domains, thereby amplifying the proximity-based strength of the interactions.

Statistical mechanics of chromosomes: in vivo and in silico approaches reveal high-level organization and structure arise exclusively through mechanical feedback between loop extruders and chromatin substrate properties.

The revolution in understanding higher order chromosome dynamics and organization derives from treating the chromosome as a chain polymer and adapting appropriate polymer-based physical principles. Using basic principles, such as entropic fluctuations and timescales of relaxation of Rouse polymer chains, one can recapitulate the dominant features of chromatin motion observed in vivo. An emerging challenge is to relate the mechanical properties of chromatin to more nuanced organizational principles such as ubiquitous DNA loops. Toward this goal, we introduce a real-time numerical simulation model of a long chain polymer in the presence of histones and condensin, encoding physical principles of chromosome dynamics with coupled histone and condensin sources of transient loop generation. An exact experimental correlate of the model was obtained through analysis of a model-matching fluorescently labeled circular chromosome in live yeast cells. We show that experimentally observed chromosome compaction and variance in compaction are reproduced only with tandem interactions between histone and condensin, not from either individually. The hierarchical loop structures that emerge upon incorporation of histone and condensin activities significantly impact the dynamic and structural properties of chromatin. Moreover, simulations reveal that tandem condensin-histone activity is responsible for higher order chromosomal structures, including recently observed Z-loops.

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking.

The spatial structure and physical properties of the cytosol are not well understood. Measurements of the material state of the cytosol are challenging due to its spatial and temporal heterogeneity. Recent development of genetically encoded multimeric nanoparticles (GEMs) has opened up study of the cytosol at the length scales of multiprotein complexes (20-60 nm). We developed an image analysis pipeline for 3D imaging of GEMs in the context of large, multinucleate fungi where there is evidence of functional compartmentalization of the cytosol for both the nuclear division cycle and branching. We applied a neural network to track particles in 3D and then created quantitative visualizations of spatially varying diffusivity. Using this pipeline to analyze spatial diffusivity patterns, we found that there is substantial variability in the properties of the cytosol. We detected zones where GEMs display especially low diffusivity at hyphal tips and near some nuclei, showing that the physical state of the cytosol varies spatially within a single cell. Additionally, we observed significant cell-to-cell variability in the average diffusivity of GEMs. Thus, the physical properties of the cytosol vary substantially in time and space and can be a source of heterogeneity within individual cells and across populations.