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The initial stage of zebrafish morphogenesis is characterized by a synchronous to asynchronous transition (SAT) in cell divisions. The cells divide in unison in the synchronous phase, unlike in the asynchronous phase. Despite the widespread observation of SAT in multiple organisms, there is no theoretical framework to predict the critical number of cell cycles $n^{*}$that mark the beginning of asynchronous division. Here, by probabilistically modeling cell cycle progression under the assumption that the distribution of cell division times is broadened, we predict the value of $n^{*}$and the time at which the SAT occurs. The theory, supplemented by agent-based simulations, supports the hypothesis that the SAT emerges as a consequence of biomechanical feedback on cell division. Our results are in excellent agreement with experiments while also explaining the cell cycle lengthening that arises as a result of biomechanical feedback. The emergence of the asynchronous phase is due to increasing fluctuations in the cell cycle times with each round of cell division. We also make several testable predictions that further shed light on the role of biomechanical feedback in growing multicellular systems, such as during tissue and tumor growth. 

Live-cell imaging experiments have shown that the distal dynamics between enhancers and promoters are unexpectedly rapid and incompatible with standard polymer models. The discordance between the compact static chromatin organization and dynamics is a conundrum that violates the expected structure–function relationship. We developed a theory to predict chromatin dynamics by accurately determining three-dimensional (3D) structures from static Hi-C contact maps or fixed-cell imaging data. Using the calculated 3D coordinates, the theory accurately forecasts experimentally observed two-point chromatin dynamics. It predicts rapid enhancer–promoter interactions and uncovers a scaling relationship between two-point relaxation time and genomic separation, closely matching recent measurements. The theory predicts that cohesin depletion accelerates single-locus diffusion while significantly slowing relaxation dynamics within topologically associating domains. Our results demonstrate that chromatin dynamics can be reliably inferred from static structural data, reinforcing the notion that 3D chromatin structure governs dynamic behavior. This general framework offers powerful tools for exploring chromatin dynamics across diverse biological contexts. 

The Random First-Order Transition (RFOT) theory predicts that transport proceeds by the cooperative movement of particles in domains, whose sizes increase as a liquid is compressed above a characteristic volume fraction, ϕd. The rounded dynamical transition around ϕd, which signals a crossover to activated transport, is accompanied by a growing correlation length that is predicted to diverge at the thermodynamic glass transition density (>ϕd). Simulations and imaging experiments probed the single particle dynamics of mobile particles in response to pinning all the particles in a semi-infinite space or randomly pinning (RP) a fraction of particles in a liquid at equilibrium. The extracted dynamic length increases non-monotonically with a peak around ϕd, which not only depends on the pinning method but is also different from ϕd of the actual liquid. This finding is at variance with the results obtained using the small wavelength limit of a four-point structure factor for unpinned systems. To obtain a consistent picture of the growth of the dynamic length, one that is impervious to the use of RP, we introduce a multiparticle structure factor, Smpc(q,t), that probes collective dynamics. The collective dynamical length, calculated from the small wave vector limit of Smpc(q,t), increases monotonically as a function of the volume fraction in a glass-forming binary mixture of charged colloidal particles in both unpinned and pinned systems. This prediction, which also holds in the presence of added monovalent salt, may be validated using imaging experiments. 

Understanding the biophysical basis of protein aggregation is important in biology because of the potential link to several misfolding diseases. Although experiments have shown that protein aggregates adopt a variety of morphologies, the dynamics of their formation are less well characterized. Here, we introduce a minimal model to explore the dependence of the aggregation dynamics on the structural and sequence features of the monomers. Using simulations, we demonstrate that sequence complexity (codified in terms of word entropy) and monomer rigidity profoundly influence the dynamics and morphology of the aggregates. Flexible monomers with low sequence complexity (corresponding to repeat sequences) form liquid-like droplets that exhibit ergodic behavior. Strikingly, these aggregates abruptly transition to more ordered structures, reminiscent of amyloid fibrils, when the monomer rigidity is increased. In contrast, aggregates resulting from monomers with high sequence complexity are amorphous and display nonergodic glassy dynamics. The heterogeneous dynamics of the low and high-complexity sequences follow stretched exponential kinetics, which is one of the characteristics of glassy dynamics. Importantly, at nonzero values of the bending rigidities, the aggregates age with the relaxation times that increase with the waiting time. Informed by these findings, we provide insights into aging dynamics in protein condensates and contrast the behavior with the dynamics expected in RNA repeat sequences. Our findings underscore the influence of the monomer characteristics in shaping the morphology and dynamics of protein aggregates, thus providing a foundation for deciphering the general rules governing the behavior of protein condensates. 

Local stresses in a tissue, a collective property, links cell division and dynamics. 

Abstract The preponderance of intrinsically disordered proteins (IDPs) in the eukaryotic proteome, and their ability to interact with each other, and with folded proteins, RNA, and DNA for functional purposes, have made it important to quantitatively characterize their biophysical properties. Toward this end, we developed the transferable self‐organized polymer (SOP‐IDP) model to calculate the properties of several IDPs. The values of the radius of gyration () obtained from SOP‐IDP simulations are in excellent agreement (correlation coefficient of 0.96) with those estimated from SAXS experiments. For AP180 and Epsin, the predicted values of the hydrodynamic radii () are in nearly quantitative agreement with those from fluorescence correlation spectroscopy (FCS) experiments. Strikingly, the calculated SAXS profiles for 36 IDPs are also nearly superimposable on the experimental profiles. The dependence of and the mean end‐to‐end distance () on chain length, , follows Flory's scaling law, ( and ), suggesting that globally IDPs behave as synthetic polymers in a good solvent. This finding depends on the solvent quality, which can be altered by changing variables such as pH and salt concentration. The values of and are 0.20 and 0.48 nm, respectively. Surprisingly, finite size corrections to scaling, expected on theoretical grounds, are negligible for and . In contrast, only by accounting for the finite sizes of the IDPs, the dependence of experimentally measurable on can be quantitatively explained using . Although Flory scaling law captures the estimates for , , and accurately, the spread of the simulated data around the theoretical curve is suggestive of of sequence‐specific features that emerge through a fine‐grained analysis of the conformational ensembles using hierarchical clustering. Typically, the ensemble of conformations partitions into three distinct clusters, having different equilibrium populations and structural properties. Without any further readjustments to the parameters of the SOP‐IDP model, we also obtained nearly quantitative agreement with paramagnetic relaxation enhancement (PRE) measurements forα‐synuclein. The transferable SOP‐IDP model sets the stage for several applications, including the study of phase separation in IDPs and interactions with nucleic acids.