We show evidence of the association of RNA Polymerase II (RNAP) with chromatin in a core-shell organization, reminiscent of microphase separation where the cores comprise dense chromatin and the shell, RNAP and chromatin with low density. These observations motivate our physical model for the regulation of core-shell chromatin organization. Here, we model chromatin as a multiblock copolymer, comprising active and inactive regions (blocks) that are both in poor solvent and tend to be condensed in the absence of binding proteins. However, we show that the solvent quality for the active regions of chromatin can be regulated by the binding of protein complexes (e.g. RNAP). Using the theory of polymer brushes, we find that such binding leads to swelling of the active chromatin regions which in turn, modifies the spatial organization of the inactive regions. In addition, we use simulations to study spherical chromatin micelles, whose cores comprise inactive regions and shells comprise active regions and bound protein complexes. In spherical micelles the swelling increases the number of inactive cores and controls their size. Thus, genetic modifications affecting the binding strength of chromatin-binding protein complexes may modulate the solvent quality experienced by chromatin and regulate the physical organization of the genome.
Nucleoli are surrounded by Peri-Centromeric Heterochromatin (PCH), reflecting a close spatial association between the two largest biomolecular condensates in eukaryotic nuclei. We have investigated how this highly conserved organization is established de novo during early Drosophila development and whether these distinct condensates influence each other's 3D organization. High-resolution live imaging revealed a highly dynamic process in which the PCH progressively surrounds nucleoli through a series of stage-specific intermediates. To assess interplay between the condensates, nucleolus assembly was eliminated by deleting the ribosomal RNA genes (rDNA), resulting in increased PCH compaction and subsequent reorganization to a hollow shell. In addition, in embryos lacking rDNA, some nucleolar proteins were abnormally redistributed into new bodies or 'neocondensates,' including enrichment in the PCH hollow core. These observations, combined with computational modeling, led to the hypothesis that nucleolar-PCH associations are mediated by a hierarchy of affinities between PCH, nucleoli, and 'amphiphilic' protein(s) that interact with both nucleolar and PCH components. We identified the nucleolar protein Pitchoune as a candidate for such an amphiphilic protein because it also contains a PCH-interaction motif and fills the PCH hollow core in embryos lacking rDNA. Together, these results unveil a dynamic program for establishing nucleolar-PCH associations during animal development, demonstrate that nucleoli are required for normal PCH organization, and identify Pitchoune as a likely molecular link for stabilizing PCH-nucleolar associations. Finally, we propose that disrupting affinity hierarchies could cause cellular disease phenotypes by liberating components that form 'neocondensates' or other abnormal structures through self-association and secondary affinities.
A case study testing the effectiveness of neural networks for permeability determination in heterogeneous media using basic rock properties is presented. The dataset used consists of 213 core samples from the Morrow and Viola formations in Kansas, United States. The characterizing parameters of the cores are porosity (ϕ), water and oil saturations (Sw and So), and grain density (GD), and the additional variables from well logs are induction resistivity (ILD), gamma ray (GR) and neutron-porosity (NPHI). The neural predictions are compared with permeability values obtained from three semi-empirical models (Timur, Coates, and Pape) widely used in reservoir characterization. It is concluded that the neural network provides the best overall prediction quantified by the highest correlation coefficients (R and R2) far above those achieved with conventional methods in formations with rock heterogeneity and complex diagenetic nature. Applying Timur’s method R was 0.58 and R2 was 0.343, for Coates’ model R was 0.60 and R2 0.365 and for Pape’s model R was 0.60 and R2 was 0.372, while for the neural model, 0.97 and 0.94 were obtained for R and R2, respectively.
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