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      Dynamical control enables the formation of demixed biomolecular condensates

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          Abstract

          Cellular matter can be organized into compositionally distinct biomolecular condensates. For example, in Ashbya gossypii, the RNA-binding protein Whi3 forms distinct condensates with different RNA molecules. Using criteria derived from a physical framework for explaining how compositionally distinct condensates can form spontaneously via thermodynamic considerations, we find that condensates in vitro form mainly via heterotypic interactions in binary mixtures of Whi3 and RNA. However, within these condensates, RNA molecules become dynamically arrested. As a result, in ternary systems, simultaneous additions of Whi3 and pairs of distinct RNA molecules lead to well-mixed condensates, whereas delayed addition of an RNA component results in compositional distinctness. Therefore, compositional identities of condensates can be achieved via dynamical control, being driven, at least partially, by the dynamical arrest of RNA molecules. Finally, we show that synchronizing the production of different RNAs leads to more well-mixed, as opposed to compositionally distinct condensates in vivo.

          Abstract

          In this work, the authors report that protein-RNA condensates with shared proteins and distinct RNAs can form and persist in vitro and in cells as distinct entities if the nonshared RNA molecules are dynamically arrested, but the shared protein components are dynamically exchangeable.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            scikit-image: image processing in Python

            scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.
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              LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

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                Author and article information

                Contributors
                amy.gladfelter@duke.edu
                pappu@wustl.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 November 2023
                24 November 2023
                2023
                : 14
                : 7678
                Affiliations
                [1 ]Division of Biology and Biomedical Sciences, Plant and Microbial Biosciences, Washington University in St. Louis, ( https://ror.org/01yc7t268) St. Louis, MO 63130 USA
                [2 ]Department of Biomedical Engineering and Center for Biomolecular Condensates, James F. McKelvey School of Engineering, Washington University in St. Louis, ( https://ror.org/01yc7t268) St. Louis, MO 63130 USA
                [3 ]Department of Cell Biology, Duke University, ( https://ror.org/00py81415) Durham, NC 27708 USA
                [4 ]Yusuf Hamied Department of Chemistry, Centre for Misfolding Diseases, University of Cambridge, Lensfield Road, ( https://ror.org/013meh722) Cambridge, CB2 1EW UK
                Author information
                http://orcid.org/0000-0002-7877-6285
                http://orcid.org/0000-0003-3240-1856
                http://orcid.org/0000-0003-1505-4805
                http://orcid.org/0000-0003-4095-2105
                http://orcid.org/0000-0001-9719-2552
                http://orcid.org/0000-0003-2568-1378
                Article
                43489
                10.1038/s41467-023-43489-4
                10667521
                37996438
                4bdf8d4c-2f02-4d36-9e0f-b4eb41f50465
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 August 2023
                : 10 November 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000181, United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research);
                Award ID: FA9550-20-1-0241
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000009, Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.);
                Award ID: R01NS121114
                Award ID: F32GM146418-01A1
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                computational biophysics,nucleus
                Uncategorized
                computational biophysics, nucleus

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