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Data-driven discovery and parameter estimation of mathematical models in biological pattern formation.

PLoS Comput Biol

January 2025

Department of Anatomy and Cell Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.

Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters.

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Copy-number variants (CNVs) are an important class of genetic variation that can mediate rapid adaptive evolution. Whereas CNVs can increase the relative fitness of the organism, they can also incur a cost due to the associated increased gene expression and repetitive DNA. We previously evolved populations of Saccharomyces cerevisiae over hundreds of generations in glutamine-limited (Gln-) chemostats and observed the recurrent evolution of CNVs at the GAP1 locus.

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Eukaryotic genome size varies considerably, even among closely related species. The causes of this variation are unclear, but weak selection against supposedly costly "extra" genomic sequences has been central to the debate for over 50 years. The mutational hazard hypothesis, which focuses on the increased mutation rate to null alleles in superfluous sequences, is particularly influential, though challenging to test.

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Conventional methods for extracting rare earth metals (REMs) from mined mineral ores are inefficient, expensive, and environmentally damaging. Recent discovery of lanmodulin (LanM), a protein that coordinates REMs with high-affinity and selectivity over competing ions, provides inspiration for new REM refinement methods. Here, we used quantum mechanical (QM) methods to investigate trivalent lanthanide cation (Ln) interactions with coordination systems representing bulk solvent water and protein binding sites.

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Background: Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Technical-Support using Artificial Intelligence), a novel digital platform, was introduced. This artificial intelligence (AI)-based network provides comprehensive technical support for the real-time sharing of medical information at the prehospital stage.

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