Automated navigation of condensate phase behavior with active machine learning

Biomolecular condensates are essential cellular structures formed via biomacromolecule phase separation. Synthetic condensates allow for systematic engineering and understanding of condensate formation mechanisms and to serve as cell-mimetic platforms. Phase diagrams give comprehensive insight into...

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Vydané v:Nature communications Ročník 16; číslo 1; s. 9598 - 15
Hlavní autori: Leurs, Yannick H. A., van den Hout, Willem, Gardin, Andrea, van Dongen, Joost L. J., Rodriguez-Abetxuko, Andoni, Erkamp, Nadia A., van Hest, Jan C. M., Grisoni, Francesca, Brunsveld, Luc
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 30.10.2025
Nature Publishing Group
Nature Portfolio
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639/301/357/341
639/301/923/966
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639/638/455/953
639/638/563/606
Automation
Cellular structure
Condensates
Humanities and Social Sciences
Learning algorithms
Machine learning
Mapping
Microscopy
multidisciplinary
Navigation behavior
Phase diagrams
Phase separation
Polypeptides
Robotics
Science
Science (multidisciplinary)
ISSN:2041-1723, 2041-1723
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Popis
Shrnutí:Biomolecular condensates are essential cellular structures formed via biomacromolecule phase separation. Synthetic condensates allow for systematic engineering and understanding of condensate formation mechanisms and to serve as cell-mimetic platforms. Phase diagrams give comprehensive insight into phase separation behavior, but their mapping is time-consuming and labor-intensive. Here, we present an automated platform for efficiently mapping multi-dimensional condensate phase diagrams. The automated platform incorporates a pipetting system for sample formulation and an autonomous confocal microscope for particle property analysis. Active machine learning is used for iterative model improvement by learning from previous results and steering subsequent experiments towards efficient exploration of the binodal. The versatility of the pipeline is demonstrated by showcasing its ability to rapidly explore the phase behavior of various polypeptides, producing detailed and reproducible multidimensional phase diagrams. The self-driven platform also quantifies key condensate properties such as particle size, count, and volume fraction, adding functional insights to phase diagrams. Researchers developed a self-driving lab, including automated confocal imaging, that uses machine learning to rapidly map the phase behavior of biomolecular condensates.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-025-64617-2