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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| Published in: | Nature communications Vol. 16; no. 1; pp. 9598 - 15 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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London
Nature Publishing Group UK
30.10.2025
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2041-1723, 2041-1723 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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. 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.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. Abstract 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. |
| ArticleNumber | 9598 |
| Author | Erkamp, Nadia A. van Hest, Jan C. M. van Dongen, Joost L. J. Gardin, Andrea Brunsveld, Luc Leurs, Yannick H. A. van den Hout, Willem Grisoni, Francesca Rodriguez-Abetxuko, Andoni |
| Author_xml | – sequence: 1 givenname: Yannick H. A. surname: Leurs fullname: Leurs, Yannick H. A. organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 2 givenname: Willem surname: van den Hout fullname: van den Hout, Willem organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 3 givenname: Andrea surname: Gardin fullname: Gardin, Andrea organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 4 givenname: Joost L. J. surname: van Dongen fullname: van Dongen, Joost L. J. organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 5 givenname: Andoni orcidid: 0000-0002-7652-1297 surname: Rodriguez-Abetxuko fullname: Rodriguez-Abetxuko, Andoni organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 6 givenname: Nadia A. surname: Erkamp fullname: Erkamp, Nadia A. organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 7 givenname: Jan C. M. orcidid: 0000-0001-7973-2404 surname: van Hest fullname: van Hest, Jan C. M. email: j.c.m.v.hest@tue.nl organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 8 givenname: Francesca orcidid: 0000-0001-8552-6615 surname: Grisoni fullname: Grisoni, Francesca email: f.grisoni@tue.nl organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology – sequence: 9 givenname: Luc orcidid: 0000-0001-5675-511X surname: Brunsveld fullname: Brunsveld, Luc email: l.brunsveld@tue.nl organization: Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41168205$$D View this record in MEDLINE/PubMed |
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