Learning the organizational principles of biological systems using AI

Christopher Soelistyo; Kristina Ulicna; Marjan Famili; Camila Rangel-Smith; Guillaume Charras; Alan Lowe

doi:10.1117/12.3007844

13 March 2024 Learning the organizational principles of biological systems using AI

Christopher Soelistyo, Kristina Ulicna, Marjan Famili, Camila Rangel-Smith, Guillaume Charras, Alan Lowe

Proceedings Volume PC12853, High-Speed Biomedical Imaging and Spectroscopy IX; PC1285305 (2024) https://doi.org/10.1117/12.3007844
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Artificial Intelligence (AI) is an increasingly important tool in the biological sciences. Here, we demonstrate our ongoing efforts to develop AI methods to directly decode the organizational principles of biological systems from large microscopy datasets. Examples include machine learning approaches that can be utilized to build multiscale maps of biological systems, to derive new quantitative insight from timelapse microscopy data and to build predictive models of cell fate in complex biological tissues. In exploring these ideas, we hope to enable a new platform for automated scientific hypothesis generation and directed experimental studies.

Conference Presentation

Citation Download Citation

Christopher Soelistyo, Kristina Ulicna, Marjan Famili, Camila Rangel-Smith, Guillaume Charras, and Alan Lowe "Learning the organizational principles of biological systems using AI", Proc. SPIE PC12853, High-Speed Biomedical Imaging and Spectroscopy IX, PC1285305 (13 March 2024); https://doi.org/10.1117/12.3007844

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