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Showing 1–4 of 4 results
Advanced filters: Author: Sarah A. Fahlberg Clear advanced filters

  • Machine learning accelerates protein engineering by predicting sequence-function relationships. Here, authors evaluate neural network architectures’ ability to extrapolate beyond training data, finding simpler models excel in local design while convolutional models explore deeper sequence spaces.

    • Chase R. Freschlin
    • Sarah A. Fahlberg
    • Philip A. Romero
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-13

  • Barcoding individual cells with microparticles emitting near-infrared laser light enables the use of flow cytometry for the measurement of time-resolved single-cell dynamics with more markers and fewer colours.

    • Sheldon J. J. Kwok
    • Sarah Forward
    • Seok-Hyun Yun
    ResearchOpen Access
    Nature Biomedical Engineering
    Volume: 8, P: 310-324

  • Fatty acyl reductases (FARs) are critical enzymes in the biosynthesis of fatty alcohols and have the ability to directly acces acyl-ACP substrates. Here, authors couple machine learning-based protein engineering framework with gene shuffling to optimize a FAR for the activity on acyl-ACP and improve fatty alcohol production.

    • Jonathan C. Greenhalgh
    • Sarah A. Fahlberg
    • Philip A. Romero
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-10

  • A combinatorial approach using laser particles for optical barcoding enables distinguishing thousands to millions of cells. Theoretical framework and enhanced tagging pave the way for scalable single-cell tracking and analysis.

    • Nicola Martino
    • Hao Yan
    • Seok-Hyun Yun
    ResearchOpen Access
    Light: Science & Applications
    Volume: 14, P: 1-17