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Showing 1–9 of 9 results
Advanced filters: Author: Diego A. Oyarzún Clear advanced filters

  • Gene deletions alter cellular physiology in complex and poorly understood ways. Here, authors present a machine learning strategy to predict the impact of metabolic gene deletions, offering top predictive accuracy for gene essentiality across varied organisms and adaptable to many other phenotypes.

    • Charlotte Merzbacher
    • Oisin Mac Aodha
    • Diego A. Oyarzún
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-9

  • Cellular senescence is involved in many disease processes but few senolytic compounds are currently known. Here, the authors report the discovery of three senolytics using machine learning models trained solely on published data, with large reductions in drug screening costs.

    • Vanessa Smer-Barreto
    • Andrea Quintanilla
    • Diego A. Oyarzún
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-15

  • Synthetic biology often involves engineering microbial strains to express high-value proteins. Here the authors build deep learning predictors of protein expression from sequence that deliver accurate models with fewer data than previously assumed, helping to lower costs of model-driven strain design.

    • Evangelos-Marios Nikolados
    • Arin Wongprommoon
    • Diego A. Oyarzún
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-12

  • Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, blood protein markers of brain health are integrated with omics data to reveal DNA methylation differences that associate with these protein markers.

    • Danni A. Gadd
    • Robert F. Hillary
    • Riccardo E. Marioni
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-14

  • Synthetic biology uses cells as its computing substrate, often based on the genetic circuit concept. In this Perspective, the authors argue that existing synthetic biology approaches based on classical models of computation limit the potential of biocomputing, and propose that living organisms have under-exploited capabilities.

    • Lewis Grozinger
    • Martyn Amos
    • Angel Goñi-Moreno
    ReviewsOpen Access
    Nature Communications
    Volume: 10, P: 1-11

  • Mona Tonn et al. propose a stochastic model to elucidate the mechanisms by which non-genetic heterogeneity arises in metabolic reactions. They find that even unimodal enzyme expression fluctuations can lead to highly heterogeneous metabolite profiles with two or more metabolically distinct subpopulations of cells.

    • Mona K. Tonn
    • Philipp Thomas
    • Diego A. Oyarzún
    ResearchOpen Access
    Communications Biology
    Volume: 2, P: 1-9