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How is Big Data reshaping preclinical aging research?

Lab Animal volume 52, pages 289–314 (2023)Cite this article

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Abstract

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

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Fig. 1: Major driving forces underlying BD in preclinical aging research.
Fig. 2: Organism-level deep phenotyping.
Fig. 3: Common computer vision tasks as applied to preclinical aging research.
Fig. 4: Multilayered interactive network perspective visualization of living organisms as dynamic complex systems for understanding the spatiotemporal emergence of aging.
Fig. 5: Horizontal and vertical integration of omics layers.

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Acknowledgements

M.A.A. is an employee of Kelly Government Solutions. This work was supported by the Intramural Research Program of the National Institute on Aging/NIH.

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  1. These authors contributed equally: Maria Emilia Fernandez, Jorge Martinez-Romero.

Authors and Affiliations

  1. Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA

    Maria Emilia Fernandez, Jorge Martinez-Romero, Miguel A. Aon, Michel Bernier, Nathan L. Price & Rafael de Cabo

  2. Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA

    Jorge Martinez-Romero

  3. Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA

    Miguel A. Aon

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All authors were responsible for conceptualization and methodology. M.E.F., J.M.-R. and M.A.A. were responsible for data collection and writing the original draft. All authors reviewed and edited the manuscript.

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Sheet 1: Supplementary Table 1. Additional multi-omics studies in preclinical aging research in rodent models. Sheet 2: Supplementary Table 2. Number of PubMed articles retrieved with keywords representing the major driving forces in Fig. 1a, at global or preclinical aging research level.

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Fernandez, M.E., Martinez-Romero, J., Aon, M.A. et al. How is Big Data reshaping preclinical aging research?. Lab Anim 52, 289–314 (2023). https://doi.org/10.1038/s41684-023-01286-y

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