Biological research and self-driving labs in deep space supported by artificial intelligence
Nature Machine Intelligence
Space biology research aims to understand fundamental spaceflight effects on organisms, develop foundational knowledge to support deep space exploration and, ultimately, bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data and model organisms from both spaceborne and ground-analogue studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally automated, light, agile and intelligent to accelerate knowledge discovery. Here we present a summary of decadal recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning and modelling applications that offer solutions to these space biology challenges. The integration of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modelling and analytics, support maximally automated and reproducible experiments, and efficiently manage spaceborne data and metadata, ultimately to enable life to thrive in deep space.
National Science Foundation
Lauren M. Sanders; Ryan T. Scott; Jason H. Yang; Amina Ann Qutub; Hector Garcia Martin; Daniel C. Berrios; Jaden J.A. Hastings; Jon Rask; Graham Mackintosh; Adrienne L. Hoarfrost; Stuart Chalk; John Kalantari; Kia Khezeli; Erik L. Antonsen; Joel Babdor; Richard Barker; Sergio E. Baranzini; Philip Heller; and For full author list, see comments below. "Biological research and self-driving labs in deep space supported by artificial intelligence" Nature Machine Intelligence (2023): 208-219. https://doi.org/10.1038/s42256-023-00618-4