Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
This work targets space biology researchers and agencies like NASA, proposing incremental improvements through AI integration to address specific challenges in deep space exploration.
The paper addresses the challenge of advancing space biology research beyond low Earth orbit by recommending the integration of artificial intelligence and machine learning to enhance autonomy, predictive modeling, and data management, with the goal of supporting sustained multi-planetary life.
Space biology research aims to understand fundamental effects of spaceflight 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-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space.