[Paper Review] Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

Lauren Sanders, Jason H. Yang+54 more|arXiv (Cornell University)|Dec 22, 2021

Spaceflight effects on biology129 references4 citations

TL;DR

This paper proposes integrating artificial intelligence (AI) and self-driving lab technologies into space biology research to enable autonomous, intelligent, and scalable experimentation beyond low Earth orbit. By leveraging AI for predictive modeling, data management, and experimental control, the framework aims to deepen understanding of spaceflight's biological effects and support sustainable multi-planetary life through intelligent, adaptive, and reproducible biological systems.

ABSTRACT

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.

Motivation & Objective

• To address the limitations of current biological research platforms in deep space missions by enabling autonomous, intelligent, and scalable experimentation.
• To overcome the constraints of human-in-the-loop experimentation in long-duration, remote space missions by developing AI-driven, self-sufficient research systems.
• To enhance data management and knowledge discovery in space biology by applying machine learning to spaceborne data and metadata.
• To support the long-term goal of sustaining human, plant, microbial, and animal life in closed-loop habitats through AI-optimized biological engineering.
• To establish a roadmap for integrating AI, machine learning, and modeling into space biology through a multidisciplinary workshop and consensus recommendations.

Proposed method

• Conducting a NASA-organized workshop with 325 experts across space biology, AI, and systems engineering to identify key challenges and opportunities.
• Developing a framework for self-driving labs that integrate AI for real-time decision-making, experimental design, and adaptive control in space environments.
• Applying machine learning models to analyze complex, multi-omics, and physiological data from spaceflight and analog studies to predict biological responses.
• Designing autonomous platforms that minimize mass, power, and crew time by embedding AI for experiment execution, monitoring, and optimization.
• Creating interoperable data standards and metadata frameworks to ensure traceability, reproducibility, and scalability of biological experiments in space.
• Integrating predictive modeling and digital twin technologies to simulate and optimize life support systems and biological processes in silico before in-flight deployment.

Experimental results

Research questions

• RQ1 How can AI and machine learning accelerate biological discovery in deep space missions where human oversight is limited?
• RQ2 What technical and operational frameworks are needed to enable fully autonomous, self-driving biological laboratories beyond low Earth orbit?
• RQ3 How can AI improve the reproducibility, efficiency, and scalability of space biology experiments under resource-constrained conditions?
• RQ4 What role can predictive modeling and digital twins play in simulating and optimizing life support systems for multi-planetary habitats?
• RQ5 How can AI-driven data pipelines manage and extract insights from heterogeneous, high-dimensional biological data collected in space?

Key findings

• The integration of AI into space biology enables predictive modeling of biological responses to spaceflight, significantly reducing reliance on trial-and-error experimentation.
• Self-driving labs powered by AI can autonomously design, execute, and adapt experiments in real time, minimizing the need for ground intervention and crew time.
• AI-driven data management systems improve the traceability, reproducibility, and interoperability of spaceborne biological data across diverse platforms and missions.
• Machine learning models applied to multi-omics and physiological data from space and analog studies reveal novel biological signatures of microgravity and radiation exposure.
• The proposed framework supports the development of closed-loop, bioengineered life support systems capable of sustaining human and non-human organisms in deep space habitats.
• A consensus roadmap was established for AI adoption in space biology, emphasizing autonomy, scalability, and cross-disciplinary collaboration across 325 contributing experts.