Space biology research focuses on answering fundamental mechanistic questions about how molecular, cellular, tissue and whole organismal life responds to the space environment. Biological spaceflight stressors include ionizing radiation, altered gravitational fields, accelerated day-night cycles, confined isolation, hostile-closed environments, distance-duration from Earth, planetary dust-regolith, and extreme temperatures/atmospheres. 

Understanding, predicting, and mitigating these changes at all levels of biology is increasingly important, given the National Aeronautics and Space Administration's (NASA) deep exploration goals toward cis-Lunar and Mars missions.  

Space biology research has benefited from innovations in increasingly efficient and sensitive research technologies. Next-generation sequencing platforms, big data frameworks and computational libraries for data storage, processing and analysis have led to the ability to conduct groundbreaking clinical studies with multiple types of data collected across thousands of samples.  

A comprehensive effort to streamline and automate biological experimentation in space is needed to generate the large-scale, high-quality, AI-ready, reproducible datasets required to expand and validate our scientific understanding and knowledge base meaningfully.  

AI and Space Biology  

The ultimate goal of space biological research is to be able to predict the effects of spaceflight at all physiological levels within diverse living systems, then develop the building blocks to support life and bioengineer the foundations for sustained life beyond Earth. Predictive modelling and bioengineering will only be possible once we can model all parts of living systems, introduce perturbations, and measure genetic, cellular and physiological outcomes longitudinally.  

Building on automated, robotic and longitudinal data capture capabilities, space biology research will benefit from developing predictive models of whole organisms ("digital twins"), which integrate multi-scale mechanistic mathematical modelling of an entire complex organism, from genes to cells to tissues to organs. There now exist whole-cell computational models of microbes Mycoplasma genitalium and Escherichia coli for cellular predictions, and the ongoing Physiome Project develops mathematical models of the human body, from cells to tissues to organs, integrating chemical, metabolic, cellular and anatomical information  

Digital twins will be critical for advancing several goals relevant to human space exploration. They will help predict the effects of spaceflight hazards on human physiology and for designing interventions or predicting the outcomes of countermeasures. They will enable the development of predictive models that can be personalized to individual human astronauts based on unique differences in genetics or physiology. Moreover, they can serve as valuable scaffolds for extrapolation across organisms of different species through transfer learning.  

AI will be required for the scalable design of engineered cells and tissues. Cell and tissue engineering activities are currently predicated on a design-build-test-learning (DBTL) cycle, which requires tremendous human input and time. AI is beginning to transform these bioengineering activities by automating and scaling DBTL cycles.  

Make AI Space-Ready  

While much of the automation discussed above is already in use terrestrially, it is essential to note that these hardware and software are not immediately suitable for spaceflight research. Steps must be taken to convert and develop these automated systems for use in flight, likely following a logical progression of deployment (sub-orbital, ISS/LEO, Lunar Gateway, Lunar surface, Mars transit, Martian surface, etc.). These spaceflight-ready automated systems will enable the cost-effective collection of extensive biological data in difficult or constrained conditions.  

The next step is to couple automation with AI-assisted or AI-driven hypothesis generation and experimental design to facilitate the automatic generation of biological insights over time without human input and expertise (self-driving labs). By benefiting from the high reproducibility of machines, we envision a future where automatic data and metadata acquisition will be complete and unambiguous such that AI methods will be able to accumulate such information, constantly learning from them and making the whole more significant than the sum of the parts.  

The next decade of investment must focus on adapting and implementing existing methods with a specific focus on space biology. Transfer learning and generative modelling are promising for space biology research. Still, care must be taken to adapt these methods with the constraints of spaceflight in mind.  

Some recommendations  

Full adoption of AI/ML methods in space biological research is an endeavour that will span the next decade but will ultimately revolutionize the way we perform experiments and analyze data for knowledge gain. Here are some recommendations for the adoption of AI in space biology:  

• AI-assisted automated experimental platforms and self-driving labs in spaceflight  
• Standards for AI-readiness for diverse biological data types  
• Open Science data management environment to standardize, organize, and analyze all space biological data, both Earth- and space-generated  
• Adaptation of existing AI/ML methods and development of new methods best suited for space biology data  
• Digital twins for prediction of biological systems response to space environments