Biological research and self-driving labs in deep space supported by artificial intelligence

Biological research and self-driving labs in deep space supported by artificial intelligence. Sanders, L. M, Scott, R. T, Yang, J. H, Qutub, A. A., Garcia Martin, H., Berrios, D. C, Hastings, J. J A, Rask, J., Mackintosh, G., Hoarfrost, A. L, Chalk, S., Kalantari, J., Khezeli, K., Antonsen, E. L, Babdor, J., Barker, R., Baranzini, S. E, Beheshti, A., Delgado-Aparicio, G. M, Glicksberg, B. S, Greene, C. S, Haendel, M., Hamid, A. A, Heller, P., Jamieson, D., Jarvis, K. J, Komarova, S. V, Komorowski, M., Kothiyal, P., Mahabal, A., Manor, U., Mason, C. E, Matar, M., Mias, G. I, Miller, J., Myers, J. G, Nelson, C., Oribello, J., Park, S., Parsons-Wingerter, P., Prabhu, R K, Reynolds, R. J, Saravia-Butler, A., Saria, S., Sawyer, A., Singh, N. K., Snyder, M., Soboczenski, F., Soman, K., Theriot, C. A, Van Valen, D., Venkateswaran, K., Warren, L., Worthey, L., Zitnik, M., & Costes, S. V Technical Report 3, March, 2023.
abstract bibtex

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.

@techreport{Sanders2023-nz,
  title    = "Biological research and self-driving labs in deep space supported
              by artificial intelligence",
  author   = "Sanders, Lauren M and Scott, Ryan T and Yang, Jason H and Qutub, Amina Ann and Garcia Martin, Hector and Berrios, Daniel C and Hastings, Jaden J A and Rask, Jon and Mackintosh, Graham and Hoarfrost, Adrienne L and Chalk, Stuart and Kalantari, John and Khezeli, Kia and Antonsen, Erik L and Babdor, Joel and Barker, Richard and Baranzini, Sergio E and Beheshti, Afshin and Delgado-Aparicio, Guillermo M and Glicksberg, Benjamin S and Greene, Casey S and Haendel, Melissa and Hamid, Arif A and Heller, Philip and Jamieson, Daniel and Jarvis, Katelyn J and Komarova, Svetlana V and Komorowski, Matthieu and Kothiyal,
              Prachi and Mahabal, Ashish and Manor, Uri and Mason, Christopher
              E and Matar, Mona and Mias, George I and Miller, Jack and Myers,
              Jerry G and Nelson, Charlotte and Oribello, Jonathan and Park,
              Seung-Min and Parsons-Wingerter, Patricia and Prabhu, R K and
              Reynolds, Robert J and Saravia-Butler, Amanda and Saria, Suchi
              and Sawyer, Aenor and Singh, Nitin Kumar and Snyder, Michael and
              Soboczenski, Frank and Soman, Karthik and Theriot, Corey A and
              Van Valen, David and Venkateswaran, Kasthuri and Warren, Liz and
              Worthey, Liz and Zitnik, Marinka and Costes, Sylvain V",
  abstract = "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.",
  journal  = "Nature Machine Intelligence",
  volume   =  5,
  number   =  3,
  pages    = "208--219",
  month    =  mar,
  year     =  2023
}

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