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

被引：17

作者：

Sanders, Lauren M. ^{[1
]}

Scott, Ryan T. ^{[2
]}

Yang, Jason H. ^{[3
]}

Qutub, Amina Ann ^{[4
]}

Garcia Martin, Hector ^{[5
,6
,7
]}

Berrios, Daniel C. ^{[2
]}

Hastings, Jaden J. A. ^{[8
]}

Rask, Jon ^{[9
]}

Mackintosh, Graham ^{[10
]}

Hoarfrost, Adrienne L. ^{[11
,12
]}

Chalk, Stuart ^{[13
]}

Kalantari, John ^{[14
]}

Khezeli, Kia ^{[14
]}

Antonsen, Erik L. ^{[15
]}

Babdor, Joel ^{[16
,17
]}

Barker, Richard ^{[18
]}

Baranzini, Sergio E. ^{[19
]}

Beheshti, Afshin ^{[2
]}

Delgado-Aparicio, Guillermo M. ^{[20
]}

Glicksberg, Benjamin S. ^{[21
]}

Greene, Casey S. ^{[22
]}

Haendel, Melissa ^{[23
]}

Hamid, Arif A. ^{[24
]}

Heller, Philip ^{[25
]}

Jamieson, Daniel ^{[26
]}

Jarvis, Katelyn J. ^{[27
]}

Komarova, Svetlana V. ^{[28
]}

Komorowski, Matthieu ^{[29
]}

Kothiyal, Prachi ^{[30
]}

Mahabal, Ashish ^{[31
]}

Manor, Uri ^{[32
]}

Mason, Christopher E. ^{[8
]}

Matar, Mona ^{[33
]}

Mias, George I. ^{[34
]}

Miller, Jack ^{[2
]}

Myers Jr, Jerry G. ^{[33
]}

Nelson, Charlotte ^{[19
]}

Oribello, Jonathan ^{[1
]}

Park, Seung-min ^{[35
]}

Parsons-Wingerter, Patricia ^{[36
]}

Prabhu, R. K. ^{[37
]}

Reynolds, Robert J. ^{[38
]}

Saravia-Butler, Amanda ^{[39
]}

Saria, Suchi ^{[40
,41
]}

Sawyer, Aenor ^{[27
]}

Singh, Nitin Kumar ^{[42
]}

Snyder, Michael ^{[43
]}

Soboczenski, Frank ^{[44
]}

Soman, Karthik ^{[19
]}

Theriot, Corey A. ^{[45
,46
]}

机构：

[1] NASA, Blue Marble Space Inst Sci, Space Biosci Div, Ames Res Ctr, Moffett Field, CA USA

[2] NASA, Space Biosci Div, KBR, Ames Res Ctr, Moffett Field, CA USA

[3] Rutgers New Jersey Med Sch, Ctr Emerging & Reemerging Pathogens, Dept Microbiol Biochem & Mol Genet, Newark, NJ USA

[4] Univ Texas San Antonio, Dept Biomed Engn, AI MATRIX Consortium, San Antonio, TX USA

[5] Lawrence Berkeley Natl Lab, Biol Syst & Engn Div, Berkeley, CA USA

[6] DOE Agile BioFoundry, Emeryville, CA USA

[7] Joint BioEnergy Inst, Emeryville, CA USA

[8] Weill Cornell Med, Dept Physiol & Biophys, New York, NY USA

[9] NASA, Off Ctr Director, Ames Res Ctr, Moffett Field, CA USA

[10] NASA, Bay Area Environm Res Inst, Ames Res Ctr, Moffett Field, CA USA

[11] Oak Ridge Assoc Univ ORAU, NASA, Space Biosci Div, Ames Res Ctr, Moffett Field, CA USA

[12] Univ Georgia, Dept Marine Sci, Athens, GA USA

[13] Univ North Florida, Dept Chem, Jacksonville, FL USA

[14] Mayo Clin, Ctr Individualized Med, Dept Surg, Dept Quantitat Hlth Sci, Rochester, MN USA

[15] Baylor Coll Med, Ctr Space Med, Dept Emergency Med, Houston, TX USA

[16] Univ Penn, Dept Syst Pharmacol & Translat Therapeut, Philadelphia, PA USA

[17] Univ Penn, Inst Immunol, Perelman Sch Med, Philadelphia, PA USA

[18] Univ Wisconsin Madison, Gilroy Astrobiol Res Grp, Madison, WI USA

[19] Univ Calif San Francisco, Weill Inst Neurosci, Dept Neurol, San Francisco, CA USA

[20] Georgia Inst Technol, Data Sci Analyt, Lima, Peru

[21] Icahn Sch Med Mt Sinai, Hasso Plattner Inst Digital Hlth Mt Sinai, Dept Genet & Genom Sci, New York, NY USA

[22] Univ Colorado, Ctr Hlth AI, Dept Biomed Informat, Sch Med, Anschutz Med Campus, Aurora, CO USA

[23] Univ Colorado, Ctr Hlth AI, Sch Med, Anschutz Med Campus, Aurora, CO USA

[24] Univ Minnesota, Dept Neurosci, Minneapolis, MN USA

[25] San Jose State Univ, Coll Sci, Dept Comp Sci, San Jose, CA USA

[26] Biorelate, Manchester, England

[27] Univ Calif San Francisco, Dept Orthopaed Surg, UC Space Hlth, San Francisco, CA USA

[28] McGill Univ, Fac Dent Med & Oral Hlth Sci, Montreal, PQ, Canada

[29] Imperial Coll London, Fac Med, Dept Surg & Canc, London, England

[30] NASA, SymbioSeq, Johnson Space Ctr, Ashburn, VA USA

[31] CALTECH, Ctr Data Driven Discovery, Pasadena, CA USA

[32] Salk Inst Biol Studies, Waitt Adv Biophoton Ctr, Chan Zuckerberg Imaging Sci, La Jolla, CA USA

[33] NASA, Human Res Program Cross Cutting Computat Modeling, John H Glenn Res Ctr, Cleveland, OH USA

[34] Michigan State Univ, Inst Quantitat Hlth Sci & Engn, Dept Biochem & Mol Biol, E Lansing, MI USA

[35] Stanford Univ, Dept Urol, Dept Radiol, Sch Med, Stanford, CA USA

[36] NASA, Low Explorat Grav Technol, John H Glenn Res Ctr, Cleveland, OH USA

[37] Univ Space Res Assoc USRA, NASA, Human Res Program Cross Cutting Computat Modeling, John H Glenn Res Ctr, Cleveland, OH USA

[38] Mortal Res & Consulting, Houston, TX USA

[39] NASA, Space Biosci Div, Logyx, Ames Res Ctr, Moffett Field, CA USA

[40] Johns Hopkins Univ, Comp Sci Stat & Hlth Policy, Baltimore, MD USA

[41] Bayesian Hlth, ML AI & Healthcare Lab, New York, NY USA

[42] Jet Prop Lab, Biotechnol & Planetary Protect Grp, Pasadena, CA USA

[43] Stanford Sch Med, Dept Genet, Stanford, CA USA

[44] Kings Coll London, Med Fac, SPHES, London, England

[45] Univ Texas Med Branch, Dept Prevent Med & Community Hlth, Galveston, TX USA

[46] NASA Johnson Space Ctr, Human Hlth & Performance Directorate, Houston, TX USA

[47] CALTECH, Dept Biol, Pasadena, CA USA

[48] ISS Natl Lab, Ctr Advancement Sci Space, Melbourne, FL USA

[49] Univ Alabama Birmingham, UAB Ctr Computat Biol & Data Sci, Birmingham, AL USA

[50] Harvard Univ, Broad Inst MIT & Harvard, Harvard Med Sch, Dept Biomed Informat,Harvard Data Sci, Boston, MA USA

来源：

NATURE MACHINE INTELLIGENCE | 2023年 / 5卷 / 03期

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会;

关键词：

DATA RESOURCES; BIG DATA; SPACEFLIGHT; PHENOTYPE; SYSTEM; EXPERIMENTATION; REPRODUCIBILITY; INTEGRATION; EXPRESSION; CHALLENGES;

D O I：

10.1038/s42256-023-00618-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Deep space exploration missions will require new technologies that can support astronaut health systems, as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this second of two Review articles based on the findings from the workshop, the intersection between artificial intelligence and space biology is discussed. 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.

引用

页码：208 / 219

页数：12

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