Beyond Low Earth Orbit: Biological Research, Artificial Intelligence,
and Self-Driving Labs
- URL: http://arxiv.org/abs/2112.12582v1
- Date: Wed, 22 Dec 2021 05:18:26 GMT
- Title: Beyond Low Earth Orbit: Biological Research, Artificial Intelligence,
and Self-Driving Labs
- Authors: Lauren M. Sanders (1), Jason H. Yang (2), Ryan T. Scott (3), Amina Ann
Qutub (4), Hector Garcia Martin (5 and 6 and 7), Daniel C. Berrios (3), Jaden
J.A. Hastings (8), Jon Rask (9), Graham Mackintosh (10), Adrienne L.
Hoarfrost (11), Stuart Chalk (12), John Kalantari (13), Kia Khezeli (13),
Erik L. Antonsen (14), Joel Babdor (15), Richard Barker (16), Sergio E.
Baranzini (17), Afshin Beheshti (3), Guillermo M. Delgado-Aparicio (18),
Benjamin S. Glicksberg (19), Casey S. Greene (20), Melissa Haendel (21), Arif
A. Hamid (22), Philip Heller (23), Daniel Jamieson (24), Katelyn J. Jarvis
(25), Svetlana V. Komarova (26), Matthieu Komorowski (27), Prachi Kothiyal
(28), Ashish Mahabal (29), Uri Manor (30), Christopher E. Mason (8), Mona
Matar (31), George I. Mias (32), Jack Miller (3), Jerry G. Myers Jr. (31),
Charlotte Nelson (17), Jonathan Oribello (1), Seung-min Park (33), Patricia
Parsons-Wingerter (34), R. K. Prabhu (35), Robert J. Reynolds (36), Amanda
Saravia-Butler (37), Suchi Saria (38 and 39), Aenor Sawyer (24), Nitin Kumar
Singh (40), Frank Soboczenski (41), Michael Snyder (42), Karthik Soman (17),
Corey A. Theriot (43 and 44), David Van Valen (45), Kasthuri Venkateswaran
(40), Liz Warren (46), Liz Worthey (47), Marinka Zitnik (48), Sylvain V.
Costes (49) ((1) Blue Marble Space Institute of Science, Space Biosciences
Division, NASA Ames Research Center, Moffett Field, CA, USA., (2) Center for
Emerging and Re-Emerging Pathogens, Department of Microbiology, Biochemistry
and Molecular Genetics, Rutgers New Jersey Medical School, Newark, NJ, USA.,
(3) KBR, Space Biosciences Division, NASA Ames Research Center, Moffett
Field, CA, USA., (4) AI MATRIX Consortium, Department of Biomedical
Engineering, University of Texas, San Antonio and UT Health Sciences, San
Antonio, TX, USA., (5) Biological Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, CA, USA., (6) DOE Agile BioFoundry,
Emeryville, CA, USA., (7) Joint BioEnergy Institute, Emeryville, CA, USA.,
(8) Department of Physiology and Biophysics, Weill Cornell Medicine, New
York, NY, USA., (9) Office of the Center Director, NASA Ames Research Center,
Moffett Field, CA, USA., (10) Bay Area Environmental Research Institute, NASA
Ames Research Center, Moffett Field, CA, USA., (11) Universities Space
Research Association (USRA), Space Biosciences Division, NASA Ames Research
Center, Moffett Field, CA, USA., (12) Department of Chemistry, University of
North Florida, Jacksonville, FL, USA., (13) Center for Individualized
Medicine, Department of Surgery, Department of Quantitative Health Sciences,
Mayo Clinic, Rochester, MN, USA., (14) Department of Emergency Medicine,
Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA.,
(15) Department of Microbiology and Immunology, Department of Otolaryngology,
Head and Neck Surgery, University of California San Francisco, San Francisco,
CA, USA., (16) The Gilroy AstroBiology Research Group, The University of
Wisconsin - Madison, Madison, WI, USA., (17) Weill Institute for
Neurosciences, Department of Neurology, University of California San
Francisco, San Francisco, CA, USA., (18) Data Science Analytics, Georgia
Institute of Technology, Lima, Peru, (19) Hasso Plattner Institute for
Digital Health at Mount Sinai, Department of Genetics and Genomic Sciences,
Icahn School of Medicine at Mount Sinai, New York, NY, USA., (20) Center for
Health AI, Department of Biochemistry and Molecular Genetics, University of
Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO, USA., (21)
Center for Health AI, University of Colorado School of Medicine, Anschutz
Medical Campus, Aurora, CO, USA., (22) Department of Neuroscience, University
of Minnesota, Minneapolis, MN, USA., (23) Department of Computer Science,
College of Science, San Jos\'e State University, San Jose, CA, USA., (24)
Biorelate, Manchester, United Kingdom., (25) UC Space Health, Department of
Orthopaedic Surgery, University of California, San Francisco, San Francisco,
CA, USA., (26) Faculty of Dental Medicine and Oral Health Sciences, McGill
University, Montreal, Quebec, Canada., (27) Faculty of Medicine, Dept of
Surgery and Cancer, Imperial College London, London, United Kingdom., (28)
SymbioSeq LLC, NASA Johnson Space Center, Ashburn, VA, USA., (29) Center for
Data Driven Discovery, California Institute of Technology, Pasadena, CA,
USA., (30) Waitt Advanced Biophotonics Center, Chan-Zuckerberg Imaging
Scientist Fellow, Salk Institute for Biological Studies, La Jolla, CA, USA.,
(31) Human Research Program Cross Cutting Computational Modeling Project,
NASA John H. Glenn Research Center, Cleveland, OH, USA., (32) Institute for
Quantitative Health Science and Engineering, Department of Biochemistry and
Molecular Biology, Michigan State University, East Lansing, MI, USA., (33)
Department of Urology, Department of Radiology, Stanford University School of
Medicine, Stanford, CA, USA., (34) Low Exploration Gravity Technology, NASA
John H. Glenn Research Center, Cleveland, OH, USA., (35) Universities Space
Research Association (USRA), Human Research Program Cross-cutting
Computational Modeling Project, NASA John H. Glenn Research Center,
Cleveland, OH, USA., (36) Mortality Research & Consulting, Inc., Houston, TX,
USA., (37) Logyx, Space Biosciences Division, NASA Ames Research Center,
Moffett Field, CA, USA., (38) Computer Science, Statistics, and Health
Policy, Johns Hopkins University, Baltimore, MD, USA., (39) ML, AI and
Healthcare Lab, Bayesian Health, New York, NY, USA., (40) Biotechnology and
Planetary Protection Group, Jet Propulsion Laboratory, Pasadena, CA, USA.,
(41) SPHES, Medical Faculty, King's College London, London, United Kingdom.,
(42) Department of Genetics, Stanford School of Medicine, Stanford, CA USA.,
(43) Department of Preventive Medicine and Community Health, UTMB, Galveston,
TX USA., (44) Human Health and Performance Directorate, NASA Johnson Space
Center, Houston, TX, USA., (45) Department of Biology, California Institute
of Technology, Pasadena, CA, USA., (46) ISS National Laboratory, Center for
the Advancement of Science in Space, Melbourne, FL, USA., (47) UAB Center for
Computational Biology and Data Science, University of Alabama, Birmingham,
Birmingham, AL, USA., (48) Department of Biomedical Informatics, Harvard
Medical School, Harvard Data Science, Broad Institute of MIT and Harvard,
Harvard University, Boston, MA, USA., (49) Space Biosciences Division, NASA
Ames Research Center, Moffett Field, CA, USA.)
- Abstract summary: Space biology research aims to understand fundamental effects of spaceflight on organisms.
Bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life.
- Score: 0.8855198354664937
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities [46.671834972945874]
We propose a vision of leveraging advances in AI to construct virtual cells.
We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities.
We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration.
arXiv Detail & Related papers (2024-09-18T02:41:50Z) - LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations.
We introduce Scientific Generative Agent (SGA), a bilevel optimization framework.
We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
arXiv Detail & Related papers (2024-05-16T03:04:10Z) - ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
The challenge of replicating research results has posed a significant impediment to the field of molecular biology.
We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective.
Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
arXiv Detail & Related papers (2023-11-01T14:44:01Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Automated Scientific Discovery: From Equation Discovery to Autonomous
Discovery Systems [5.7923858184309385]
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents.
We will present closed-loop scientific discovery systems, starting with the pioneering work on the Adam system up to current efforts in fields from material science to astronomy.
The maximal level, level five, is defined to require no human intervention at all in the production of scientific knowledge.
arXiv Detail & Related papers (2023-05-03T16:35:41Z) - Onboard Science Instrument Autonomy for the Detection of Microscopy
Biosignatures on the Ocean Worlds Life Surveyor [2.526702791640305]
The quest to find extraterrestrial life is a critical scientific endeavor with civilization-level implications.
The lack of a precise definition of life poses a fundamental challenge to formulating detection strategies.
We describe two OSIA implementations developed as part of the Ocean Worlds Life Surveyor prototype instrument suite at the Jet Propulsion Laboratory.
arXiv Detail & Related papers (2023-04-25T23:10:02Z) - Artificial Intelligence and Natural Language Processing and
Understanding in Space: Four ESA Case Studies [48.53582660901672]
We present a methodological framework based on artificial intelligence and natural language processing and understanding to automatically extract information from Space documents.
Case studies are implemented across different functional areas of ESA, including Mission Design, Quality Assurance, Long-Term Data Preservation, and the Open Space Innovation Platform.
arXiv Detail & Related papers (2022-10-07T15:50:17Z) - A Low-Cost Robot Science Kit for Education with Symbolic Regression for
Hypothesis Discovery and Validation [15.72286703649173]
Next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop.
To build and use these systems, the next generation workforce requires expertise in diverse areas including ML, control systems, measurement science, materials synthesis, decision theory, among others.
We present the next generation in science education, a kit for building a low-cost autonomous scientist.
arXiv Detail & Related papers (2022-04-08T17:25:28Z) - Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and
Precision Space Health [0.8838373492847601]
We propose an appropriately autonomous and intelligent Precision Space Health system.
It will monitor, aggregate, and assess biomedical statuses.
It will analyze and predict personalized adverse health outcomes.
arXiv Detail & Related papers (2021-12-22T05:33:15Z) - Seeing biodiversity: perspectives in machine learning for wildlife
conservation [49.15793025634011]
We argue that machine learning can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species.
In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies.
arXiv Detail & Related papers (2021-10-25T13:40:36Z) - On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian
Active Learning [12.021024778717575]
We focus a closed-loop, active learning-driven autonomous system on the discovery of advanced materials.
We demonstrate autonomous research methodology that can place complex, advanced materials in reach.
This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs.
arXiv Detail & Related papers (2020-06-11T01:26:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.