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Failure of human rhombic lip differentiation underlies medulloblastoma formation (vol 609, pg 1021, 2022)

NATURE 2022年第7940期612卷 E12-E12页

作者： Hendrikse, Liam D. Haldipur, Parthiv Saulnier, Olivier Millman, Jake Sjoboen, Alexandria H. Erickson, Anders W. Ong, Winnie Gordon, Victor Coudiere-Morrison, Ludivine Mercier, Audrey L. Shokouhian, Mohammad Suarez, Raul A. Ly, Michelle Borlase, Stephanie Scott, David S. Vladoiu, Maria C. Farooq, Hamza Sirbu, Olga Nakashima, Takuma Nambu, Shohei Funakoshi, Yusuke Bahcheli, Alec Diaz-Mejia, J. Javier Golser, Joseph Bach, Kathleen Phuong-Bao, Tram Skowron, Patryk Wang, Evan Y. Kumar, Sachin A. Balin, Polina Visvanathan, Abhirami Lee, John J. Y. Ayoub, Ramy Chen, Xin Chen, Xiaodi Mungall, Karen L. Luu, Betty Berube, Pierre Wang, Yu C. Pfister, Stefan M. Kim, Seung-Ki Delattre, Olivier Bourdeaut, Franck Doz, Francois Masliah-Planchon, Julien Grajkowska, Wieslawa A. Loukides, James Dirks, Peter Fevre-Montange, Michelle Jouvet, Anne French, Pim J. Kros, Johan M. Zitterbart, Karel Bailey, Swneke D. Eberhart, Charles G. Rao, Amulya A. N. Giannini, Caterina Olson, James M. Garami, Miklos Hauser, Peter Phillips, Joanna J. Ra, Young S. de Torres, Carmen Mora, Jaume Li, Kay K. W. Ng, Ho-Keung Poon, Wai S. Pollack, Ian F. Lopez-Aguilar, Enrique Gillespie, G. Yancey Van Meter, Timothy E. Shofuda, Tomoko Vibhakar, Rajeev Thompson, Reid C. Cooper, Michael K. Rubin, Joshua B. Kumabe, Toshihiro Jung, Shin Lach, Boleslaw Iolascon, Achille Ferrucci, Veronica de Antonellis, Pasqualino Zollo, Massimo Cinalli, Giuseppe Robinson, Shenandoah Stearns, Duncan S. Van Meir, Erwin G. Porrati, Paola Finocchiaro, Gaetano Massimino, Maura Carlotti, Carlos G. Faria, Claudia C. Roussel, Martine F. Boop, Frederick Chan, Jennifer A. Aldinger, Kimberly A. Razavi, Ferechte Silvestri, Evelina McLendon, Roger E. Thompson, Eric M. Ansari, Marc Garre, Maria L. Chico, Fernando Eguia, Pilar Perezpena, Mario Morrissy, A. Sorana Cavalli, Florence M. G. Wu, Xiaochong Daniels, Craig Rich, Jeremy N. Jones, Steven J. M. Moore, Richard A. Marra, Marco A. Huang, Xi Reimand, Juri Sorensen, Poul H. Wechsler-Reya, Robert J. Weiss, William A. Pugh, Trevor J. Garzia, Liv The Arthur and Sonia Labatt Brain Tumor Research Centre The Hospital for Sick Children Toronto Ontario Canada Developmental and Stem Cell Biology Program The Hospital for Sick Children Toronto Ontario Canada Department of Medical Biophysics University of Toronto Toronto Ontario Canada Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Ontario Canada Department of Molecular Genetics University of Toronto Toronto Ontario Canada Division of Neurosurgery The Hospital for Sick Children Toronto Ontario Canada Division of Haematology/Oncology Department of Pediatrics The Hospital for Sick Children Toronto Ontario Canada Computational Biology Program Ontario Institute for Cancer Research Toronto Ontario Canada Princess Margaret Cancer Centre University Health Network Toronto Ontario Canada Center for Integrative Brain Research Seattle Children’s Research Institute Seattle WA USA Brotman Baty Institute for Precision Medicine Seattle WA USA Department of Biochemistry and Medical Genetics University of Manitoba Winnipeg Manitoba Canada CancerCare Manitoba Research Institute Winnipeg Manitoba Canada PSL Research University Université Paris Sud Université Paris-Saclay CNRS UMR 3347 INSERM U1021 Institut Curie Orsay France Department of Pediatrics and Child Health and Department of Biochemistry and Medical Genetics Rady Faculty of Health Sciences University of Manitoba Winnipeg Manitoba Canada Division of Brain Tumor Translational Research National Cancer Center Research Institute Tokyo Japan Adaptive Oncology Ontario Institute for Cancer Research Toronto Ontario Canada Canada’s Michael Smith Genome Sciences Centre BC Cancer Agency Vancouver British Columbia Canada Department of Medical Genetics University of British Columbia Vancouver British Columbia Canada Department of Molecular Biology and Biochemistry Simon Fraser University Burnaby British Columbia Canada McGill University Genome Centre McGill University Mon

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Integration of Protein Interactome Networks with Congenital Heart Disease Variants Reveals Candidate Disease Genes

SSRN

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SSRN 2020年

作者： Gonzalez-Teran, Barbara Pittman, Maureen Felix, Franco Richmond-Buccola, Desmond Thomas, Reuben Choudhary, Krishna Moroni, Elisabetta Colombo, Giorgio Alexanian, Michael Cole, Bonnie Samse-Knapp, Kaitlen McGregor, Michael Gifford, Casey A. Huttenhain, Ruth Gelb, Bruce D. Conklin, Bruce R. Black, Brian L. Bruneau, Benoit G. Krogan, Nevan J. Pollard, Katherine S. Srivastava, Deepak Gladstone Institutes San FranciscoCA United States SCITEC-CNR Milano Italy University of Pavia Department of Chemistry Pavia Italy Developmental and Stem Cell Biology PhD Program University of California San FranciscoCA United States Department of Cellular and Molecular Pharmacology University of California San Francisco San FranciscoCA United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY United States Mindich Child Health and Development Institute Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Pediatrics Icahn School of Medicine at Mount Sinai New YorkNY United States Cardiovascular Research Institute University of California San FranciscoCA United States Department of Pediatrics UCSF School of Medicine San FranciscoCA United States Roddenberry Center for Stem Cell Biology and Medicine at Gladstone San FranciscoCA United States University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Epidemiology & Biostatistics Institute for Computational Health Sciences Institute for Human Genetics University of California San FranciscoCA United States Department of Biochemistry and Biophysics University of California San FranciscoCA United States Gladstone Institutes 1650 Owens St San FranciscoCA94158 United States

Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains a challenge despite large-scale genomic sequencing efforts. We hypothesized that genetic determinants for CHDs may lie in protein interactomes of GATA4 and TBX5, two transcription factors that cause CHDs. Defining their interactomes in human cardiac progenitors via affinity purification-mass spectrometry and integrating results with genetic data from the Pediatric Cardiac Genomic Consortium revealed an enrichment of de novo variants among proteins that interact with GATA4 or TBX5. A consolidative score that prioritized interactome members based on variant, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied cardiac developmental genes, resulting in co-activation, and the GLYR1 missense variant associated with CHD disrupted interaction with GATA4. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating the contribution of genetic variants in disease. © 2020, The Authors. All rights reserved.

关键词： Proteins

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New site-specific protein functionality with a click

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BioSpektrum 2016年第7期22卷 704-707页

作者： Koehler, Christine Lemke, Edward A. European Molecular Biology Laboratory (EMBL) Structural and Computational Biology Unit Cell Biology and Biophysics Unit Meyerhofstraße 1 Heidelberg D-69117 Germany

Genetic encoding of custom designed non-canonical amino acids has emerged as one of the most versatile tools to install site-specific “click-reactive” functionalities into proteins. These non-toxic groups can readily react with suitable substances carrying a compatible clickchemistry. The technology can be used for a variety of applications, such as fluorescent labelling of protein in cells to visualize molecular machines and to custom-engineer better drugs. © 2016, Springer-Verlag Berlin Heidelberg.

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Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes

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Nature 2022年第7909期605卷 E3页

作者： Jill E Moore Michael J Purcaro Henry E Pratt Charles B Epstein Noam Shoresh Jessika Adrian Trupti Kawli Carrie A Davis Alexander Dobin Rajinder Kaul Jessica Halow Eric L Van Nostrand Peter Freese David U Gorkin Yin Shen Yupeng He Mark Mackiewicz Florencia Pauli-Behn Brian A Williams Ali Mortazavi Cheryl A Keller Xiao-Ou Zhang Shaimae I Elhajjajy Jack Huey Diane E Dickel Valentina Snetkova Xintao Wei Xiaofeng Wang Juan Carlos Rivera-Mulia Joel Rozowsky Jing Zhang Surya B Chhetri Jialing Zhang Alec Victorsen Kevin P White Axel Visel Gene W Yeo Christopher B Burge Eric Lécuyer David M Gilbert Job Dekker John Rinn Eric M Mendenhall Joseph R Ecker Manolis Kellis Robert J Klein William S Noble Anshul Kundaje Roderic Guigó Peggy J Farnham J Michael Cherry Richard M Myers Bing Ren Brenton R Graveley Mark B Gerstein Len A Pennacchio Michael P Snyder Bradley E Bernstein Barbara Wold Ross C Hardison Thomas R Gingeras John A Stamatoyannopoulos Zhiping Weng University of Massachusetts Medical School Program in Bioinformatics and Integrative Biology Worcester MA USA. The Broad Institute of Harvard and MIT Cambridge MA USA. Department of Genetics School of Medicine Stanford University Palo Alto CA USA. Cold Spring Harbor Laboratory Functional Genomics Cold Spring Harbor NY USA. Altius Institute for Biomedical Sciences Seattle WA USA. Department of Medicine University of Washington School of Medicine Seattle WA USA. Department of Cellular and Molecular Medicine Institute for Genomic Medicine Stem Cell Program Sanford Consortium for Regenerative Medicine University of California San Diego La Jolla CA USA. Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA. Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. Ludwig Institute for Cancer Research University of California San Diego La Jolla CA USA. Institute for Human Genetics Department of Neurology University of California San Francisco San Francisco CA USA. Genomics Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. HudsonAlpha Institute for Biotechnology Huntsville AL USA. Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA. Department of Developmental and Cell Biology University of California Irvine Irvine CA USA. Department of Biochemistry and Molecular Biology The Pennsylvania State University University Park PA USA. Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA. Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA. Département de Biochimie et Médecine Moléculaire Université de Montréal Montréal Quebec Canada. Division of Experimental Medicine McGill University Montreal Quebec Canada. Institut de Recherches Cliniques de Montréal (IR

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Convolutional neural networks for automated annotation of cellular cryo-electron tomograms

arXiv

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arXiv 2017年

作者： Chen, Muyuan Dai, Wei Sun, Stella Y. Jonasch, Darius He, Cynthia Y. Schmid, Michael F. Chiu, Wah Ludtke, Steven J. Graduate Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine HoustonTX77030 United States Verna Marrs and McLean Department of Biochemistry and Molecular Biology Baylor College of Medicine HoustonTX77030 United States Department of Biological Science Centre for BioImaging Sciences National University of Singapore 14 Science Drive 4 Singapore117543 Singapore

Cellular Electron Cryotomography (CryoET) offers the ability to look inside cells and observe macromolecules frozen in action. A primary challenge for this technique is identifying and extracting the molecular components within the crowded cellular environment. We introduce a method using neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yields in-situ structures of molecular components of interest. © 2017, CC BY.

关键词： Cellular neural networks

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Void distributions reveal structural link between jammed packings and protein cores

arXiv

引用

arXiv 2018年

作者： Treado, John D. Mei, Zhe Regan, Lynne O'Hern, Corey S. Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction φ ≈ 0.56, which is the same as dense, random packing of amino acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. By measuring the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation, we show that void percolation in packings of amino acid-shaped particles and protein cores belong to the same universality class, which is different from that for void percolation in jammed sphere packings. We propose that the connected void regions of proteins are a defining feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations. Copyright © 2018, The Authors. All rights reserved.

关键词： Amino acids

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Transgenic mouse models for studying adult neurogenesis

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Frontiers in biology 2016年第3期11卷 151-167页

作者： Fatih Semerci Mirjana Maletic-Savatic Program in Developmental Biology Baylor College of Medicine Houston TX 77030 USA Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital Houston TX 77030 USA Department of Pediatrics-Neurology Department of Neuroscience and Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX 77030 USA

The mammalian hippocampus shows a remarkable capacity for continued neurogenesis throughout life. Newborn neurons, generated by the radial neural stem cells （NSCs）, are important for learning and memory as well as mood control. During aging, the number and responses of NSCs to neurogenic stimuli diminish, leading to decreased neurogenesis and age-associatedcognitive decline and psychiatric disorders. Thus, adult hippocampal neurogenesis has garnered significant interest because targeting it could be a novel potential therapeutic strategy for these disorders. However, if we are to use nenrogenesis to halt or reverse hippocampal-related pathology, we need to understand better the core molecular machinery that governs NSC and their progeny. In this review, we summarize a wide variety of mouse models used in adult neurogenesis field, present their advantages and disadvantages based on specificity and efficiency of labeling of different cell types, and review their contribution to our understanding of the biology and the heterogeneity of different cell types found in adult neurogenic niches.

关键词： adult neurogenesis mouse models neural stem cells neuroprogenitors lineage tracing

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Cryo-EM structure and biochemical analysis reveal the basis of the functional difference between human PI3KC3-C1 and-C2

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细胞研究（英文版） 2017年第8期27卷 989-1001页

作者： Meisheng Ma Jun-Jie Liu Yan Li Yuwei Huang Na Ta Yang Chen Hua Fu Ming-Da Ye Yuehe Ding Weijiao Huang Jia Wang Meng-Qiu Dong Li Yu Hong-Wei Wang The State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing 100084 China Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA Ministry of Education Key Laboratory of Protein Sciences Tsinghua-Peking Joint Center for Life Sciences Beijing Advanced Innovation Center for Structural Biology School of Life SciencesTsinghua University Beijing 100084 China The State Key Laboratory of Membrane Biology Tsinghua University-Peking University Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing 100084 China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China MOH Key Laboratory of Systems Biology of Pathogens Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China National Institute of Biological Sciences Beijing 102206 China

Phosphatidylinositol 3-phosphate (PI3P) plays essential roles in vesicular trafficking,organelle biogenesis and *** class Ⅲ phosphatidylinositol 3-kinase (PI3KC3) complexes have been identified in mammals,the ATG14L complex (PI3KC3-C1) and the UVRAG complex (PI3KC3-C2).PI3KC3-C1 is crucial for autophagosome biogenesis,and PI3KC3-C2 is involved in various membrane trafficking *** we report the cryo-EM structures of human PI3KC3-C1 and PI3KC3-C2 at sub-nanometer *** two structures share a common L-shaped overall architecture with distinct *** examination revealed that PI3KC3-C1 "stands up" on lipid monolayers,with the ATG14L BATs domain and the VPS34 C-terminal domain (CTD) directly contacting the *** dissection indicated that the ATG14L BATs domain is responsible for membrane anchoring,whereas the CTD of VPS34 determines the ***,PI3KC3-C2 binds much more weakly than PI3KC3-C1 to both PI-containing liposomes and purified endoplasmic reticulum (ER) vesicles,a property that is specifically determined by the ATG14L BATs *** in vivo ER localization analysis indicated that the BATs domain was required for ER localization of *** propose that the different lipid binding capacity is the key factor that differentiates the functions of PI3KC3-C1 and PI3KC3-C2 in autophagy.

关键词： PI3KC3 ATG14L UVRAG autophagy PI cryo-EM

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

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. © 2021, CC BY.

关键词： Predictive analytics

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