1
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Del Val C, Díaz de la Guardia-Bolívar E, Zwir I, Mishra PP, Mesa A, Salas R, Poblete GF, de Erausquin G, Raitoharju E, Kähönen M, Raitakari O, Keltikangas-Järvinen L, Lehtimäki T, Cloninger CR. Gene expression networks regulated by human personality. Mol Psychiatry 2024; 29:2241-2260. [PMID: 38433276 PMCID: PMC11408262 DOI: 10.1038/s41380-024-02484-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 03/05/2024]
Abstract
Genome-wide association studies of human personality have been carried out, but transcription of the whole genome has not been studied in relation to personality in humans. We collected genome-wide expression profiles of adults to characterize the regulation of expression and function in genes related to human personality. We devised an innovative multi-omic approach to network analysis to identify the key control elements and interactions in multi-modular networks. We identified sets of transcribed genes that were co-expressed in specific brain regions with genes known to be associated with personality. Then we identified the minimum networks for the co-localized genes using bioinformatic resources. Subjects were 459 adults from the Young Finns Study who completed the Temperament and Character Inventory and provided peripheral blood for genomic and transcriptomic analysis. We identified an extrinsic network of 45 regulatory genes from seed genes in brain regions involved in self-regulation of emotional reactivity to extracellular stimuli (e.g., self-regulation of anxiety) and an intrinsic network of 43 regulatory genes from seed genes in brain regions involved in self-regulation of interpretations of meaning (e.g., production of concepts and language). We discovered that interactions between the two networks were coordinated by a control hub of 3 miRNAs and 3 protein-coding genes shared by both. Interactions of the control hub with proteins and ncRNAs identified more than 100 genes that overlap directly with known personality-related genes and more than another 4000 genes that interact indirectly. We conclude that the six-gene hub is the crux of an integrative network that orchestrates information-transfer throughout a multi-modular system of over 4000 genes enriched in liquid-liquid-phase-separation (LLPS)-related RNAs, diverse transcription factors, and hominid-specific miRNAs and lncRNAs. Gene expression networks associated with human personality regulate neuronal plasticity, epigenesis, and adaptive functioning by the interactions of salience and meaning in self-awareness.
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Affiliation(s)
- Coral Del Val
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
- Instituto de Investigación Biosanitaria de Granada (ibs. GRANADA), Granada, Spain
| | - Elisa Díaz de la Guardia-Bolívar
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
| | - Igor Zwir
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
| | - Pashupati P Mishra
- Tampere University, Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Alberto Mesa
- University of Granada, Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
| | - Ramiro Salas
- The Menninger Clinic, Baylor College of Medicine, and DeBakey VA Medical Center, Houston, TX, USA
| | | | - Gabriel de Erausquin
- University of Texas Health San Antonio, Long School of Medicine, Department of Neurology, Biggs Institute of Alzheimer's & Neurodegenerative Disorders, San Antonio, TX, USA
| | - Emma Raitoharju
- Tampere University, Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli Raitakari
- University of Turku and Turku University Hospital, Center for Population Health Research; University of Turku, Research Center of Applied and Preventive Cardiovascular Medicine; Turku University Hospital, Department of Clinical Physiology and Nuclear Medicine, Turku, Finland
| | | | - Terho Lehtimäki
- Tampere University, Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
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2
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Goldford JE, Smith HB, Longo LM, Wing BA, McGlynn SE. Primitive purine biosynthesis connects ancient geochemistry to modern metabolism. Nat Ecol Evol 2024; 8:999-1009. [PMID: 38519634 DOI: 10.1038/s41559-024-02361-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/06/2024] [Indexed: 03/25/2024]
Abstract
An unresolved question in the origin and evolution of life is whether a continuous path from geochemical precursors to the majority of molecules in the biosphere can be reconstructed from modern-day biochemistry. Here we identified a feasible path by simulating the evolution of biosphere-scale metabolism, using only known biochemical reactions and models of primitive coenzymes. We find that purine synthesis constitutes a bottleneck for metabolic expansion, which can be alleviated by non-autocatalytic phosphoryl coupling agents. Early phases of the expansion are enriched with enzymes that are metal dependent and structurally symmetric, supporting models of early biochemical evolution. This expansion trajectory suggests distinct hypotheses regarding the tempo, mode and timing of metabolic pathway evolution, including a late appearance of methane metabolisms and oxygenic photosynthesis consistent with the geochemical record. The concordance between biological and geological analyses suggests that this trajectory provides a plausible evolutionary history for the vast majority of core biochemistry.
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Affiliation(s)
- Joshua E Goldford
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA.
- Physics of Living Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Blue Marble Space Institute of Science, Seattle, WA, USA.
| | - Harrison B Smith
- Blue Marble Space Institute of Science, Seattle, WA, USA
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan
| | - Liam M Longo
- Blue Marble Space Institute of Science, Seattle, WA, USA
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan
| | - Boswell A Wing
- Department of Geological Sciences, University of Colorado, Boulder, CO, USA
| | - Shawn Erin McGlynn
- Blue Marble Space Institute of Science, Seattle, WA, USA.
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan.
- Biofunctional Catalyst Research Team, RIKEN Center for Sustainable Resource Science, Wako, Japan.
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3
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Ghosh S, Baltussen MG, Ivanov NM, Haije R, Jakštaitė M, Zhou T, Huck WTS. Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems. Chem Rev 2024; 124:2553-2582. [PMID: 38476077 PMCID: PMC10941194 DOI: 10.1021/acs.chemrev.3c00681] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.
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Affiliation(s)
- Souvik Ghosh
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Mathieu G. Baltussen
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Nikita M. Ivanov
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Rianne Haije
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Miglė Jakštaitė
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Tao Zhou
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Wilhelm T. S. Huck
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
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4
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Schaible MJ, Szeinbaum N, Bozdag GO, Chou L, Grefenstette N, Colón-Santos S, Rodriguez LE, Styczinski MJ, Thweatt JL, Todd ZR, Vázquez-Salazar A, Adams A, Araújo MN, Altair T, Borges S, Burton D, Campillo-Balderas JA, Cangi EM, Caro T, Catalano E, Chen K, Conlin PL, Cooper ZS, Fisher TM, Fos SM, Garcia A, Glaser DM, Harman CE, Hermis NY, Hooks M, Johnson-Finn K, Lehmer O, Hernández-Morales R, Hughson KHG, Jácome R, Jia TZ, Marlow JJ, McKaig J, Mierzejewski V, Muñoz-Velasco I, Nural C, Oliver GC, Penev PI, Raj CG, Roche TP, Sabuda MC, Schaible GA, Sevgen S, Sinhadc P, Steller LH, Stelmach K, Tarnas J, Tavares F, Trubl G, Vidaurri M, Vincent L, Weber JM, Weng MM, Wilpiszeki RL, Young A. Chapter 1: The Astrobiology Primer 3.0. ASTROBIOLOGY 2024; 24:S4-S39. [PMID: 38498816 DOI: 10.1089/ast.2021.0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
The Astrobiology Primer 3.0 (ABP3.0) is a concise introduction to the field of astrobiology for students and others who are new to the field of astrobiology. It provides an entry into the broader materials in this supplementary issue of Astrobiology and an overview of the investigations and driving hypotheses that make up this interdisciplinary field. The content of this chapter was adapted from the other 10 articles in this supplementary issue and thus represents the contribution of all the authors who worked on these introductory articles. The content of this chapter is not exhaustive and represents the topics that the authors found to be the most important and compelling in a dynamic and changing field.
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Affiliation(s)
- Micah J Schaible
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Nadia Szeinbaum
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - G Ozan Bozdag
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Luoth Chou
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Center for Space Sciences and Technology, University of Maryland, Baltimore, Maryland, USA
- Georgetown University, Washington DC, USA
| | - Natalie Grefenstette
- Santa Fe Institute, Santa Fe, New Mexico, USA
- Blue Marble Space Institute of Science, Seattle, Washington, USA
| | - Stephanie Colón-Santos
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Wisconsin, USA
- Department of Botany, University of Wisconsin-Madison, Wisconsin, USA
| | - Laura E Rodriguez
- Lunar and Planetary Institute, Universities Space Research Association, Houston, Texas, USA
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | - M J Styczinski
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
- University of Washington, Seattle, Washington, USA
| | - Jennifer L Thweatt
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, Pennsylvania, USA
| | - Zoe R Todd
- Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
| | - Alberto Vázquez-Salazar
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Department of Chemical and Biomolecular Engineering, University of California Los Angeles, California, USA
| | - Alyssa Adams
- Center for Space Sciences and Technology, University of Maryland, Baltimore, Maryland, USA
| | - M N Araújo
- Biochemistry Department, University of São Paulo, São Carlos, Brazil
| | - Thiago Altair
- Institute of Chemistry of São Carlos, Universidade de São Paulo, São Carlos, Brazil
- Department of Chemistry, College of the Atlantic, Bar Harbor, Maine, USA
| | | | - Dana Burton
- Department of Anthropology, George Washington University, Washington DC, USA
| | | | - Eryn M Cangi
- Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Tristan Caro
- Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA
| | - Enrico Catalano
- Sant'Anna School of Advanced Studies, The BioRobotics Institute, Pisa, Italy
| | - Kimberly Chen
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Peter L Conlin
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Z S Cooper
- Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
| | - Theresa M Fisher
- School of Earth and Space Exploration, Arizona State University, Tempe, Arizona, USA
| | - Santiago Mestre Fos
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Amanda Garcia
- Department of Bacteriology, University of Wisconsin-Madison, Wisconsin, USA
| | - D M Glaser
- Arizona State University, Tempe, Arizona, USA
| | - Chester E Harman
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Ninos Y Hermis
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
- Department of Physics and Space Sciences, University of Granada, Granada, Spain
| | - M Hooks
- NASA Johnson Space Center, Houston, Texas, USA
| | - K Johnson-Finn
- Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Owen Lehmer
- Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
| | - Ricardo Hernández-Morales
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Kynan H G Hughson
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Rodrigo Jácome
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Tony Z Jia
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan
| | - Jeffrey J Marlow
- Department of Biology, Boston University, Boston, Massachusetts, USA
| | - Jordan McKaig
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Veronica Mierzejewski
- School of Earth and Space Exploration, Arizona State University, Tempe, Arizona, USA
| | - Israel Muñoz-Velasco
- Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Ceren Nural
- Istanbul Technical University, Istanbul, Turkey
| | - Gina C Oliver
- Department of Geology, San Bernardino Valley College, San Bernardino, California, USA
| | - Petar I Penev
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Chinmayee Govinda Raj
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Tyler P Roche
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Mary C Sabuda
- Department of Earth and Environmental Sciences, University of Minnesota-Twin Cities, Minneapolis, Minnesota, USA
- Biotechnology Institute, University of Minnesota-Twin Cities, St. Paul, Minnesota, USA
| | - George A Schaible
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, USA
| | - Serhat Sevgen
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Institute of Marine Sciences, Middle East Technical University, Erdemli, Mersin, Turkey
| | - Pritvik Sinhadc
- BEYOND: Center For Fundamental Concepts in Science, Arizona State University, Arizona, USA
- Dubai College, Dubai, United Arab Emirates
| | - Luke H Steller
- Australian Centre for Astrobiology, and School of Biological, Earth and Environmental Sciences, University of New South Wales, Kensington, Australia
| | - Kamil Stelmach
- Department of Chemistry, University of Virginia, Charlottesville, Virginia, USA
| | - J Tarnas
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | - Frank Tavares
- Space Enabled Research Group, MIT Media Lab, Cambridge, Massachusetts, USA
| | - Gareth Trubl
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, USA
| | - Monica Vidaurri
- Center for Space Sciences and Technology, University of Maryland, Baltimore, Maryland, USA
- Department of Physics and Astronomy, Howard University, Washington DC, USA
| | - Lena Vincent
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Wisconsin, USA
| | - Jessica M Weber
- NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | | | | | - Amber Young
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Northern Arizona University, Flagstaff, Arizona, USA
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5
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Grefenstette N, Chou L, Colón-Santos S, Fisher TM, Mierzejewski V, Nural C, Sinhadc P, Vidaurri M, Vincent L, Weng MM. Chapter 9: Life as We Don't Know It. ASTROBIOLOGY 2024; 24:S186-S201. [PMID: 38498819 DOI: 10.1089/ast.2021.0103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
While Earth contains the only known example of life in the universe, it is possible that life elsewhere is fundamentally different from what we are familiar with. There is an increased recognition in the astrobiology community that the search for life should steer away from terran-specific biosignatures to those that are more inclusive to all life-forms. To start exploring the space of possibilities that life could occupy, we can try to dissociate life from the chemistry that composes it on Earth by envisioning how different life elsewhere could be in composition, lifestyle, medium, and form, and by exploring how the general principles that govern living systems on Earth might be found in different forms and environments across the Solar System. Exotic life-forms could exist on Mars or Venus, or icy moons like Europa and Enceladus, or even as a shadow biosphere on Earth. New perspectives on agnostic biosignature detection have also begun to emerge, allowing for a broader and more inclusive approach to seeking exotic life with unknown chemistry that is distinct from life as we know it on Earth.
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Affiliation(s)
- Natalie Grefenstette
- Santa Fe Institute, Santa Fe, New Mexico, USA
- Blue Marble Space Institute of Science, Seattle, Washington, USA
| | - Luoth Chou
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Georgetown University, Washington, DC, USA
| | | | - Theresa M Fisher
- School of Earth and Space Exploration, Arizona State University, Arizona, USA
| | | | - Ceren Nural
- Istanbul Technical University, Istanbul, Turkey
| | - Pritvik Sinhadc
- BEYOND: Center For Fundamental Concepts in Science, Arizona State University, Arizona, USA
- Dubai College, Dubai, United Arab Emirates
| | - Monica Vidaurri
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Howard University, DC, USA
| | - Lena Vincent
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Wisconsin, USA
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6
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Gilmour KM, Daley MA, Egginton S, Kelber A, McHenry MJ, Patek SN, Sane SP, Schulte PM, Terblanche JS, Wright PA, Franklin CE. Through the looking glass: attempting to predict future opportunities and challenges in experimental biology. J Exp Biol 2023; 226:jeb246921. [PMID: 38059428 DOI: 10.1242/jeb.246921] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
To celebrate its centenary year, Journal of Experimental Biology (JEB) commissioned a collection of articles examining the past, present and future of experimental biology. This Commentary closes the collection by considering the important research opportunities and challenges that await us in the future. We expect that researchers will harness the power of technological advances, such as '-omics' and gene editing, to probe resistance and resilience to environmental change as well as other organismal responses. The capacity to handle large data sets will allow high-resolution data to be collected for individual animals and to understand population, species and community responses. The availability of large data sets will also place greater emphasis on approaches such as modeling and simulations. Finally, the increasing sophistication of biologgers will allow more comprehensive data to be collected for individual animals in the wild. Collectively, these approaches will provide an unprecedented understanding of 'how animals work' as well as keys to safeguarding animals at a time when anthropogenic activities are degrading the natural environment.
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Affiliation(s)
| | - Monica A Daley
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
| | - Stuart Egginton
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK
| | - Almut Kelber
- Department of Biology, Lund University, 22362 Lund, Sweden
| | - Matthew J McHenry
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
| | - Sheila N Patek
- Biology Department, Duke University, Durham, NC 27708, USA
| | - Sanjay P Sane
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore, Karnataka 560065, India
| | - Patricia M Schulte
- Department of Zoology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - John S Terblanche
- Center for Invasion Biology, Department of Conservation Ecology & Entomology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Patricia A Wright
- Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Craig E Franklin
- School of the Environment, The University of Queensland, St. Lucia, Brisbane 4072, Australia
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7
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Smith HB, Mathis C. Life detection in a universe of false positives: Can the Fatal Flaws of Exoplanet Biosignatures be Overcome Absent a Theory of Life? Bioessays 2023; 45:e2300050. [PMID: 37821360 DOI: 10.1002/bies.202300050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023]
Abstract
Astrobiology aims to determine the distribution and diversity of life in the universe. But as the word "biosignature" suggests, what will be detected is not life itself, but an observation implicating living systems. Our limited access to other worlds suggests this observation is more likely to reflect out-of-equilibrium gasses than a writhing octopus. Yet, anything short of a writhing octopus will raise skepticism about what has been detected. Resolving that skepticism requires a theory to delineate processes due to life and those due to abiotic mechanisms. This poses an existential question for life detection: How do astrobiologists plan to detect life on exoplanets via features shared between non-living and living systems? We argue that you cannot without an underlying theory of life. We illustrate this by analyzing the hypothetical detection of an "Earth 2.0" exoplanet. Without a theory of life, we argue the community should focus on identifying unambiguous features of life via four areas: examining life on Earth, building life in the lab, probing the solar system, and searching for technosignatures. Ultimately, we ask, what exactly do astrobiologists hope to learn by searching for life?
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Affiliation(s)
- Harrison B Smith
- Earth-Life Science Institute, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan
- Blue Marble Space Institute of Science, Seattle, Washington, USA
| | - Cole Mathis
- Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, Arizona, USA
- Santa Fe Institute, Santa Fe, New Mexico, USA
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8
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Lingam M, Frank A, Balbi A. Planetary Scale Information Transmission in the Biosphere and Technosphere: Limits and Evolution. Life (Basel) 2023; 13:1850. [PMID: 37763254 PMCID: PMC10532900 DOI: 10.3390/life13091850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
Information transmission via communication between agents is ubiquitous on Earth, and is a vital facet of living systems. In this paper, we aim to quantify this rate of information transmission associated with Earth's biosphere and technosphere (i.e., a measure of global information flow) by means of a heuristic order-of-magnitude model. By adopting ostensibly conservative values for the salient parameters, we estimate that the global information transmission rate for the biosphere might be ∼1024 bits/s, and that it may perhaps exceed the corresponding rate for the current technosphere by ∼9 orders of magnitude. However, under the equivocal assumption of sustained exponential growth, we find that information transmission in the technosphere can potentially surpass that of the biosphere ∼90 years in the future, reflecting its increasing dominance.
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Affiliation(s)
- Manasvi Lingam
- Department of Aerospace, Physics and Space Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA
- Department of Physics and Institute for Fusion Studies, The University of Texas at Austin, Austin, TX 78712, USA
| | - Adam Frank
- Department of Physics and Astronomy, University of Rochester, Rochester, NY 14620, USA
| | - Amedeo Balbi
- Dipartimento di Fisica, Università di Roma “Tor Vergata”, 00133 Roma, Italy
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9
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Mondal S, Greenberg JS, Green JR. Dynamic scaling of stochastic thermodynamic observables for chemical reactions at and away from equilibrium. J Chem Phys 2022; 157:194105. [DOI: 10.1063/5.0106714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Physical kinetic roughening processes are well-known to exhibit universal scaling of observables that fluctuate in space and time. Are there analogous dynamic scaling laws that are unique to the chemical reaction mechanisms available synthetically and occurring naturally? Here, we formulate an approach to the dynamic scaling of stochastic fluctuations in thermodynamic observables at and away from equilibrium. Both analytical expressions and numerical simulations confirm our dynamic scaling ansatz with associated scaling exponents, function, and law. A survey of common chemical mechanisms reveals classes that organize according to the molecularity of the reactions involved, the nature of the reaction vessel and external reservoirs, (non)equilibrium conditions, and the extent of autocatalysis in the reaction network. Varying experimental parameters, such as temperature, can cause coupled reactions capable of chemical feedback to transition between these classes. While path observables, such as the dynamical activity, have scaling exponents that are time-independent, the variance in the entropy production and flow can have time-dependent scaling exponents and self-averaging properties as a result of temporal correlations that emerge during thermodynamically irreversible processes. Altogether, these results establish dynamic universality classes in the nonequilibrium fluctuations of thermodynamic observables for well-mixed chemical reactions.
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Affiliation(s)
- Shrabani Mondal
- Department of Chemistry, University of Massachusetts Boston, Boston, Massachusetts 02125, USA
- Department of Chemistry, Physical Chemistry Section, Jadavpur University, Kolkata 700032, India
| | - Jonah S. Greenberg
- Department of Chemistry, University of Massachusetts Boston, Boston, Massachusetts 02125, USA
- Department of Chemistry, Northwestern University, Evanston, Illinois 60208, USA
| | - Jason R. Green
- Department of Chemistry, University of Massachusetts Boston, Boston, Massachusetts 02125, USA
- Department of Physics, University of Massachusetts Boston, Boston, Massachusetts 02125, USA
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10
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Ruf A, Danger G. Network Analysis Reveals Spatial Clustering and Annotation of Complex Chemical Spaces: Application to Astrochemistry. Anal Chem 2022; 94:14135-14142. [PMID: 36209417 PMCID: PMC9583070 DOI: 10.1021/acs.analchem.2c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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How are molecules
linked to each other in complex systems?
In a
proof-of-concept study, we have developed the method mol2net (https://zenodo.org/record/7025094) to generate and analyze the molecular network of complex astrochemical
data (from high-resolution Orbitrap MS1 analysis of H2O:CH3OH:NH3 interstellar ice analogs)
in a data-driven and unsupervised manner, without any prior knowledge
about chemical reactions. The molecular network is clustered according
to the initial NH3 content and unlocked HCN, NH3, and H2O as spatially resolved key transformations. In
comparison with the PubChem database, four subsets were annotated:
(i) saturated C-backbone molecules without N, (ii) saturated N-backbone
molecules, (iii) unsaturated C-backbone molecules without N, and (iv)
unsaturated N-backbone molecules. These findings were validated with
previous results (e.g., identifying the two major graph components
as previously described N-poor and N-rich molecular groups) but with
additional information about subclustering, key transformations, and
molecular structures, and thus, the structural characterization of
large complex organic molecules in interstellar ice analogs has been
significantly refined.
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Affiliation(s)
- Alexander Ruf
- Laboratoire de Physique des Interactions Ioniques et Moléculaires (PIIM), Université Aix-Marseille, CNRS, 13013 Marseille, France
- Department of Chemistry and Pharmacy, Ludwig-Maximilians-University, 81377 Munich, Germany
- Excellence Cluster ORIGINS, Boltzmannstraße 2, 85748 Garching, Germany
| | - Grégoire Danger
- Laboratoire de Physique des Interactions Ioniques et Moléculaires (PIIM), Université Aix-Marseille, CNRS, 13013 Marseille, France
- Aix-Marseille Université, CNRS, CNES, LAM, 13013 Marseille, France
- Institut Universitaire de France (IUF), 75231 Paris, France
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11
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Ng JWX, Chua SK, Mutwil M. Feature importance network reveals novel functional relationships between biological features in Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2022; 13:944992. [PMID: 36212273 PMCID: PMC9539877 DOI: 10.3389/fpls.2022.944992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/24/2022] [Indexed: 06/16/2023]
Abstract
Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it can find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools.
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12
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Müller S, Flamm C, Stadler PF. What makes a reaction network "chemical"? J Cheminform 2022; 14:63. [PMID: 36123755 PMCID: PMC9484159 DOI: 10.1186/s13321-022-00621-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 06/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Reaction networks (RNs) comprise a set X of species and a set [Formula: see text] of reactions [Formula: see text], each converting a multiset of educts [Formula: see text] into a multiset [Formula: see text] of products. RNs are equivalent to directed hypergraphs. However, not all RNs necessarily admit a chemical interpretation. Instead, they might contradict fundamental principles of physics such as the conservation of energy and mass or the reversibility of chemical reactions. The consequences of these necessary conditions for the stoichiometric matrix [Formula: see text] have been discussed extensively in the chemical literature. Here, we provide sufficient conditions for [Formula: see text] that guarantee the interpretation of RNs in terms of balanced sum formulas and structural formulas, respectively. RESULTS Chemically plausible RNs allow neither a perpetuum mobile, i.e., a "futile cycle" of reactions with non-vanishing energy production, nor the creation or annihilation of mass. Such RNs are said to be thermodynamically sound and conservative. For finite RNs, both conditions can be expressed equivalently as properties of the stoichiometric matrix [Formula: see text]. The first condition is vacuous for reversible networks, but it excludes irreversible futile cycles and-in a stricter sense-futile cycles that even contain an irreversible reaction. The second condition is equivalent to the existence of a strictly positive reaction invariant. It is also sufficient for the existence of a realization in terms of sum formulas, obeying conservation of "atoms". In particular, these realizations can be chosen such that any two species have distinct sum formulas, unless [Formula: see text] implies that they are "obligatory isomers". In terms of structural formulas, every compound is a labeled multigraph, in essence a Lewis formula, and reactions comprise only a rearrangement of bonds such that the total bond order is preserved. In particular, for every conservative RN, there exists a Lewis realization, in which any two compounds are realized by pairwisely distinct multigraphs. Finally, we show that, in general, there are infinitely many realizations for a given conservative RN. CONCLUSIONS "Chemical" RNs are directed hypergraphs with a stoichiometric matrix [Formula: see text] whose left kernel contains a strictly positive vector and whose right kernel does not contain a futile cycle involving an irreversible reaction. This simple characterization also provides a concise specification of random models for chemical RNs that additionally constrain [Formula: see text] by rank, sparsity, or distribution of the non-zero entries. Furthermore, it suggests several interesting avenues for future research, in particular, concerning alternative representations of reaction networks and infinite chemical universes.
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Affiliation(s)
- Stefan Müller
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
| | - Christoph Flamm
- Department of Theoretical Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria
| | - Peter F. Stadler
- Department of Theoretical Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16–18, 04107 Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig & Competence Center for Scalable Data Services and Solutions Dresden-Leipzig & Leipzig Research Center for Civilization Diseases University Leipzig, 04107 Leipzig, Germany
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany
- Faculdad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Ciudad Universitaria, Bogotá, 111321 Colombia
- Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM87501 USA
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13
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Formalising the Pathways to Life Using Assembly Spaces. ENTROPY 2022; 24:e24070884. [PMID: 35885107 PMCID: PMC9323097 DOI: 10.3390/e24070884] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022]
Abstract
Assembly theory (referred to in prior works as pathway assembly) has been developed to explore the extrinsic information required to distinguish a given object from a random ensemble. In prior work, we explored the key concepts relating to deconstructing an object into its irreducible parts and then evaluating the minimum number of steps required to rebuild it, allowing for the reuse of constructed sub-objects. We have also explored the application of this approach to molecules, as molecular assembly, and how molecular assembly can be inferred experimentally and used for life detection. In this article, we formalise the core assembly concepts mathematically in terms of assembly spaces and related concepts and determine bounds on the assembly index. We explore examples of constructing assembly spaces for mathematical and physical objects and propose that objects with a high assembly index can be uniquely identified as those that must have been produced using directed biological or technological processes rather than purely random processes, thereby defining a new scale of aliveness. We think this approach is needed to help identify the new physical and chemical laws needed to understand what life is, by quantifying what life does.
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14
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Scaling laws in enzyme function reveal a new kind of biochemical universality. Proc Natl Acad Sci U S A 2022; 119:2106655119. [PMID: 35217602 PMCID: PMC8892295 DOI: 10.1073/pnas.2106655119] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/18/2021] [Indexed: 11/21/2022] Open
Abstract
Known examples of life all share the same core biochemistry going back to the last universal common ancestor (LUCA), but whether this feature is universal to other examples, including at the origin of life or alien life, is unknown. We show how a physics-inspired statistical approach identifies universal scaling laws across biochemical reactions that are not defined by common chemical components but instead, as macroscale patterns in the reaction functions used by life. The identified scaling relations can be used to predict statistical features of LUCA, and network analyses reveal some of the functional principles that underlie them. They are, therefore, prime candidates for developing new theory on the “laws of life” that might apply to all possible biochemistries. All life on Earth is unified by its use of a shared set of component chemical compounds and reactions, providing a detailed model for universal biochemistry. However, this notion of universality is specific to known biochemistry and does not allow quantitative predictions about examples not yet observed. Here, we introduce a more generalizable concept of biochemical universality that is more akin to the kind of universality found in physics. Using annotated genomic datasets including an ensemble of 11,955 metagenomes, 1,282 archaea, 11,759 bacteria, and 200 eukaryotic taxa, we show how enzyme functions form universality classes with common scaling behavior in their relative abundances across the datasets. We verify that these scaling laws are not explained by the presence of compounds, reactions, and enzyme functions shared across known examples of life. We demonstrate how these scaling laws can be used as a tool for inferring properties of ancient life by comparing their predictions with a consensus model for the last universal common ancestor (LUCA). We also illustrate how network analyses shed light on the functional principles underlying the observed scaling behaviors. Together, our results establish the existence of a new kind of biochemical universality, independent of the details of life on Earth’s component chemistry, with implications for guiding our search for missing biochemical diversity on Earth or for biochemistries that might deviate from the exact chemical makeup of life as we know it, such as at the origins of life, in alien environments, or in the design of synthetic life.
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15
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The Substrate-Independence Theory: Advancing Constructor Theory to Scaffold Substrate Attributes for the Recursive Interaction between Knowledge and Information. SYSTEMS 2022. [DOI: 10.3390/systems10010007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The substrate-independence theory utilizes sensemaking techniques to provide cognitively based scaffolds that guide and structure learning. Scaffolds are cognitive abstractions of constraints that relate to information within a system. The substrate-independence theory concentrates on the flow of information as the underlying property of the host system. The substrate-independence theory views social systems as complex adaptive systems capable of repurposing their structure to combat external threats by utilizing constructors and substrates. Constructor theory is used to identify potential construction tasks, the legitimate input and output states that are possible, to map the desired change in the substrate’s attributes. Construction tasks can be mapped in advance for ordered and known environments. Construction tasks may also be mapped in either real-time or post hoc for unordered and complex environments using current sensemaking techniques. Mapping of the construction tasks in real-time becomes part of the landscape, and scaffolds are implemented to aid in achieving the desired state or move to a more manageable environment (e.g., from complex to complicated).
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16
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Ueltzhöffer K, Da Costa L, Cialfi D, Friston K. A Drive towards Thermodynamic Efficiency for Dissipative Structures in Chemical Reaction Networks. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1115. [PMID: 34573740 PMCID: PMC8472781 DOI: 10.3390/e23091115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 11/17/2022]
Abstract
Dissipative accounts of structure formation show that the self-organisation of complex structures is thermodynamically favoured, whenever these structures dissipate free energy that could not be accessed otherwise. These structures therefore open transition channels for the state of the universe to move from a frustrated, metastable state to another metastable state of higher entropy. However, these accounts apply as well to relatively simple, dissipative systems, such as convection cells, hurricanes, candle flames, lightning strikes, or mechanical cracks, as they do to complex biological systems. Conversely, interesting computational properties-that characterize complex biological systems, such as efficient, predictive representations of environmental dynamics-can be linked to the thermodynamic efficiency of underlying physical processes. However, the potential mechanisms that underwrite the selection of dissipative structures with thermodynamically efficient subprocesses is not completely understood. We address these mechanisms by explaining how bifurcation-based, work-harvesting processes-required to sustain complex dissipative structures-might be driven towards thermodynamic efficiency. We first demonstrate a simple mechanism that leads to self-selection of efficient dissipative structures in a stochastic chemical reaction network, when the dissipated driving chemical potential difference is decreased. We then discuss how such a drive can emerge naturally in a hierarchy of self-similar dissipative structures, each feeding on the dissipative structures of a previous level, when moving away from the initial, driving disequilibrium.
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Affiliation(s)
- Kai Ueltzhöffer
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK; (L.D.C.); (K.F.)
- Department of General Psychiatry, Center of Psychosocial Medicine, Heidelberg University, 69115 Heidelberg, Germany
| | - Lancelot Da Costa
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK; (L.D.C.); (K.F.)
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
| | - Daniela Cialfi
- Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, Economic and Quantitative Methods Section, University of Studies Gabriele d’Annunzio Chieti-Pescara, 65127 Pescara, Italy;
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK; (L.D.C.); (K.F.)
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17
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Marshall SM, Mathis C, Carrick E, Keenan G, Cooper GJT, Graham H, Craven M, Gromski PS, Moore DG, Walker SI, Cronin L. Identifying molecules as biosignatures with assembly theory and mass spectrometry. Nat Commun 2021; 12:3033. [PMID: 34031398 PMCID: PMC8144626 DOI: 10.1038/s41467-021-23258-x] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 04/07/2021] [Indexed: 01/14/2023] Open
Abstract
The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab.
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Affiliation(s)
| | - Cole Mathis
- School of Chemistry, University of Glasgow, Glasgow, UK
| | - Emma Carrick
- School of Chemistry, University of Glasgow, Glasgow, UK
| | - Graham Keenan
- School of Chemistry, University of Glasgow, Glasgow, UK
| | | | - Heather Graham
- Astrobiology Analytical Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | | | | | - Douglas G Moore
- Beyond Centre for Concepts in Fundamental Science, Arizona State University, Tempe, AZ, USA
| | - Sara I Walker
- Beyond Centre for Concepts in Fundamental Science, Arizona State University, Tempe, AZ, USA
| | - Leroy Cronin
- School of Chemistry, University of Glasgow, Glasgow, UK.
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18
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Bogdan P, Caetano-Anollés G, Jolles A, Kim H, Morris J, Murphy CA, Royer C, Snell EH, Steinbrenner A, Strausfeld N. Biological networks across scales. Integr Comp Biol 2021; 61:1991-2010. [PMID: 34021749 DOI: 10.1093/icb/icab069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, i.e. dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations-an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales.
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Affiliation(s)
- Paul Bogdan
- Ming-Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles
| | | | - Anna Jolles
- Department of Integrative Biology, Oregon State University, Corvallis
| | - Hyunju Kim
- The Beyond Center, Arizona State University, Tempe
| | - James Morris
- Baruch Institute for Marine and Coastal Sciences, University of South Carolina, Columbia
| | - Cheryl A Murphy
- Department of Fisheries and Wildlife, Michigan State University, East Lansing
| | | | - Edward H Snell
- Hauptman-Woodward Medical Research Institute and SUNY, Buffalo
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19
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Kempes CP, Follows MJ, Smith H, Graham H, House CH, Levin SA. Generalized Stoichiometry and Biogeochemistry for Astrobiological Applications. Bull Math Biol 2021; 83:73. [PMID: 34008062 PMCID: PMC8131296 DOI: 10.1007/s11538-021-00877-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/25/2021] [Indexed: 11/03/2022]
Abstract
A central need in the field of astrobiology is generalized perspectives on life that make it possible to differentiate abiotic and biotic chemical systems McKay (2008). A key component of many past and future astrobiological measurements is the elemental ratio of various samples. Classic work on Earth's oceans has shown that life displays a striking regularity in the ratio of elements as originally characterized by Redfield (Redfield 1958; Geider and La Roche 2002; Eighty years of Redfield 2014). The body of work since the original observations has connected this ratio with basic ecological dynamics and cell physiology, while also documenting the range of elemental ratios found in a variety of environments. Several key questions remain in considering how to best apply this knowledge to astrobiological contexts: How can the observed variation of the elemental ratios be more formally systematized using basic biological physiology and ecological or environmental dynamics? How can these elemental ratios be generalized beyond the life that we have observed on our own planet? Here, we expand recently developed generalized physiological models (Kempes et al. 2012, 2016, 2017, 2019) to create a simple framework for predicting the variation of elemental ratios found in various environments. We then discuss further generalizing the physiology for astrobiological applications. Much of our theoretical treatment is designed for in situ measurements applicable to future planetary missions. We imagine scenarios where three measurements can be made-particle/cell sizes, particle/cell stoichiometry, and fluid or environmental stoichiometry-and develop our theory in connection with these often deployed measurements.
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Affiliation(s)
| | - Michael J Follows
- Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hillary Smith
- Department of Geosciences, Pennsylvania State University, University Park, PA, USA
| | - Heather Graham
- NASA Goddard Spaceflight Center, Greenbelt, MD, USA
- Catholic University of America, Washington, DC, USA
| | - Christopher H House
- Department of Geosciences, Pennsylvania State University, University Park, PA, USA
| | - Simon A Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
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20
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Hartle H, Papadopoulos F, Krioukov D. Dynamic hidden-variable network models. Phys Rev E 2021; 103:052307. [PMID: 34134209 DOI: 10.1103/physreve.103.052307] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/12/2021] [Indexed: 11/07/2022]
Abstract
Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.
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Affiliation(s)
- Harrison Hartle
- Network Science Institute, Northeastern University, Boston, 02115 Massachusetts, USA
| | - Fragkiskos Papadopoulos
- Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 3036 Limassol, Cyprus
| | - Dmitri Krioukov
- Network Science Institute, Northeastern University, Boston, 02115 Massachusetts, USA.,Northeastern University, Departments of Physics, Mathematics, and Electrical & Computer Engineering, Boston, 02115 Massachusetts, USA
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21
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Smith HB, Kim H, Walker SI. Scarcity of scale-free topology is universal across biochemical networks. Sci Rep 2021; 11:6542. [PMID: 33753807 PMCID: PMC7985396 DOI: 10.1038/s41598-021-85903-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/19/2021] [Indexed: 01/31/2023] Open
Abstract
Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as "scale-free". Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks.
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Affiliation(s)
- Harrison B. Smith
- grid.215654.10000 0001 2151 2636School of Earth and Space Exploration, Arizona State University, Tempe, AZ USA ,grid.32197.3e0000 0001 2179 2105Present Address: Earth-Life Science Institute, Tokyo Institute of Technology, Meguro-ku, Tokyo Japan
| | - Hyunju Kim
- grid.215654.10000 0001 2151 2636School of Earth and Space Exploration, Arizona State University, Tempe, AZ USA ,grid.215654.10000 0001 2151 2636Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ USA ,grid.215654.10000 0001 2151 2636ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, AZ USA
| | - Sara I. Walker
- grid.215654.10000 0001 2151 2636School of Earth and Space Exploration, Arizona State University, Tempe, AZ USA ,grid.215654.10000 0001 2151 2636Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ USA ,grid.215654.10000 0001 2151 2636ASU-SFI Center for Biosocial Complex Systems, Arizona State University, Tempe, AZ USA ,grid.209665.e0000 0001 1941 1940Santa Fe Institute, Santa Fe, NM USA
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22
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Tran QP, Adam ZR, Fahrenbach AC. Prebiotic Reaction Networks in Water. Life (Basel) 2020; 10:E352. [PMID: 33339192 PMCID: PMC7765580 DOI: 10.3390/life10120352] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/05/2020] [Accepted: 12/06/2020] [Indexed: 02/07/2023] Open
Abstract
A prevailing strategy in origins of life studies is to explore how chemistry constrained by hypothetical prebiotic conditions could have led to molecules and system level processes proposed to be important for life's beginnings. This strategy has yielded model prebiotic reaction networks that elucidate pathways by which relevant compounds can be generated, in some cases, autocatalytically. These prebiotic reaction networks provide a rich platform for further understanding and development of emergent "life-like" behaviours. In this review, recent advances in experimental and analytical procedures associated with classical prebiotic reaction networks, like formose and Miller-Urey, as well as more recent ones are highlighted. Instead of polymeric networks, i.e., those based on nucleic acids or peptides, the focus is on small molecules. The future of prebiotic chemistry lies in better understanding the genuine complexity that can result from reaction networks and the construction of a centralised database of reactions useful for predicting potential network evolution is emphasised.
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Affiliation(s)
| | - Zachary R. Adam
- Department of Planetary Sciences, University of Arizona, Tucson, AZ 85721, USA;
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23
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Rubin S, Parr T, Da Costa L, Friston K. Future climates: Markov blankets and active inference in the biosphere. J R Soc Interface 2020; 17:20200503. [PMID: 33234063 PMCID: PMC7729048 DOI: 10.1098/rsif.2020.0503] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/26/2020] [Indexed: 02/01/2023] Open
Abstract
We formalize the Gaia hypothesis about the Earth climate system using advances in theoretical biology based on the minimization of variational free energy. This amounts to the claim that non-equilibrium steady-state dynamics-that underwrite our climate-depend on the Earth system possessing a Markov blanket. Our formalization rests on how the metabolic rates of the biosphere (understood as Markov blanket's internal states) change with respect to solar radiation at the Earth's surface (i.e. external states), through the changes in greenhouse and albedo effects (i.e. active states) and ocean-driven global temperature changes (i.e. sensory states). Describing the interaction between the metabolic rates and solar radiation as climatic states-in a Markov blanket-amounts to describing the dynamics of the internal states as actively inferring external states. This underwrites climatic non-equilibrium steady-state through free energy minimization and thus a form of planetary autopoiesis.
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Affiliation(s)
- Sergio Rubin
- Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, Louvain, Belgium
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Lancelot Da Costa
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
- Department of Mathematics, Imperial College London, London, UK
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
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Servadio JL, Machado G, Alvarez J, de Ferreira Lima Júnior FE, Vieira Alves R, Convertino M. Information differences across spatial resolutions and scales for disease surveillance and analysis: The case of Visceral Leishmaniasis in Brazil. PLoS One 2020; 15:e0235920. [PMID: 32678864 PMCID: PMC7367469 DOI: 10.1371/journal.pone.0235920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/25/2020] [Indexed: 12/26/2022] Open
Abstract
Nationwide disease surveillance at a high spatial resolution is desired for many infectious diseases, including Visceral Leishmaniasis. Statistical and mathematical models using data collected from surveillance activities often use a spatial resolution and scale either constrained by data availability or chosen arbitrarily. Sensitivity of model results to the choice of spatial resolution and scale is not, however, frequently evaluated. This study aims to determine if the choice of spatial resolution and scale are likely to impact statistical and mathematical analyses. Visceral Leishmaniasis in Brazil is used as a case study. Probabilistic characteristics of disease incidence, representing a likely outcome in a model, are compared across spatial resolutions and scales. Best fitting distributions were fit to annual incidence from 2004 to 2014 by municipality and by state. Best fits were defined as the distribution family and parameterization minimizing the sum of absolute error, evaluated through a simulated annealing algorithm. Gamma and Poisson distributions provided best fits for incidence, both among individual states and nationwide. Comparisons of distributions using Kullback-Leibler divergence shows that incidence by state and by municipality do not follow distributions that provide equivalent information. Few states with Gamma distributed incidence follow a distribution closely resembling that for national incidence. These results demonstrate empirically how choice of spatial resolution and scale can impact mathematical and statistical models.
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Affiliation(s)
- Joseph L. Servadio
- Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, Minnesota, United States of America
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Julio Alvarez
- VISAVET Health Surveillance Center, Universidad Complutense, Madrid, Spain
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense, Madrid, Spain
| | | | - Renato Vieira Alves
- Secretaria de Vigilância em Saúde, Ministério da Saúde (SVS-MH), Brasília, Brazil
| | - Matteo Convertino
- Nexus Group, Graduate School of Information Science and Technology and GI-CoRE Station for Big-Data and Cybersecurity, Hokkaido University, Sapporo, Hokkaido, Japan
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25
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Spontaneous formation of autocatalytic sets with self-replicating inorganic metal oxide clusters. Proc Natl Acad Sci U S A 2020; 117:10699-10705. [PMID: 32371490 PMCID: PMC7245103 DOI: 10.1073/pnas.1921536117] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Self-replication is an important property of life, yet no one knows how it arose, and the machinery found in modern cells is far too complex to have formed by chance. One suggestion is that simple networks may become able to cooperate and hence replicate together forming autocatalytic sets, but no simple systems have been found. Here we present an inorganic autocatalytic, based on molybdenum blue, that is formed spontaneously when a simple inorganic salt of sodium molybdate is reduced under acidic conditions. This study demonstrates how autocatalytic sets, based on simple inorganic salts, can lead to the spontaneous emergence of self-replicating systems and solves the mystery of how gigantic molecular nanostructures of molybdenum blue can form in the first place. Here we show how a simple inorganic salt can spontaneously form autocatalytic sets of replicating inorganic molecules that work via molecular recognition based on the {PMo12} ≡ [PMo12O40]3– Keggin ion, and {Mo36} ≡ [H3Mo57M6(NO)6O183(H2O)18]22– cluster. These small clusters are able to catalyze their own formation via an autocatalytic network, which subsequently template the assembly of gigantic molybdenum-blue wheel {Mo154} ≡ [Mo154O462H14(H2O)70]14–, {Mo132} ≡ [MoVI72MoV60O372(CH3COO)30(H2O)72]42– ball-shaped species containing 154 and 132 molybdenum atoms, and a {PMo12}⊂{Mo124Ce4} ≡ [H16MoVI100MoV24Ce4O376(H2O)56 (PMoVI10MoV2O40)(C6H12N2O4S2)4]5– nanostructure. Kinetic investigations revealed key traits of autocatalytic systems including molecular recognition and kinetic saturation. A stochastic model confirms the presence of an autocatalytic network involving molecular recognition and assembly processes, where the larger clusters are the only products stabilized by the cycle, isolated due to a critical transition in the network.
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Bartlett S, Wong ML. Defining Lyfe in the Universe: From Three Privileged Functions to Four Pillars. Life (Basel) 2020; 10:E42. [PMID: 32316364 PMCID: PMC7235751 DOI: 10.3390/life10040042] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 01/08/2023] Open
Abstract
Motivated by the need to paint a more general picture of what life is-and could be-with respect to the rest of the phenomena of the universe, we propose a new vocabulary for astrobiological research. Lyfe is defined as any system that fulfills all four processes of the living state, namely: dissipation, autocatalysis, homeostasis, and learning. Life is defined as the instance of lyfe that we are familiar with on Earth, one that uses a specific organometallic molecular toolbox to record information about its environment and achieve dynamical order by dissipating certain planetary disequilibria. This new classification system allows the astrobiological community to more clearly define the questions that propel their research-e.g., whether they are developing a historical narrative to explain the origin of life (on Earth), or a universal narrative for the emergence of lyfe, or whether they are seeking signs of life specifically, or lyfe at large across the universe. While the concept of "life as we don't know it" is not new, the four pillars of lyfe offer a novel perspective on the living state that is indifferent to the particular components that might produce it.
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Affiliation(s)
- Stuart Bartlett
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Michael L. Wong
- Department of Astronomy and Astrobiology Program, University of Washington, Seattle, WA 98195, USA;
- NASA Nexus for Exoplanet System Science’s Virtual Planetary Laboratory, University of Washington, Seattle, WA 98195, USA
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Preiner M, Asche S, Becker S, Betts HC, Boniface A, Camprubi E, Chandru K, Erastova V, Garg SG, Khawaja N, Kostyrka G, Machné R, Moggioli G, Muchowska KB, Neukirchen S, Peter B, Pichlhöfer E, Radványi Á, Rossetto D, Salditt A, Schmelling NM, Sousa FL, Tria FDK, Vörös D, Xavier JC. The Future of Origin of Life Research: Bridging Decades-Old Divisions. Life (Basel) 2020; 10:E20. [PMID: 32110893 PMCID: PMC7151616 DOI: 10.3390/life10030020] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/19/2020] [Accepted: 02/21/2020] [Indexed: 12/12/2022] Open
Abstract
Research on the origin of life is highly heterogeneous. After a peculiar historical development, it still includes strongly opposed views which potentially hinder progress. In the 1st Interdisciplinary Origin of Life Meeting, early-career researchers gathered to explore the commonalities between theories and approaches, critical divergence points, and expectations for the future. We find that even though classical approaches and theories-e.g. bottom-up and top-down, RNA world vs. metabolism-first-have been prevalent in origin of life research, they are ceasing to be mutually exclusive and they can and should feed integrating approaches. Here we focus on pressing questions and recent developments that bridge the classical disciplines and approaches, and highlight expectations for future endeavours in origin of life research.
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Affiliation(s)
- Martina Preiner
- Institute of Molecular Evolution, University of Düsseldorf, 40225 Düsseldorf, Germany; (S.G.G.); (F.D.K.T.)
| | - Silke Asche
- School of Chemistry, University of Glasgow, Glasgow G128QQ, UK;
| | - Sidney Becker
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK;
| | - Holly C. Betts
- School of Earth Sciences, University of Bristol, Bristol BS8 1RL, UK;
| | - Adrien Boniface
- Environmental Microbial Genomics, Laboratoire Ampère, Ecole Centrale de Lyon, Université de Lyon, 69130 Ecully, France;
| | - Eloi Camprubi
- Origins Center, Department of Earth Sciences, Utrecht University, 3584 CB Utrecht, The Netherlands;
| | - Kuhan Chandru
- Space Science Center (ANGKASA), Institute of Climate Change, Level 3, Research Complex, National University of Malaysia, UKM Bangi 43600, Selangor, Malaysia;
- Department of Physical Chemistry, University of Chemistry and Technology, Prague, Technicka 5, 16628 Prague 6–Dejvice, Czech Republic
| | - Valentina Erastova
- UK Centre for Astrobiology, School of Chemistry, University of Edinburgh, Edinburgh EH9 3FJ, UK;
| | - Sriram G. Garg
- Institute of Molecular Evolution, University of Düsseldorf, 40225 Düsseldorf, Germany; (S.G.G.); (F.D.K.T.)
| | - Nozair Khawaja
- Institut für Geologische Wissenschaften, Freie Universität Berlin, 12249 Berlin, Germany;
| | | | - Rainer Machné
- Institute of Synthetic Microbiology, University of Düsseldorf, 40225 Düsseldorf, Germany; (R.M.); (N.M.S.)
- Quantitative and Theoretical Biology, University of Düsseldorf, 40225 Düsseldorf, Germany
| | - Giacomo Moggioli
- School of Biological and Chemical Sciences, Queen Mary University of London, London E1 4DQ, UK;
| | - Kamila B. Muchowska
- Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, 67000 Strasbourg, France;
| | - Sinje Neukirchen
- Archaea Biology and Ecogenomics Division, University of Vienna, 1090 Vienna, Austria; (S.N.); (E.P.); (F.L.S.)
| | - Benedikt Peter
- Cellular and Molecular Biophysics, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany;
| | - Edith Pichlhöfer
- Archaea Biology and Ecogenomics Division, University of Vienna, 1090 Vienna, Austria; (S.N.); (E.P.); (F.L.S.)
| | - Ádám Radványi
- Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary (D.V.)
- Institute of Evolution, MTA Centre for Ecological Research, Klebelsberg Kuno u. 3., H-8237 Tihany, Hungary
| | - Daniele Rossetto
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123 Trento, Italy;
| | - Annalena Salditt
- Systems Biophysics, Physics Department, Ludwig-Maximilians-Universität München, 80799 Munich, Germany;
| | - Nicolas M. Schmelling
- Institute of Synthetic Microbiology, University of Düsseldorf, 40225 Düsseldorf, Germany; (R.M.); (N.M.S.)
- Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, 50674 Cologne, Germany
| | - Filipa L. Sousa
- Archaea Biology and Ecogenomics Division, University of Vienna, 1090 Vienna, Austria; (S.N.); (E.P.); (F.L.S.)
| | - Fernando D. K. Tria
- Institute of Molecular Evolution, University of Düsseldorf, 40225 Düsseldorf, Germany; (S.G.G.); (F.D.K.T.)
| | - Dániel Vörös
- Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary (D.V.)
- Institute of Evolution, MTA Centre for Ecological Research, Klebelsberg Kuno u. 3., H-8237 Tihany, Hungary
| | - Joana C. Xavier
- Institute of Molecular Evolution, University of Düsseldorf, 40225 Düsseldorf, Germany; (S.G.G.); (F.D.K.T.)
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