Difference between revisions of "Journal:National and transnational security implications of asymmetric access to and use of biological data"

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==Abstract==
==Abstract==
Biology and [[biotechnology]] have changed dramatically during the past 20 years, in part because of increases in computational capabilities and use of engineering principles to study biology. The advances in supercomputing, data storage capacity, and [[Cloud computing|cloud platforms]] enable scientists throughout the world to generate, analyze, share, and store vast amounts of data, some of which are biological and much of which may be used to understand the human condition, agricultural systems, evolution, and environmental ecosystems. These advances and applications have enabled: (1) the emergence of data science, which involves the development of new algorithms to analyze and [[Data visualization|visualize data]]; and (2) the use of engineering approaches to manipulate or create new biological organisms that have specific functions, such as production of industrial chemical precursors and development of environmental bio-based sensors. Several biological sciences fields harness the capabilities of computer, data, and engineering sciences, including synthetic biology, precision medicine, precision agriculture, and systems biology. These advances and applications are not limited to one country. This capability has economic and physical consequences but is vulnerable to unauthorized intervention. Healthcare and [[Genomics|genomic]] information of patients, [[information]] about pharmaceutical and biotechnology products in development, and results of scientific research have been stolen by state and non-state actors through infiltration of databases and computer systems containing this information. Countries have developed their own policies for governing data generation, access, and sharing with foreign entities, resulting in asymmetry of data sharing. This paper describes security implications of asymmetric access to and use of biological data.
Biology and [[biotechnology]] have changed dramatically during the past 20 years, in part because of increases in computational capabilities and use of engineering principles to study biology. The advances in supercomputing, data storage capacity, and [[Cloud computing|cloud platforms]] enable scientists throughout the world to generate, analyze, share, and store vast amounts of data, some of which are biological and much of which may be used to understand the human condition, agricultural systems, evolution, and environmental ecosystems. These advances and applications have enabled: (1) the emergence of data science, which involves the development of new algorithms to analyze and [[Data visualization|visualize data]]; and (2) the use of engineering approaches to manipulate or create new biological organisms that have specific functions, such as production of industrial chemical precursors and development of environmental bio-based sensors. Several biological sciences fields harness the capabilities of computer, data, and engineering sciences, including synthetic biology, precision medicine, precision agriculture, and systems biology. These advances and applications are not limited to one country. This capability has economic and physical consequences but is vulnerable to unauthorized intervention. Healthcare and [[Genomics|genomic]] information of patients, [[information]] about pharmaceutical and biotechnology products in development, and results of scientific [[research]] have been stolen by state and non-state actors through infiltration of databases and computer systems containing this information. Countries have developed their own policies for governing data generation, access, and sharing with foreign entities, resulting in asymmetry of data sharing. This paper describes security implications of asymmetric access to and use of biological data.


'''Keywords''': biotechnology, cybersecurity, information security, data vulnerability, biological data, biosecurity, data access, data protection
'''Keywords''': biotechnology, cybersecurity, information security, data vulnerability, biological data, biosecurity, data access, data protection


 
==Introduction==
Advances in computer science, engineering, and data science have changed research, development, and application of biology and biotechnology in the United States and internationally. Examples of changes include: (a) increased reliance on internet connectivity for research and [[laboratory]] operations<ref name=AccentureTheFuture15">{{cite web |url=https://www.accenture.com/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Dualpub_20/Accenture-15-1429U-FutureOfApps-LSCS-v5-web.pdf |format=PDF |title=The Future of Applications in Life Sciences |author=Accenture |publisher=Accenture |date=2015}}</ref><ref name="BajemaTheDig18">{{cite journal |title=The digitization of biology: Understanding the new risks and implications for governance |journal=Emergence & Convergence |author=Bajema, N.E.; DiEuliis, D.; Lutes, C.; Lim, Y.-B. |page=3 |year=2018 |url=https://wmdcenter.ndu.edu/Media/News/Article/1569559/the-digitization-of-biology-understanding-the-new-risks-and-implications-for-go/}}</ref><ref name="OlenaBringing18">{{cite web |url=https://www.the-scientist.com/bio-business/bringing-the-internet-of-things-into-the-lab-64265 |title=Bringing the Internet of Things into the Lab |author=Olena, A. |work=The Scientist |date=01 June 2018}}</ref>; (b) increased use of automation in life-science laboratories<ref name="ChapmanLab03">{{cite journal |title=Lab automation and robotics: Automation on the move |journal=Nature |author=Chapman, T. |volume=421 |issue=6923 |pages=661, 663, 665–6 |year=2003 |doi=10.1038/421661a |pmid=12571603}}</ref>; (c) application of the “design-build-test” paradigm to create new biological organisms<ref name="AgapakisDesign14">{{cite journal |title=Designing synthetic biology |journal=ACS Synthetic Biology |author=Agapakis, C.M. |volume=3 |issue=3 |pages=121–8 |year=2014 |doi=10.1021/sb4001068 |pmid=24156739}}</ref><ref name="CarbonellAnAuto18">{{cite journal |title=An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals |journal=Communications Biology |author=Carbonell, P.; Jervis, A.J.; Robinson, C.J. et al. |volume=1 |pages=66 |year=2018 |doi=10.1038/s42003-018-0076-9 |pmid=30271948 |pmc=PMC6123781}}</ref>; (d) increased generation, analyses, and computational modeling of information about biological systems, cells, and molecules<ref name="ThurowLab04">{{cite journal |title=Laboratory Information Management Systems for Life Science Applications |journal=Organic Process Researh & Development |author=Thurow, K.; Göde, B.; Dingerdissen, U. Stoll, N. |volume=8 |issue=6 |pages=970–982 |year=2004 |doi=10.1021/op040017s}}</ref><ref name="WalpoleMulti13">{{cite journal |title=Multiscale computational models of complex biological systems |journal=Annual Review of Biomedical Engineering |author=Walpole, J.; Papin, J.A.; Peirce, S.M. |volume=15 |pages=137–54 |year=2013 |doi=10.1146/annurev-bioeng-071811-150104 |pmid=23642247 |pmc=PMC3970111}}</ref>; (e) treatment of organisms and DNA as materials rather than phenomena to study<ref name="ServiceDNA17">{{cite web |url=https://www.sciencemag.org/news/2017/03/dna-could-store-all-worlds-data-one-room |title=DNA could store all of the world's data in one room |author=Service, R.F. |work=Science |date=02 March 2017 |doi=10.1126/science.aal0852}}</ref><ref name="AndersonSynth18">{{cite journal |title=Synthetic biology strategies for improving microbial synthesis of "green" biopolymers |journal=Journal of Biological Chemistry |author=Anderson, L.A.; Islam, M.A.; Prather, K.L.J. |volume=293 |issue=14 |pages=5053-5061 |year=2018 |doi=10.1074/jbc.TM117.000368 |pmid=29339554 |pmc=PMC5892568}}</ref><ref name="PatelDNA18">{{cite web |url=https://spectrum.ieee.org/the-human-os/biomedical/devices/dna-data-storage-gets-random-access |title=DNA Data Storage Gets Random Access |work=IEEE Spectrum |author=Patel, P. |date=20 February 2018}}</ref>; and (f) new funders such as venture capital, crowdfunding platforms, and foreign companies and governments.<ref name="vonKroghTheChang12">{{cite journal |title=The changing face of corporate venturing in biotechnology |journal=Nature Biotechnology |author=von Krogh, G.; Battistini, B.; Pachidou, F.; Baschera, P. |volume=30 |issue=10 |pages=911–5 |year=2012 |doi=10.1038/nbt.2383 |pmid=23051802}}</ref><ref name="ChaCrowd15">{{cite web |url=https://www.washingtonpost.com/national/health-science/crowdfunding-propels-scientific-research/2015/01/18/c1937690-9758-11e4-8005-1924ede3e54a_story.html?utm_term=.734eb498edb5 |title=Crowdfunding propels scientific research |author=Cha, A.E. |work=The Washington Post |date=18 January 2015}}</ref><ref name="MervisData17">{{cite web |url=https://www.sciencemag.org/news/2017/03/data-check-us-government-share-basic-research-funding-falls-below-50 |title=Data check: U.S. government share of basic research funding falls below 50% |author=Mervis, J. |work=Science |date=09 March 2017 |doi=10.1126/science.aal0890}}</ref> These changes have transformed the scientific, agricultural, and health communities' ability to understand and manipulate the world around them. In addition, the changes have enabled an influx of new practitioners and problem-solvers into biology, providing opportunities for education and research all over the world.




Revision as of 20:51, 20 May 2019

Full article title National and transnational security implications of asymmetric access to and use of biological data
Journal Frontiers in Bioengineering and Biotechnology
Author(s) Berger, Kavita M.; Schneck, Phyllis A.
Author affiliation(s) Gryphon Scientific, LLC; Promontory Financial Group, an IBM Company
Primary contact Email: kberger at gryphonscientific dot com
Editors Murch, Randall S.
Year published 2019
Volume and issue 7
Page(s) 21
DOI 10.3389/fbioe.2019.00021
ISSN 2296-4185
Distribution license Creative Commons Attribution 4.0 International
Website https://www.frontiersin.org/articles/10.3389/fbioe.2019.00021/full
Download https://www.frontiersin.org/articles/10.3389/fbioe.2019.00021/pdf (PDF)
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Abstract

Biology and biotechnology have changed dramatically during the past 20 years, in part because of increases in computational capabilities and use of engineering principles to study biology. The advances in supercomputing, data storage capacity, and cloud platforms enable scientists throughout the world to generate, analyze, share, and store vast amounts of data, some of which are biological and much of which may be used to understand the human condition, agricultural systems, evolution, and environmental ecosystems. These advances and applications have enabled: (1) the emergence of data science, which involves the development of new algorithms to analyze and visualize data; and (2) the use of engineering approaches to manipulate or create new biological organisms that have specific functions, such as production of industrial chemical precursors and development of environmental bio-based sensors. Several biological sciences fields harness the capabilities of computer, data, and engineering sciences, including synthetic biology, precision medicine, precision agriculture, and systems biology. These advances and applications are not limited to one country. This capability has economic and physical consequences but is vulnerable to unauthorized intervention. Healthcare and genomic information of patients, information about pharmaceutical and biotechnology products in development, and results of scientific research have been stolen by state and non-state actors through infiltration of databases and computer systems containing this information. Countries have developed their own policies for governing data generation, access, and sharing with foreign entities, resulting in asymmetry of data sharing. This paper describes security implications of asymmetric access to and use of biological data.

Keywords: biotechnology, cybersecurity, information security, data vulnerability, biological data, biosecurity, data access, data protection

Introduction

Advances in computer science, engineering, and data science have changed research, development, and application of biology and biotechnology in the United States and internationally. Examples of changes include: (a) increased reliance on internet connectivity for research and laboratory operations[1][2][3]; (b) increased use of automation in life-science laboratories[4]; (c) application of the “design-build-test” paradigm to create new biological organisms[5][6]; (d) increased generation, analyses, and computational modeling of information about biological systems, cells, and molecules[7][8]; (e) treatment of organisms and DNA as materials rather than phenomena to study[9][10][11]; and (f) new funders such as venture capital, crowdfunding platforms, and foreign companies and governments.[12][13][14] These changes have transformed the scientific, agricultural, and health communities' ability to understand and manipulate the world around them. In addition, the changes have enabled an influx of new practitioners and problem-solvers into biology, providing opportunities for education and research all over the world.


References

  1. ↑ Accenture (2015). "The Future of Applications in Life Sciences" (PDF). Accenture. https://www.accenture.com/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Dualpub_20/Accenture-15-1429U-FutureOfApps-LSCS-v5-web.pdf. 
  2. ↑ Bajema, N.E.; DiEuliis, D.; Lutes, C.; Lim, Y.-B. (2018). "The digitization of biology: Understanding the new risks and implications for governance". Emergence & Convergence: 3. https://wmdcenter.ndu.edu/Media/News/Article/1569559/the-digitization-of-biology-understanding-the-new-risks-and-implications-for-go/. 
  3. ↑ Olena, A. (1 June 2018). "Bringing the Internet of Things into the Lab". The Scientist. https://www.the-scientist.com/bio-business/bringing-the-internet-of-things-into-the-lab-64265. 
  4. ↑ Chapman, T. (2003). "Lab automation and robotics: Automation on the move". Nature 421 (6923): 661, 663, 665–6. doi:10.1038/421661a. PMID 12571603. 
  5. ↑ Agapakis, C.M. (2014). "Designing synthetic biology". ACS Synthetic Biology 3 (3): 121–8. doi:10.1021/sb4001068. PMID 24156739. 
  6. ↑ Carbonell, P.; Jervis, A.J.; Robinson, C.J. et al. (2018). "An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals". Communications Biology 1: 66. doi:10.1038/s42003-018-0076-9. PMC PMC6123781. PMID 30271948. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123781. 
  7. ↑ Thurow, K.; Göde, B.; Dingerdissen, U. Stoll, N. (2004). "Laboratory Information Management Systems for Life Science Applications". Organic Process Researh & Development 8 (6): 970–982. doi:10.1021/op040017s. 
  8. ↑ Walpole, J.; Papin, J.A.; Peirce, S.M. (2013). "Multiscale computational models of complex biological systems". Annual Review of Biomedical Engineering 15: 137–54. doi:10.1146/annurev-bioeng-071811-150104. PMC PMC3970111. PMID 23642247. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3970111. 
  9. ↑ Service, R.F. (2 March 2017). "DNA could store all of the world's data in one room". Science. doi:10.1126/science.aal0852. https://www.sciencemag.org/news/2017/03/dna-could-store-all-worlds-data-one-room. 
  10. ↑ Anderson, L.A.; Islam, M.A.; Prather, K.L.J. (2018). "Synthetic biology strategies for improving microbial synthesis of "green" biopolymers". Journal of Biological Chemistry 293 (14): 5053-5061. doi:10.1074/jbc.TM117.000368. PMC PMC5892568. PMID 29339554. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5892568. 
  11. ↑ Patel, P. (20 February 2018). "DNA Data Storage Gets Random Access". IEEE Spectrum. https://spectrum.ieee.org/the-human-os/biomedical/devices/dna-data-storage-gets-random-access. 
  12. ↑ von Krogh, G.; Battistini, B.; Pachidou, F.; Baschera, P. (2012). "The changing face of corporate venturing in biotechnology". Nature Biotechnology 30 (10): 911–5. doi:10.1038/nbt.2383. PMID 23051802. 
  13. ↑ Cha, A.E. (18 January 2015). "Crowdfunding propels scientific research". The Washington Post. https://www.washingtonpost.com/national/health-science/crowdfunding-propels-scientific-research/2015/01/18/c1937690-9758-11e4-8005-1924ede3e54a_story.html?utm_term=.734eb498edb5. 
  14. ↑ Mervis, J. (9 March 2017). "Data check: U.S. government share of basic research funding falls below 50%". Science. doi:10.1126/science.aal0890. https://www.sciencemag.org/news/2017/03/data-check-us-government-share-basic-research-funding-falls-below-50. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added.

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