Predictive biology: modelling, understanding and harnessing microbial complexity
Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions,...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/132619 |
| _version_ | 1826200205876264960 |
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| author | Lopatkin, Allison J. Collins, James J. |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Lopatkin, Allison J. Collins, James J. |
| author_sort | Lopatkin, Allison J. |
| collection | MIT |
| description | Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable. |
| first_indexed | 2024-09-23T11:32:47Z |
| format | Article |
| id | mit-1721.1/132619 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:32:47Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1326192022-10-01T04:22:23Z Predictive biology: modelling, understanding and harnessing microbial complexity Lopatkin, Allison J. Collins, James J. Massachusetts Institute of Technology. Institute for Medical Engineering & Science Massachusetts Institute of Technology. Department of Biological Engineering Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable. Defence Threat Reduction Agency (Grant HDTRA1-15-1-0051) 2021-09-21T19:45:14Z 2021-09-21T19:45:14Z 2020-05 2020-04 2021-09-21T14:05:26Z Article http://purl.org/eprint/type/JournalArticle 1740-1526 1740-1534 https://hdl.handle.net/1721.1/132619 Lopatkin, Allison J. and James J. Collins. "Predictive biology: modelling, understanding and harnessing microbial complexity." Nature Reviews Microbiology 18, 9 (September 2020): 507–520. © 2020 Springer Nature Limited en http://dx.doi.org/10.1038/s41579-020-0372-5 Nature Reviews Microbiology Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer Science and Business Media LLC Prof. Collins |
| spellingShingle | Lopatkin, Allison J. Collins, James J. Predictive biology: modelling, understanding and harnessing microbial complexity |
| title | Predictive biology: modelling, understanding and harnessing microbial complexity |
| title_full | Predictive biology: modelling, understanding and harnessing microbial complexity |
| title_fullStr | Predictive biology: modelling, understanding and harnessing microbial complexity |
| title_full_unstemmed | Predictive biology: modelling, understanding and harnessing microbial complexity |
| title_short | Predictive biology: modelling, understanding and harnessing microbial complexity |
| title_sort | predictive biology modelling understanding and harnessing microbial complexity |
| url | https://hdl.handle.net/1721.1/132619 |
| work_keys_str_mv | AT lopatkinallisonj predictivebiologymodellingunderstandingandharnessingmicrobialcomplexity AT collinsjamesj predictivebiologymodellingunderstandingandharnessingmicrobialcomplexity |