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Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria
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Farasat, Iman, Kushwaha, Manish, Collens, Jason, Easterbrook, Michael, Guido, Matthew and Salis, Howard M. (2014) Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria. Molecular Systems Biology, 10 (6). pp. 1-18. 731. doi:10.15252/msb.20134955 ISSN 1744-4292.
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Official URL: http://dx.doi.org/10.15252/msb.20134955
Abstract
Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | ||||||
| Library of Congress Subject Headings (LCSH): | Synthetic biology | ||||||
| Journal or Publication Title: | Molecular Systems Biology | ||||||
| Publisher: | Nature Publishing Group | ||||||
| ISSN: | 1744-4292 | ||||||
| Official Date: | June 2014 | ||||||
| Dates: |
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| Volume: | 10 | ||||||
| Number: | 6 | ||||||
| Number of Pages: | 18 | ||||||
| Page Range: | pp. 1-18 | ||||||
| Article Number: | 731 | ||||||
| DOI: | 10.15252/msb.20134955 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 31 December 2015 | ||||||
| Date of first compliant Open Access: | 31 December 2015 | ||||||
| Funder: | United States. Office of Naval Research, National Science Foundation (U.S.) (NSF), United States. Defense Advanced Research Projects Agency (DARPA), Pennsylvania State University. Institutes of the Environment, Amazon.com (Firm) | ||||||
| Grant number: | N00014-13-1-0074 (ONR), CBET-1253641 (NSF) |
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