Open AccessEfficient search, mapping, and optimization of multi‐protein genetic systems in diverse bacteria
Author(s) -
Farasat Iman,
Kushwaha Manish,
Collens Jason,
Easterbrook Michael,
Guido Matthew,
Salis Howard M
Publication year - 2014
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20134955
Subject(s) - biology , computational biology , systems biology , plasmid , expression (computer science) , sequence space , sequence (biology) , genome , translation (biology) , computer science , genetics , gene , messenger rna , mathematics , pure mathematics , banach space , programming language
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.