Quantitative estimation of activity and quality for collections of functional genetic elements

• Published in: Nature methods Vol. 10; no. 4; pp. 347 - 353
• Main Authors: Mutalik, Vivek K, Guimaraes, Joao C, Cambray, Guillaume, Mai, Quynh-Anh, Christoffersen, Marc Juul, Martin, Lance, Yu, Ayumi, Lam, Colin, Rodriguez, Cesar, Bennett, Gaymon, Keasling, Jay D, Endy, Drew, Arkin, Adam P
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2013; Nature Publishing Group
• Subjects: 631/114/2415; 631/1647/1511; 631/1647/2017/1958; 631/208; 631/208/199; 631/337/574; Animals; Bacterial Proteins; Biodiversity and Ecology; Bioengineering; Bioengineering - methods; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; E coli; Environmental Sciences; Escherichia coli; Escherichia coli - genetics; Escherichia coli - metabolism; Gene Expression Regulation, Bacterial - physiology; Gene Library; Genetic engineering; Genetic translation; Genomes; Identification and classification; Life Sciences; Peptide Chain Initiation, Translational; Peptide Initiation Factors - metabolism; Prokaryotes; Prokaryotic Initiation Factors - metabolism; Promoters (Genetics); Proteomics; Transcription, Genetic; Variance analysis
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.2403

This linear ANOVA-based method quantifies the activity of different combinations of genetic elements and assigns a score that indicates the variation in performance across changing contexts. The practice of engineering biology now depends on the ad hoc reuse of genetic elements whose precise activities vary across changing contexts. Methods are lacking for researchers to affordably coordinate the quantification and analysis of part performance across varied environments, as needed to identify, evaluate and improve problematic part types. We developed an easy-to-use analysis of variance (ANOVA) framework for quantifying the performance of genetic elements. For proof of concept, we assembled and analyzed combinations of prokaryotic transcription and translation initiation elements in Escherichia coli . We determined how estimation of part activity relates to the number of unique element combinations tested, and we show how to estimate expected ensemble-wide part activity from just one or two measurements. We propose a new statistic, biomolecular part 'quality', for tracking quantitative variation in part performance across changing contexts.