Joao Guimaraes

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University of California, Berkeley
Guimaraes, Joao

Joao Guimaraes received his B.Eng. in Computer Science and Systems Engineering from the University of Minho, Portugal. In 2009, he started his Ph.D. in Computational Biology supervised by Adam Arkin at the University of California, Berkeley and Miguel Rocha at the University of Minho, Portugal.

As part of his graduate studies, he was also a researcher at the BIOFAB: International Open Facility Advancing Biotechnology. The main focus of his graduate studies has been the development of computational models to understand gene regulation in natural systems and to aid engineering synthetic genetic elements leading to a predictable control of gene expression in bacteria.

Tue July 9 | 2:00 - 4:00
ABSTRACT: Quantitative estimation of activity and quality for collections of functional genetic elements and definition of design principles for predictable gene expression.

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 propose a new statistic, biomolecular part “quality,” for tracking quantitative variation in part performance across changing contexts. We used this metric to identify design flaws leading to unpredictable behavior, and motivate the engineering of improved genetic elements that can reliably express sequence distinct genes across a 1000-fold observed dynamic range and within 2-fold relative target expression windows with ~93% reliability. Lastly, we characterized a set of transcription terminators with widely variable efficiencies; used co-transcriptional folding simulations to identify context effects leading to deviant behavior; and developed a sequence-activity relationship model for this class of elements. In summary, we developed a gene expression control system comprised of transcription and translation initiation elements, and transcription terminators with predictable performance that enable reliable forward engineering of gene expression at genome scales.