Design principles for controlling gene expression in bacteria

Speaker: João Guimarães , University of California, Berkeley

Date: Thursday, October 10, 2013

Time: 9:00 AM to 10:30 AM Note: all times are in the Eastern Time Zone

Refreshments: 9:00 AM

Public: Yes

Location: 32-D463 (Star)

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Host: Manolis Kellis

Contact: derek aylward, 6177154882, derekaylward@csail.mit.edu

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Reminders to: kellislab@mit.edu

Reminder Subject: TALK: João Guimarães ,Design principles for controlling gene expression in bacteria

Control of gene expression underlies the majority of cellular processes and, hence, it is of utmost importance to understand how living organisms tailor protein levels precisely at all times. Additionally, it has recently become critical to develop genetic tools enabling reliable control of gene expression within synthetic circuits for biotechnology purposes.

To afford efficient engineering of biological systems, synthetic biologists seek parts (DNA segments) with diverse and reliable functional properties. One of the main hurdles faced by synthetic biology is the unpredictable behavior resulting from the reuse of genetic elements whose activities vary across changing contexts. Methods are lacking for researchers to affordably coordinate the quantification and analysis of part performance in different environments, as needed to identify, evaluate and improve problematic part types. Here we demonstrate how the combination of careful experimental designs and appropriate statistical frameworks can be used for quantifying the performance of genetic elements as they are reused in varying contexts. This methodology revealed design flaws of current gene expression platforms leading to unpredictable behavior, and it further motivated the engineering of enhanced genetic elements that can reliably express sequence distinct genes across a large dynamic range.
We also show how the use of integrative approaches can yield quantitative models for different gene expression regulation systems, and elucidate the relevance of the multiple sequence determinants on the observed phenotype. In particular, we have developed sequence-activity relationship models for different regulatory mechanisms, including small RNA, transcription termination and translation efficiency regulation. Such models are not only important to understand the functional determinants of a given regulatory system, but also to enable forward design of new regulatory sequences with predictable behavior.

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Created by derek aylward Email at Tuesday, September 24, 2013 at 10:35 AM.