Engineered Gene Circuits

Jeff Hasty (UCSD)

Abstract

Uncovering the structure and function of gene regulatory networks has become one of the central challenges of the post-genomic era. Theoretical models of protein-DNA feedback loops and gene regulatory networks have long been proposed, and recently, certain qualitative features of such models have been experimentally corroborated. For the first portion of the presentation, recent progress in constructing a genetic oscillator based on design criteria generated from computational modeling will be discussed. This oscillator is robust, with all cells oscillating with a characteristic frequency that can be tuned with external inducers or temperature shifts. The oscillator network will be shown to be an interesting system for exploring the loss of synchronization at the colony level. In the second part of the presentation, the response of metabolic gene regulation to periodic changes in the external carbon source will be discussed. The central finding is the metabolic regulatory system acts as a low-pass filter that reliably responds to a slowly changing environment, while effectively ignoring fluctuations that are too fast for the cell to mount an efficient response. Computational modeling calibrated with experimental data is used to determine that frequency selection in the system is controlled by the interaction of coupled positive feedback networks governing the signal transduction of alternative carbon sources. The simulations suggest that the feedback loops may confer a robustness to environmental fluctuations on cells regardless of deficiencies in network components. This prediction is validated with an experimental comparison of two cellular strains that exhibit the same filtering properties despite having markedly different induction characteristics. Finally, fluctuations originating from small molecule-number effects will be discussed in the context of model predictions, and the experimental validation of these stochastic effects underscores the importance of internal noise in gene expression. The underlying methodology highlights the utility of engineering-based methods in the design of synthetic gene regulatory networks.

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