Enhancing Robustness of Gene Regulatory Networks

University Of Washington, Seattle WA

Investigators

Abstract

Robustness in synthetic genetic circuits is a central issue for the reliable operation of engineered genetic devices. This project is anticipated to uncover principles of genetic circuit design that can lead to the robust performance of such engineered devices. The research will also advance our understanding of the functional life span of synthetically engineered organisms in different environments, and aid in devising active mechanisms to enhance or attenuate robustness. The wider implication of this this research is that it can help to provide a "safety net" that limits the survivability of engineered organisms that escape or are released into the environment. The project provides training opportunities through the development of laboratory courses that integrate bio-engineering principles with concepts of robustness and bio-safety. This project will develop design principles for enhancing the functional robustness of engineered cells using genetic homology and gene network topology. When introduced into host organisms, genetically engineered devices or synthetic genetic networks will be subjected to continuous perturbations due to changes in cellular processes and environmental factors, and the synthetic genetic components themselves operate stochastically due to the random nature of the underlying biochemical processes. Engineering robust genetic circuits under mutation pressures (whereby mutated strains quickly outgrow the original engineered strain) is one of the key challenges for synthetic biology. This project will take a step toward designing robust synthetic circuits by applying integrated mathematical, computational, and experimental approaches, where the goal is to model such fitness changes to enable the design of controlled functional robustness. The fitness landscape design concept will be tested in synthetic circuits implemented in E. coli. This study will help the analysis and prediction of the degree of robustness of synthetic genetic circuits, and help to identify and correct mutation-prone genetic components and network structures.