The ways biological systems sense, process, and respond to the dynamic environment in short and long time scales have inspired engineers to apply biological algorithms (e.g. neural networks, genetic algorithms) to solve complex engineering problems. More recently, advances in both genetic engineering and quantitative modeling of biological systems triggered significant research activities in the emerging area of "synthetic biology", a field whose one of the main goals is to forward engineer complex functions in living cells based on engineering principles.
The focus of this project, however, is to partially implement the mechanisms of biomolecular information processing in cell-free (in vitro) environment. Cell-free molecular computation and information processing can avoid many of the limitations of implementing similar functions in living cells. For example, it is easier to characterize cell-free systems because of their reduced complexity and thus more amenable to forward engineering. The larger volume of cell-free reactions relative to the small volumes in living cells frees the systems from stochastic factors that are difficult to model and overcome. Use of biomolecules such as DNA, RNA, and proteins in cell-free computation devices should also facilitate interfacing of such devices with living cells and organisms in future applications.
This project is supported by Emerging Models and Technologies (EMT) Program of the National Science Foundation (NSF) (#0829536).
Living bacterial cells contain thousands of genes. However, only a subset of the genes are activated (expressed) at any given time. Bacterial cells activate or repress a specific set of genes to optimally respond to or adapt to the dynamic environment. When engineering bacterial cells to produce useful chemicals or proteins (metabolic engineering), to detect and measure speficic compounds (biosensors), or to metabolize environmental toxins (bioremediation), we need versatile gene switches or circuits that can turn on or turn off genes in response to chemical or physical signals. We developed a powerful technique called dual genetic selection that allows us to efficiently design such gene switches in E. coli.
Riboswitches are RNA-based sensors found in bacteria that detect small molecule metabolites such as vitamins, nucleobases, and amino acids. When a riboswitch binds to the associated metabolite molecule, the structure of the RNA changes, resulting in a change in the level of gene expression. In other words, bacteria use RNA to recognize certain metabolites and turn on or turn off a set of genes that are involved in the synthesis or removal of the metabolites. We are using the dual genetic selection technique developed in our lab to engineer natural riboswitches. For example, we took a vitamin B1 (thiamine) riboswitch from E. coli that turns off gene expression when there is excess vitamin, and reengineered the riboswitch to turn on gene expression when the vitamin is available. The engineered riboswitches may be useful for measuring metabolite levels inside cells or to redesign metabolic pathways.
