Gene regulatory networks are an important cellular signal processing mechanism for translating input signals into appropriate phenotypes by modulating expression of the genome. The quantitative details of how cells process information through gene regulatory networks are still poorly understood, but of central importance in a large number of biological processes. Considerable progress has been made in mapping the topology of gene regulatory networks and more recently in deciphering the relationship between promoter sequence and function. Nonetheless, it is not yet possible to computationally predict the output of most native promoters, nor is it trivial to build promoters that integrate signals in a novel and predictive manner. Developing a quantitative understanding of transcriptional regulation, ultimately leading to the ability to predict entire gene regulatory networks will be a significant achievement and a prerequisite for our ability to engineer biological systems.
Gene regulatory networks are at the core of all biological processes, including disease states such as cancer. Furthermore, biotechnology heavily relies on engineered gene regulatory networks for pharmaceutical and other product syntheses. In the near future it can be expected that gene regulatory networks will play an increasingly important and central role in the development of smart and ultra-low-cost diagnostic assays. Despite the obvious importance of gene regulatory networks, they remain relatively poorly understood, limiting our ability to engineer novel complex gene regulatory networks or decipher the regulation or mis-regulation of native gene regulatory networks. Current gene regulatory network engineering still heavily relies on inefficient trial and error approaches. Other engineering disciplines such as electrical and mechanical are much more advanced, allowing the creation of complex systems that can be expected to work as designed (a building, bridge, computer, cell phone, etc.). Our project will provide useful insights into the basic biology and physics of gene regulatory networks, which will hopefully make the process of engineering biological systems such as gene regulatory networks much more robust and efficient.
We are employing a multi-disciplinary approach incorporating biology, engineering, and computational modelling to improve our quantitative understanding of gene regulatory networks by reverse engineering a native GRNs from S. cerevisiae. My research group has developed a powerful set of unique, high-throughput microfluidic technologies that enable the quantitative analysis of gene regulatory networks in vitro and in vivo. Specifically we are quantitatively investigating the yeast phosphate regulatory network under various inorganic phosphate concentrations, developing novel approaches for modulating gene regulatory networks using engineered Zn-finger transcription factors (TF), linking gene regulatory network output to fitness in order to develop an understanding of how networks are optimized and evolve, and reverse engineering an exact functional copy of the native phosphate regulatory network with orthogonal components.
