RetroNets · Reverse Engineering Gene Regulatory Networks

Gene regulatory networks (GRNs) are an important cellular signal processing mechanism for translating input signals intoappropriate phenotypes by modulating expression of the genome. The quantitative details of how cells process informationthrough GRNs are still poorly understood, but of central importance in a large number of biological processes. Considerableprogress has been made in mapping the topology of GRNs and more recently in deciphering the relationship betweenpromoter sequence and function. Nonetheless, it is not yet possible to computationally predict the output of most nativepromoters, nor is it trivial to build promoters that integrate signals in a novel and predictive manner. Developing aquantitative understanding of transcriptional regulation, ultimately leading to the ability to predict entire GRNs will be asignificant achievement and a prerequisite for our ability to engineer biological systems.I propose a multi-disciplinary approach incorporating biology, engineering, and computational modelling to improve ourquantitative understanding by reverse engineering GRNs in S. cerevisiae. My research group has developed a powerful setof unique, high-throughput microfluidic technologies that enable the quantitative analysis of GRNs in vitro and in vivo.Specifically I propose to quantitatively investigate the yeast phosphate regulatory network and to develop a master modelcapable of predicting output of the network under various inorganic phosphate concentrations, to develop novel approachesfor modulating GRNs using engineered Zn-finger transcription factors (TF) and CRISPR/Cas, to link GRN output to fitnessin order to develop an understanding of how networks are optimized and evolve, and to reverse engineer an exactfunctional copy of the native phosphate regulatory network with orthogonal components.