Grant: Discovery Grants Program - Individual

Humans have used budding yeast to make bread, beer and wine since ancient times. Today, we also use yeast to mass-produce beneficial chemicals that are otherwise difficult or expensive to obtain, such as drugs to combat malaria. Industrial uses of yeast involve altering or adding information encoded in its DNA in the form of genes. While scientists can change DNA through breeding, technological advances in a new scientific field are much more efficient. This field is called Synthetic Biology because it uses biology to create, or synthesize, something that is new. Synthetic Biology is about altering organisms to do what we want them to do. This can involve providing cells the genes needed to produce beneficial chemicals, or writing DNA code instructing cells to express different genes in different situations. Either way, the process of programming cells is very complicated and we therefore use mathematics and computers to guide our work. My research program will improve our ability to write the DNA-encoded instructions that program how cells behave. Scientists are actually not very good at this, and the programs we write often dont work as expected, if they work at all. To address this problem, Synthetic Biologists have studied how natural organisms regulate gene expression. We know that molecules called transcription factors, which bind to DNA and control the expression of nearby genes, play a critical role. We also know that individual genes are regulated by many different transcription factors, and that transcription factors regulate one another through intricate networks. It is the interconnections within these networks that define which genes a cell will express in different situations. The goal of my research program is to gain knowledge on how gene regulatory networks operate inside living yeast cells. Towards this goal, I will interconnect existing DNA codes that are very predictable to form larger gene regulatory networks. Using mathematical modelling and advanced DNA manipulation techniques, I will focus on addressing two problems that make gene regulatory networks complicated: (1) when a gene is regulated by two or more transcription factors, we dont know how to predict their combined effects; and (2) when a network involves two or more interconnected transcription factors, we dont know if we can accurately predict the outcome. To further test our mathematical models, we will use the same techniques to study a natural gene regulatory network involved in drug resistance. Our models predict that cells can become resistant to drug treatment without changing their DNA if their regulatory network encodes specific timing properties. This prediction goes beyond conventional wisdom, and is an example of how a multidisciplinary research program can lead to research opportunities that will contribute new knowledge toward solving a problem with significant potential societal implications.