Using a combinatorial decomposition of a genetic regulatory network into sub-networks and harnessing the versatility of yeast to encode each of these as a separate strain, this research will dissect, deconvolve and decipher the dynamics of nitrogen catabolite repression. Based on detailed measurements using transcription factor fusion proteins, live cell imaging, and quantitative measurement of transcription rate, the investigors will develop precise models of the dynamical behavior of the complex yeast network. Quantitative models will provide a platform for for the discovery of mathematical theorems, that relate the structure of genetic circuits to their dynamics and function. This theory in turn will allow a study of the robustness of the circuit with respect to parameters and its output. The mathematical theory that will be developed will, among other things, allow researchers to streamline simulations of large biological networks.
In recent years there has been a growing awareness of the connections between biological organisms and engineering systems. There are networks of genes that control and regulate the behavior of other genes and ultimately all biological processes. These networks of genes are analogous to electrical circuits and switches. There has been an expanding willingness and desire to use mathematical and computational techniques to leverage all existing information regarding the structure of biological control circuitry to understand the basic rules on which they are predicated and to acquire the ability to predict their output. Understanding the dynamics of such networks promises revolutionary changes in the treatment of human diseases as well as providing insight for the development of a new generations of robust electronic and digital devices. This project will have a significant impact on graduate education and on recruitment of mathematics graduate students for interdisciplinary scientific work as well as the recruitment of biology, computer science and medical students into the interdisciplinary and emerging field of biomedical informatics.
