Abstract

We now live in a time that provides relatively inexpensive approaches to measure a vast number of molecules and their interactions within many organisms. Next-generation sequencing is elucidating myriads of mutations and enabling the routine quantification of all RNAs in cell line studies. In addition, proteomic efforts have found hundreds of 1000s of protein modifications in cancer cell lines. With this data deluge, our current challenge is to fully utilize and understand these “omic” measurements and gain novel mechanistic insights. Recent developments in systems biology now provide a whole cell view into how changes in the “omes” influence cell growth and homeostasis. Indeed, using genome-scale in silico models as a context for data analysis enables researchers to obtain molecular-level predictions.

In this talk, I will show how mechanistic computational models are elucidating how the transcriptome, proteome, and genome evolve specific mechanisms to better regulate growth and homeostasis. First, I will show how microbes evolve their entire gene expression profile to improve the growth rate when forced to grow in a non-native environment. Second, enzyme specificity has evolved to cope with metabolic demands and to optimize regulation. Third, in silico models can be used with large scale genome-editing technologies and protein structures to predict how post-translational modifications are used to regulate metabolism. Finally, I will discuss how these approaches can be applied to discover the mechanisms by which driver mutations and protein modifications in cancer can regulate or dysregulate cell growth.