Linking metabolic activity with drug sensitivity using metabolic influence networks

University Of Michigan At Ann Arbor, Ann Arbor MI

Investigators

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Abstract

My lab develops systems biology models of metabolic regulation and applies these for drug discovery. The impact of cellular metabolism on drug response has been observed in a wide range of diseases. However, how metabolism influences the activity of diverse classes of drugs has not been systematically explored. This has been a challenge due to the impact of metabolism on numerous cellular processes. To address this, over the past 5 years, my lab has developed several new systems biology models that simulate the impact of metabolic heterogeneity and regulation on drug response using multiomic data. During the next phase of this R35, we will focus on expanding our models to incorporate metabolic regulation by signaling networks, non-coding RNAs, DNA methylation and splicing. In addition, we will also develop methods to incorporate multiscale behavior and spatial-temporal heterogeneity from spatial imaging and single-cell omics datasets. This will be achieved using an innovative combination of new technologies including genome-scale modeling, machine learning, single-cell and spatial omics. We will use these models to predict the impact of metabolic activity on drugs that inhibit diverse cellular processes such as replication, gene regulation, or signaling. This framework will be applied to model organisms - E. coli and yeast, and model human cell lines, allowing us to uncover conserved principles linking metabolism with potency of drugs. This approach will be tested experimentally by altering cellular metabolic state and drug sensitivity with high-throughput drug and nutrient screens, enzyme inhibitors and drug combinations. The integrative multi-network models developed here will allow us to quantify the myriad effects of cell metabolism on drug action, from uptake, collateral interactions, to efflux. Through analysis of single cell and spatial omics datasets using our models, we will simulate the phenotypic impact of fluctuations in the levels of metabolites or enzymes on drug tolerance and sensitivity. Our approach can shed light on the differences in drug efficacy between in vitro and in vivo by dissecting the impact of in vivo environment. Ultimately, this research program can help precision medicine efforts by matching therapy based on cellular metabolic activity. The models will be made publicly available and can aid in the systems-level interpretation of omics datasets and can aid synthetic biology efforts by enabling rational engineering of cellular networks.