Cancer is a leading cause of death in the world, and the mechanisms that underlie this disease are still not completely understood. The recognition of altered metabolism as a hallmark of cancer has created a resurgence of interest in the field of systems biology and metabolic modelling to analyze and understand the metabolic changes occurring in cancer cells, both with respect to their healthy counterparts and within different types of cancer.<br/>Seeking to study this metabolic reprogramming at a mechanistic level, we generated a workflow that allow us to build cancer-specific models, to integrate different types of omics data such as metabolomics, proteomics, and transcriptomics, and to identify the metabolic pathways required to perform cellular functions. First, we reconstructed a systematically and consistently reduced version of the human genome-scale model Recon 3D. We then used a computational method to integrate RNAseq data for different cancer cells into the model. Next, we developed a novel approach to characterize the metabolic state of the cancer cells under different conditions using the resulting cancer-specific metabolic models. Finally, we identified the metabolic requirements of different cancer cells to satisfy a set of metabolic functions and we performed pathway enrichment analysis to study the differences in enzyme deregulation across cancer cells. Moreover, the cancer models serve as a basis to develop integrative models of signaling and metabolism to further study the regulation of metabolic pathways.<br/>These methods and analysis provide a novel approach to characterize the metabolic signatures of cancer cells and improve our understanding of how cancer cells rewire their metabolism and gene expression. Furthermore, it will allow us to identify possible therapeutic targets leading to the design of novel and personalized treatments.

Systems biology approaches to investigate the metabolic mechanisms underlying cancer

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.