Toward the identification of new cancer therapy targets using metabolic modeling in a human genome scale

Conference Dates

July 16-20, 2017


Cancer is a leading cause of death in the world, and the mechanisms that underlie this disease are still not completely understood. In the last decades, altered tumor metabolism has been recognized as one of the hallmarks of cancer [2]. This has created a resurgence of interest in the field of systems biology and metabolic modeling to analyze and understand the metabolic changes occurring in cancer cells with respect to their healthy counterparts and within different types of cancer. Modeling the different phenotypes of healthy and cancer cells will help to make predictions to create effective therapies to prevent, diagnose and treat cancer. The reconstruction of genome-scale models (GEMs) enables the computation of phenotypic traits based on the genetic composition of the target organism. Therefore, we use the human genome-scale model Recon 2 [4] to build cell-specific human GEMs by the integration of experimental data (fluxomics, metabolomics, genomics, transcriptomics) and thermodynamics data. One of the challenges when working with GEMs is to characterize the extracellular medium. We overcome this problem using a method to obtain the minimal media required for growth. With this method, we generate alternative extracellular media for the GEM Recon 2 to meet biological requirements. A second challenge arises from the complexity working with large models. We follow a systematic model reduction framework to reduce the GEMs [1] to focus on metabolic subsystems of interest retaining the linkages and knowledge captured in the GEM. The reduced models are used as a scaffold to generate systematic kinetic models following the ORACLE workflow [3]. Our approach for building kinetic models allows to study the dynamic behavior of the system as well as the overall network response to perturbations on targeted enzyme activities. The proposed pipeline will enhance the comparison and understanding, at the stoichiometric and kinetic level, of the main metabolic differences that emerge in cancer development and progression. Furthermore, predicting the effect of perturbations in the network will help to design experiments to find new targets for therapies and drugs. [1] Ataman, M., Hernandez Gardiol, D. F., Fengos, G., and Hatzimanikatis, V. redGEM: Systematic Reduction of Genome-scale Metabolic Reconstructions for Development of Core Metabolic Models. Accepted in Plos Computational Biology. [2] Hanahan, D. and Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell, 144(5):646–674. [3] Miskovic, L. and Hatzimanikatis, V. (2010). Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotechnology, 28(8):391–397. [4] Thiele, I., Swainston, N., Fleming, R. M., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013). A community-driven global reconstruction of human metabolism. Nature biotechnology, 31(5):419–425.

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