A Predictive Modeling Framework to Dissect the Dynamic Immunometabolic Responses to Pathogenic infection and the Kinetic Reprogramming of Metabolism in Cancer Cell System

Cellular metabolism is emerging as a critical factor to control the immune responses and their impact on the pathogens. In addition, recent studies pinpoint a more prominent role of the aberrant metabolism in controlling both genetic and epigenetic cellular phenomena of any form of cancer. Thus, investigating the dynamic metabolic shift in immune cells upon pathogenic infection and temporal 'reactomics' (defined as a combination of reaction mechanisms, regulations, and kinetic parameters) and associated vulnerabilities of tumor cells holds immense potential to develop novel therapeutic approaches. While the existing multi-scale modeling of immune cells tries to bridge the gap between multiple scales (i.e., molecular to organ-level), none of the existing approaches can simultaneously do that by building a proper, predictive 'full-scale' model. Furthermore, whether or to what extent metabolic shifts occur in the host's immune system is still not known. In case of cancer cell, some of the critical challenges include defining the systems-level cellular metabolic phenotype and tracking the temporal changes in reactomics which are critical for reverting the cell metabolism to more healthy state. Herein, PI Saha proposes to develop and iteratively improve a systems-level, comprehensive, and integrative metabolic modeling framework: i) to dissect the dynamic shifts in the immunometabolic responses associated with pathogenic Infection, and ii) investigate the changes in temporal reactomics associated with the metabolic reprogramming in a specific cancer cell. The proposed research program will leverage the unique combination of computational modeling skills and rich research experience in Saha's laboratory that are crucial for characterizing the metabolic phenomena associated with any disease. His research team recently developed the first computationally tractable and accurate modeling framework to track the temporal dynamics of cellular metabolism and also established a new method to estimate the reactomics of each of the metabolic reactions involved in a cellular system when 'omics' datasets are incomplete or missing and, thereby, develop a predictive kinetic modeling framework. Thus, the proposed modeling framework can potentially investigate the metabolic dynamics associated with a cluster of cells (e.g., immune cells) interacting with a pathogen or the temporal reactomics of a specific cell (e.g., cancer cell). As a first step, Saha will investigate the dynamic metabolic shifts in a specific type of immune cell (i.e., macrophage) upon SARS-Cov-2 and Staphylococcus aureus infection and the temporal reprogramming and reactomics of pancreatic ductal adenocarcinoma (PDAC) cell metabolism and test the hypothesis that if the degree to these changes gives rise to the severity of the disease symptoms. Overall, the proposed framework as well as the associated 'predictome' database (containing the predictions of key genes/proteins/reactions playing critical roles) will provide the broader scientific community including molecular biologists, computational biologists, clinicians, and translational scientists with a basic understanding of the role of metabolism in dictating disease severity and also a useful template to investigate other diseases.