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.
