Sai Teja Reddy Moolamalla received his MS  Dual Degree in Computational Natural Sciences (CNS). His  research work was supervised by Dr. Vinod Palakkad Krishnannuni. Here’s a summary of Sai Teja Reddy Moolamalla’s  MS  thesis, Metabolic Network Modelling of Neuropsychiatric Disorders and COVID-19 as explained by him: 

Distinguishing neuropsychiatric disorders is challenging due to the overlap in symptoms and genetic risk factors. People suffering from these disorders face personal and professional challenges. Understanding the dysregulation of brain metabolism under disease condition can aid in effective diagnosis and in developing treatment strategies based on the metabolism. In this study, we reconstructed the metabolic network of three major neuropsychiatric disorders, schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) using transcriptomic data and constrained based modelling approach. We integrated brain transcriptomic data from six independent studies with a recent comprehensive genome-scale metabolic model, Recon3D. The analysis of the reconstructed network revealed flux-level alterations in the peroxisome-mitochondria-golgi axis in neuropsychiatric disorders. We also extracted reporter metabolites and pathways that distinguish these three neuropsychiatric disorders. We found differences with respect to fatty acid oxidation, aromatic and branched chain amino acid metabolism, bile acid synthesis, glycosaminoglycans synthesis and modifications, and phospholipid metabolism. Further, we predicted network perturbations that transform the disease metabolic state to a healthy metabolic state for each disorder. These analyses provide local and global views of the metabolic changes in SCZ, BD and MDD, which may have clinical implications. In light of the current pandemic of COVID-19, we also focussed on understanding the pathogenesis in SARS-CoV-2 infection. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on the host metabolism is yet to be fully understood. We analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swabs, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand the metabolic alterations in response to SARS-CoV-2 infection. We explored the express vision pattern of metabolic genes in Recon3D and identified key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2 infected condition and compared them to identify common and unique changes in the metabolism. Our analysis revealed host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how antiviral metabolism can be targeted/enhanced for reducing viral titers. These findings warrant further exploration with more samples and in vitro studies to test predictions. Overall, this thesis highlights that metabolic network analysis techniques help to understand the pathogenesis of complex diseases like neuropsychiatric disorders and COVID-19, and aids in developing effective treatment strategies.