Nagrath:Research

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Research Interests


- Network Systems Biology

- Nutritional Systems Biology

- Cellular Reprogramming into Embryonic Stem Cells

- Disease Systems Biology

- Cancer Metabolism

- Cellular and Metabolic Tissue Engineering


The Systems Biology of Human Diseases Laboratory is focused on both experimental and theoretical aspects in the area of Cellular and Molecular Tissue Engineering, Metabolic Engineering, and Biomedicince with emphasis on clinical applications. Our research interests lie in the application of nutritional systems-biology approaches in highly challenging, cutting edge problems in clinical disciplines such as metabolic syndrome, liver fibrosis, and regenerative medicine. Nutritional Systems Biology (NSB) is defined as the approach to understand the key processes that regulate metabolism at all levels of complexity and to predict the outcome of any alteration of the system by utilizing metabolic tools. We use transcriptional and metabolic design principles to analyze healthy and diseased biological states. His research focuses on various diseases such as metabolic syndrome, cancer, and diabetes, and potential treatments using metabolic supplementation and embryonic stem cells. Dr. Nagrath makes a concerted effort to use engineering principles, such as multiobjective optimality and nonequilibrium thermodynamics, for analyzing complex disease states. His research integrates both experimental and theoretical tools to develop a recipe for maintaining normal function of various organs. The goal of his work is to offer an important window to understand the role of environmental stress/factors interactions with the cellular components, and in modulating those interactions optimally to improve human health. In an effort towards understanding the energetic basis of embryonic stem cells (ESC) transcriptional network, Professor Nagrath is focusing on developing framework that can predict the abundance of topological motifs in the transcriptional regulatory network using combined thermodynamics and Pareto optimality analysis.



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