The Sriram Lab's research is focused on two related areas: metabolic engineering and systems biology. Metabolic engineering is the rational modification of organisms to improve their cellular properties or performance. Systems biology is the holistic, quantitative analysis of large-scale biological datasets toward improved understanding, prediction and control of how a cell, tissue or organism behaves. Both these are interdisciplinary fields with immense potential for chemical engineers to uniquely apply their expertise as well as very rapidly growing research areas.
Quantifying carbon traffic by isotope-assisted metabolic flux analysis.
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We analyze and engineer metabolic and gene regulatory pathways of plant, mammalian and other eukaryotic cells. Metabolic pathways are "traffic maps" of carbon and other elements within cells and gene regulatory pathways are networks showing how this traffic is controlled by the cell. Such analysis provides insights into how metabolic bottlenecks can be relieved and how cellular performance can be boosted by engineering select genes.
Toward these objectives, we combine experimental techniques such as isotope labeling, two-dimensional (2-D) NMR, gas chromatography-mass spectrometry (GC-MS), DNA microarray analysis and quantitative RT-PCR (qPCR) with several computational techniques for metabolic flux/pathway analysis and deduction of gene regulatory networks.
Studying biological networks in plants quantitatively has much promise for a sustainable future. This is because plants are the primary producers of several commodities crucial to an economy such as food, biofuels, fiber, several high-value therapeutics and recently, chemical industry feedstocks. Highly sophisticated plant metabolic networks synthesize these commodities from thin air (CO2), light and minerals. Quantitative studies of plant networks opens the prospect of smartly engineering these networks. Mammalian tissue cultures provide a means to understand human genetic diseases in greater detail, especially how biological networks are perturbed due to the lack of a gene or genes.