Shireesh Srivastava

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BACKGROUND Free fatty acids (FFA) and tumor necrosis factor alpha (TNF-alpha) have been implicated in the pathogenesis of many obesity-related metabolic disorders. When human hepatoblastoma cells (HepG2) were exposed to different types of FFA and TNF-alpha, saturated fatty acid was found to be cytotoxic and its toxicity was exacerbated by TNF-alpha. In(More)
BACKGROUND Among the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior(More)
BACKGROUND The ability to obtain profiles of gene expressions, proteins and metabolites with the advent of high throughput technologies has advanced the study of pathway and network reconstruction. Genome-wide network reconstruction requires either interaction measurements or large amount of perturbation data, often not available for mammalian cell systems.(More)
BACKGROUND In order to devise efficient treatments for complex, multi-factorial diseases, it is important to identify the genes which regulate multiple cellular processes. Exposure to elevated levels of free fatty acids (FFAs) and tumor necrosis factor alpha (TNF-alpha) alters multiple cellular processes, causing lipotoxicity. Intracellular lipid(More)
In response to carbohydrate deprivation or prolonged fasting the ketone bodies, β-hydroxybutyrate (βHB) and acetoacetate (AcAc), are produced from the incomplete β-oxidation of fatty acids in the liver. Neither βHB nor AcAc are well utilized for synthesis of sterols or fatty acids in human or rat liver. To study the effects of ketones on cholesterol(More)
Reconstructing gene networks from micro-array data can provide information on the mechanisms that govern cellular processes. Numerous studies have been devoted to addressing this problem. A popular method is to view the gene network as a Bayesian inference network, and to apply structure learning methods to determine the topology of the gene network. There(More)
Numerous mathematical methods have been developed to reconstruct biological networks, for example gene (1,2), metabolic (3), and protein-protein (4,5) networks, from experimental data. Reconstructing pathways and networks provides a framework for predictive modeling and hypotheses testing to gain more insight into living organisms, disease mechanisms, and(More)
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