Transcriptional regulation of metabolism and behavior: insights from reconstruction and modeling of complex biochemical networks

Systems Biology;Metabolism;Gene Regulation;Social Behavior;Neuroscience;Machine learning;Genomics;Data Mining

Chandrasekaran, Sriram

关键词: Systems Biology;    Metabolism;    Gene Regulation;    Social Behavior;    Neuroscience;    Machine learning;    Genomics;    Data Mining;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/45486/Sriram_Chandrasekaran.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The genotype and the environment significantly influence the behavior and phenotype of an organism. Yet the mechanism by which a simple genetic change or environmental perturbation alters the state of an organism at the molecular level, and subsequently its phenotype, is still not completely clear. Large-scale omics experiments are now generating a wealth of data on these various processes. As a result, there is a critical need for methods that rapidly transform these high-throughput data into predictive models for medicine and bioengineering. These predictive models need to seamlessly integrate molecular components of the cell into networks and subsequently predict macroscopic phenotypic changes that emerge from these interacting networks. I have developed new tools and algorithms that address this key challenge of multi-scale network integration in order to assemble a holistic view of the cell. My dissertation involves three main themes:1.The development of new tools (PROM, GEMINI and ASTRIX) for reconstruction and modeling of biochemical networks2.Understanding transcriptional regulation of metabolism in various model organisms3.Applying systems approaches to social behavior to dissect the role of transcriptional regulation.