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Dynamics of the Eukaryotic Stress Response
A large variety of stresses, in the form of heat, oxidizers, metal ions, etc constantly bombard our cells. All of these stresses have the potential to cause damage to vital intracellular proteins, causing either loss of function or formation of protein aggregates (plaques). In neurons, these aggregates lead to a number of neurodegenerative diseases including Huntington's, and Parkinson's disease. Fortunately, evolution supplied a defense mechanism for all cells against stress, the molecular chaperones, or heat shock proteins. Upon stress, our cells transiently over-express these proteins to either sequester damaged proteins, giving them time to refold back to their native conformation, or escort them to the proteasome for degradation. To understand what regulates the expression level of heat shock proteins, and look for potential targets for future treatments of plaque-associated disorders we develop mathematical, predictive models of the eukaryotic stress response system. The tools we use include sensitivity analysis, bifurcation analysis, and control theory to identify the key regulatory steps and parameters in the system.
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Modeling and Analysis of Large Biopolymerization Networks
Transcription and translation are two central cellular processes and are very important to understand for biochemical engineering and medical applications. The large number of components involved in these processes and the sequence of elementary steps that comprise these processes introduce a high degree of complexity. Moreover, if we consider these processes in the context of integrated cellular function, their complexity further increases as a large number of components, such as genes and mRNAs, simultaneously compete for the catalytic components such as RNA polymerases and ribosomes, respectively. Therefore, understanding the systemic properties of large transcription and translation networks is central to systems biology. With the recent advances in genomics, microarray technology, and proteomics, it is now possible to measure simultaneously the changes in the levels of every mRNA and protein in a cell subject to an environmental and/or genetic perturbation. Also, recent genome-wide experimental studies in yeast, E. coli, and human liver cells have shown poor correlation between the mRNA and corresponding protein expression levels, which necessitates simultaneous analysis of mRNA and protein. Mathematical modeling and computational studies are key to integrate and interpret quantitative information from such large networks to decipher their design principles. We have developed a genome wide deterministic model for the translation machinery which provides a mapping between mRNA and protein expression levels and are currently working on the stochastic formulation of the system. Using our modeling framework, we are also trying to understand the key parameters that affect translational regulation and the dynamic properties of such regulation networks. Our current focus is also on understanding codon usage and tRNA availability and their effects on both protein synthesis rate and evolutionary genetic design.
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Metabolic Control Analysis under Uncertainty
Metabolic control analysis is a robust theoretical framework for the quantification of the control of metabolic variables, e.g. the steady-state responses of fluxes and metabolite concentrations, to changes in system parameters such as enzyme activities. In biotechnological applications, and specifically in metabolic engineering, MCA is used for the identification of the most promising targets to modify in order to achieve a desired performance of the microbial production of chemicals. In medical applications, it is used for the identification of the drug targets. In principle, those enzymes and processes in metabolism and cellular processes that have high control coefficients are the primary targets for genetic manipulation. Benefited from advancements of recombinant DNA technology and analytical biochemistry techniques, metabolic parameters and intracellular variables can be measured to provide information for MCA study. However, this data is subject to variation among individual organism and precise measurement is experimentally daunting and unrealistic. Therefore, we employ statistical tools to address this uncertain nature of cell metabolism by confining the metabolite concentrations and kinetic parameters in certain bounds which are determined by physiological experiments and then perform Monte Carlo simulation to generate randomized data. This statistical framework can be readily used to understand the control properties of complex metabolic pathways and offer directions to metabolic engineering.
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Discovery of Novel Biotransformations
Living organisms utilize enzyme-catalyzed reactions to synthesize a large array of complex molecules. Enzyme catalyzed processes are characterized by mild conditions, fast reaction rates, highly stereospecific interactions, and minimal toxic byproduct formation. However, living organisms often consist of thousands of metabolites undergoing thousands of reactions. These reactions are carefully regulated through mechanisms developed over millions of years of evolution. An understanding of this complex system and regulation will enable the engineering of enzymes and pathways for the biosynthesis of industrial chemicals or novel pharmaceuticals. The objective of this project is the development of a computational framework for the discovery and the rational design of novel biosynthetic pathways for the production of useful or novel chemicals.
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