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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/29190
Title: Systems Analysis Of Core Architectures Regulating Cellular Responses Under Stress In Eukaryotes
Authors: Chakrabarti, Anirikh
Keywords: Hypoxia
upr
emt
Multiscale Modeling
Sensitivity Analysis
Robustness
Cellular Stress
Issue Date: 31-Jan-2012
Abstract: Eukaryotes have developed evolutionarily conserved mechanisms to respond to diverse ranges of internal and external perturbations e.g., changes in oxygen/nutrient levels, temperature oscillations, protein folding load, viral/bacterial attacks and graft implants. Alterations and malfunctions within these core regulatory architectures provide a peek into shifts towards cancerous and disease states. These core architectures include signal integrators and actuators, positive and negative feedback mechanisms which mediate the effectiveness and ultimate outcome of the response. Contemporary modeling approaches in the era of genomics revolution and high-throughput technology presents a unique systems level insight into many areas of biology, ecology, developmental biology and immunology. In my research, I have used a combination of bottom-up analysis (signaling networks based analysis) and a top-down analysis (using microarray/high throughput experimental data) to investigate stress responses in eukaryotes. Using the bottom-up analysis scheme, we assembled a series of molecular modules describing different aspects of the cellular response to stress. Some of these modules included the Unfolded Protein Response (UPR), Hypoxic Response (HR) and Tumor Angiogenesis, Epithelial to Mesenchymal Transition (EMT), Translation Initiation and Renal Allograft Failure. These modules were modeled using mass action kinetics with an Ordinary Differential Equation (ODE) based framework to investigate the internal regula- tory cores. For example, in UPR we identified the differential negative feedback of activating transcription factor 4 (ATF4) as the key in the adaptation phase via regulation of binding immunoglobulin protein (BiP). Similarly in HR, we identified the role of activator protein 1 (AP1) in mediating the autocrine response via vascular endothelial growth factor (VEGF) and interleukin 8 (IL8) signaling modules. Model generation was done using UNIVERSAL, an in house software freely available at google code. We addressed issues pertaining to uncertainties within these models by developing POETs, a multi-objective optimizing algorithm which allowed us to train our models with experimental data from the literature. POETs presented us with an advantage by generating an ensemble of models consistent with experimental data. The diversity within these ensembles were used to study different operational paradigms within these modules. For example, in EMT we identified the differential modes of crosstalk between mitogen-activated protein kinase (MAPK) and SMADs in mediating the cellular transformation. These different modes of operation suggest insights into different diseases and irregularities in cellular adaptation. We subsequently analyzed these models using parameter independent structural analysis tools like extreme pathways and parameter dependent tools such as fragility and robustness to identify targets relevant to therapeutic interventions. These configurations represent experimentally testable hypothesis and potentially new strategies to manipulate the cellular responses. At an intermediate (length-scale) level, we developed multiscale modeling strategies by inductively extrapolating the consequences of cell signaling to tissue/organ function. We employed this (signaling assisted multiscale modeling (SAMM)) strategy to investigate tumor growth and angiogenesis. Using a top down strategy, we used microarray datasets to investigate cellular signaling, identify malfunctions and create predictive models to infer patient outcome in case of hypoxia induced tumor growth and angiogenesis.
Committee Chair: Varner, Jeffrey D.
Committee Member: Shuler, Michael Louis
Delisa, Matthew
Discipline: Chemical Engineering
Degree Name: Ph.D. of Chemical Engineering
Degree Level: Doctor of Philosophy
Degree Grantor: Cornell University
No Access Until: 2017-06-01
URI: http://hdl.handle.net/1813/29190
Appears in Collections:Theses and Dissertations (CLOSED)

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