Title page for ETD etd-07062011-140306

Type of Document Master's Thesis
Author Koneru, Murali Krushna
Author's Email Address mkoner1@tigers.lsu.edu, murali.koneru@gmail.com
URN etd-07062011-140306
Title Stochastic Modelling of Eukaryotic Cell Cycle
Degree Master of Science in Chemical Engineering (M.S.Ch.E.)
Department Chemical Engineering
Advisory Committee
Advisor Name Title
Hjortso, Martin Committee Chair
Benton, Michael Committee Member
Henry, James Committee Member
  • Insilico Evolution
  • Chemotherapy
  • Stochastic Model
  • Cell Cycle
Date of Defense 2011-06-15
Availability unrestricted
Stochastic models are developed to capture the inherent stochasticity of the biochemical networks associated to many biological processes. The objective of the present thesis is to present a detailed picture of stochastic approach for the mathematical modeling of eukaryotic cell cycle, to demonstrate an important application of such model in chemotherapy and to present a methodology for selecting the model parameters. The stochastic cell cycle model, developed using stochastic chemical kinetics approach, leads to the formation of an infinite dimensional differential equation in probabilities of system being in a specific state. Using Monte Carlo simulations of this model, dynamics of populations of eukaryotic cells such as yeasts or mammalian cells are obtained. Simulations are stochastic in nature and therefore exhibit variability among cells that is similar to the variability observed in natural populations. The modelís capability to predict heterogeneities in cell populations is used as a basis to implement it in a chemotherapic modeling framework to demonstrate how the model can be used to assist in the drug development stage by investigating drug administration strategies that can have different killing effect on cancer cells and healthy cells.

Finally, basic cell cycle model is refined in a systematic way to make it more suitable for describing the population characteristics of budding yeast. Selection of model parameters using an evolutionary optimization strategy referred to as insilico evolution is described. The benefits of this approach lie in the fact that it generates an initial guess of reasonable set of parameters which in turn can be used in the least squares fitting of model to the steady state distributions obtained from flow cytometry measurements. The Insilco evolution algorithm serves as a tool for sensitivity analysis of the model parameters and leads to a synergistic approach of model and experiments guiding each other.

To conclude, the stochastic model based on single cell kinetics will be useful for predicting the population distribution on whole organism level. Such models find applications in wide areas of biological and biomedical applications. Evolutionary optimization strategies can be used in parameter estimation methods based on steady state distributions.

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