Nonlinear Optimization of a Stochastic Function in a Cell Migration Model
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open in viewerThe basis for many biological processes such as cell division and differentiation, immune responses, and tumor metastasis depends upon the cell's ability to migrate effectively. A mathematical model for simulating cell migration can be useful in identifying the underlying contributing factors to the crawling motions observed in different types of cells. We present a cell migration model that simulates the 2D motion of amoeba, fibroblasts, keratocytes, and neurons according to a set of input parameters. In the absence of external stimuli the pattern of cell migration follows a persistent random walk which necessitates for several stochastic components in the mathematical model. Consequently, the cell metrics which provide a quantitative description of the cell motion varies between simulations. First we examine different methods for computing the error observed between the output metrics generated by our model and a set of target cell metrics. We also investigate ways of minimizing the variability of the output by varying the number of iterations within a simulation. Finally we apply finite differences, Hooke and Jeeves, and Nelder-Mead minimization methods to our nonlinear stochastic function to search for optimal input values.
- Creator
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- Degree
- Unit
- Publisher
- Language
- English
- Identifier
- etd-050506-164020
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- Advisor
- Defense date
- Year
- 2006
- Date created
- 2006-05-05
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- Rights statement
- Última modificação
- 2020-12-07
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Permanent link to this page: https://digital.wpi.edu/show/dr26xx50d