Logarithmic Growth in Biological Processes

Author

Paltanea, M. ; Tabirca, S. ; Scheiber, E. ; Tangney, M.

Author_Institution

Dept. of Comput. Sci., Univ. Coll. Cork, Cork, Ireland

fYear

2010

fDate

24-26 March 2010

Firstpage

116

Lastpage

121

Abstract

The aim of our paper is to approximate the parameters of a generic logarithmic function that can be used to model various biological processes. We are considering the Gauss-Newton algorithm for solving the non-linear least squares problem and we are proposing a method for selecting the initial choice for the algorithm. This method proves to considerably increase the convergence probability of the Gauss-Newton algorithm applied for our function compared to its convergence probability when choosing a purely heuristic initial approximation.

Keywords

biology computing; heuristic programming; least squares approximations; physiological models; Gauss-Newton algorithm; biological processes; convergence probability; heuristic initial approximation; logarithmic function; nonlinear least squares problem; Biological processes; Biological system modeling; Cancer; Computer science; Educational institutions; Equations; Least squares approximation; Least squares methods; Mathematical model; Neoplasms; approximation; least squares; logarithmic growth;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Modelling and Simulation (UKSim), 2010 12th International Conference on

Conference_Location

Cambridge

Print_ISBN

978-1-4244-6614-6

Type

conf

DOI

10.1109/UKSIM.2010.29

Filename

5481085