Dynamic biological signaling pathway modeling and parameter estimation through optimization
The development of computational models for systematic, large scale biochemical networks is becoming an important part of modern research with scientific and practical value, due to the fact that construction of computational models can provide valuable insights into the organization and functions of the systems. However, lack of kinetic parameters often becomes a bottleneck in testing a biological hypothesis. One approach to alleviate this bottleneck is to use parameter estimation methods through optimization techniques, coupled with mathematical modeling of biological systems to estimate those kinetic parameters. In this dissertation, we apply a validated optimization method for the purpose of estimating kinetic constants for cell signaling pathway models, and integrate this method into SIGnal Modeling Interface and Database (SIGMOID) We present several problems that have been successfully solved by this strategy and evaluate the performance in terms of parameter fitting, curve fitting and computational time. We explore the effect of varying algorithm control parameters on improving optimization results. We also compare our simulated annealing method with several popular numerical optimization methods such as genetic algorithms, evolutionary programming, random search, Levenberg Marquardt and Nelder Mead method from other software packages. We also establish mathematical models for the DNA damage and response signaling transduction pathway, focusing on ATM activation and the positive/negative feedback regulation from MRN and PP2A, respectively. We use the Cellerator modeling framework to build the models in the form of enzyme kinetic reactions which are then translated into a set of nonlinear ordinary differential equations (ODEs) We simulate the models using assumed parameter values, consistent with very limited experimental data, and perform sensitivity analysis on the models in order to discuss the various factors that influence the ATM activation. We also demonstrate how we utilize the approach of integration of simulation and optimization techniques defined in this dissertation to estimate the parameters from models. The estimated system dynamics should fit either real or synthetic experimental data. We use stochastic optimization techniques such as simulated annealing to minimize the objective functions for these models. We will show the statistical results and evaluate the performance of optimization algorithms on the DNA damage signaling pathway models.