COMPUTER-SIMULATION OF CLONAL GROWTH CANCER MODELS .1. PARAMETER-ESTIMATION USING AN ITERATIVE ABSOLUTE BISECTION ALGORITHM

Quantitative models of the relationship between exposure to chemical c arcinogens and carcinogenic response are useful for hypothesis evaluat ion and risk assessment. The degree to which such models accurately de pict the underlying biology is often a function of the need for mathem atical tractability. When closed-form expressions are used, the need f or tractability may significantly limit their complexity. This problem can be minimized by using numerical computer simulation methods to so lve the model, thereby allowing more complex and realistic description s of the biology to be used. Unfortunately, formal methods of paramete r estimation for numerical models are not as well developed as they ar e for analytical models. In this report, we develop a formal parameter estimation routine and apply it to a numerical clonal growth simulati on (CGS) model of the growth of preneoplastic lesions consisting of in itiated cells. An iterative bisection algorithm was used to estimate p arameters from time-course data on the number of initiated cells and t he number of clones of these cells. The algorithm successfully estimat ed parameter values to give a best fit to the observed dataset and was robust vis-a-vis starting values of the parameters. Furthermore, the number of data points to which the model was fit, the number of stocha stic repetitions and other variables were examined with respect to the ir effects on the parameter estimates. This algorithm facilitates the application of CGS models for hypothesis evaluation and risk assessmen t by ensuring uniformity and reproducibility of parameter estimates.