Grantee Research Project Results
Final Report: Carcinogenesis Modeling for Livers and Liver Tumors of Mice With DCA or TCA
EPA Grant Number: R828082Title: Carcinogenesis Modeling for Livers and Liver Tumors of Mice With DCA or TCA
Investigators: Lei, Xingye Cherry
Institution: Pacific Northwest National Laboratory
EPA Project Officer: Hahn, Intaek
Project Period: February 1, 2000 through April 30, 2003
Project Amount: $513,113
RFA: Mechanistic-Based Cancer Risk Assessment Methods (1999) RFA Text | Recipients Lists
Research Category: Human Health
Objective:
The overall technical objectives of this project were to: (1) develop a prototype tool for multipath/multistage mechanistic modeling; (2) gain a better understanding of the mechanisms of DCA/TCA-induced or -promoted tumor development in mouse liver; and (3) provide insight into future experimental as well as mechanistic modeling of carcinogenic studies.
Summary/Accomplishments (Outputs/Outcomes):
We organized all of the data suitable for modeling from Dr. Bull’s prior work. A two-stage clonal expansion model was expanded (generalized) to handle the cases where the observation related to tumor size is a continuous variable such as diameter or volume. A three-stage, two-path model was investigated. In addition, the approach was applied to a multistage, multipath model of cell killing through exposure to radiation. Parameter estimation algorithms were implemented in MATLAB, MathCAD, and Mathematica. Algorithms for general multiple-stage and multiple-path (or multicomponent) systems were developed, and two special cases were implemented in Mathematica. Sensitivities of parameter estimations to initial values were studied. This is an active area of research for numerical computations.
Generally, multicomponent systems can be formulated as a system of linear and/or nonlinear parameterized differential equations. The model parameters have chemical/physical/biological meanings, and parameter estimates can be used to make comparisons between different systems. Parameter estimations can be performed through either least squares methods or maximum likelihood methods. Once model parameters are estimated, sensitivities of the system to parameter changes can be performed by limiting parameters in certain meaningful intervals and seeing how system responses change corresponding to parameter changes.
We will follow through with the manuscript publications and will finalize collections of MatLab scripts and Mathematica® notebooks.
Journal Articles:
No journal articles submitted with this report: View all 3 publications for this projectSupplemental Keywords:
dichloroacetate, DCA, trichloroacetate, TCA, liver tumor, initiation and promotion experiment, multiple path and multiple stage model, parameter estimation, differential equation, least squares, maximum likelihood, systems dynamics,,Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.