Abstract

The central theme of this program project is the development of quantitative methods, based on biological mechanism, to address important problems in cancer and AIDS. To this end, biologically-based mathematical models will be developed, together with the requisite statistical methods and computational tools for the analysis of data using these models. The usefulness of the models will be demonstrated by analyses of substantial data sets. Three research projects are proposed. 1. Stochastic Models of Carcinogenesis with Applications to Analyses of Epidemiologic and Experimental Data. In this project stochastic models of carcinogenesis will be developed for analyses of time-to-tumor data in epidemiologic and experimental studies and for analyses of intermediate lesions on the pathway to cancer in initiation-promotion experiments. 2. Risk Prediction Models for Breast and other Cancers. In this project, risk prediction models, based on the natural history of the disease and incorporating both genetic and environmental factors, will be developed for breast and other cancers 3. Quantitative methods for Modelling HIV Infection Dynamics: In this project, deterministic and stochastic models will be developed for describing interactions between HIV and the immune system during the acute and early stages of infection.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA076466-03
Application #
2896275
Study Section
Subcommittee G - Education (NCI)
Program Officer
Erickson, Burdette (BUD) W
Project Start
1997-09-30
Project End
2001-09-29
Budget Start
1999-09-30
Budget End
2000-09-29
Support Year
3
Fiscal Year
1999
Total Cost
Indirect Cost

  Institution

Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
075524595
City
Seattle
State
WA
Country
United States
Zip Code
98109

  Publications

Wick, David; Self, Steven G (2004) On simulating strongly-interacting, stochastic population models. Math Biosci 187:1-20
Wick, David; Self, Steven G (2004) On simulating strongly interacting, stochastic population models. II. Multiple compartments. Math Biosci 190:127-43
de Gunst, Mathisca C M; Dewanji, Anup; Luebeck, E Georg (2003) Exploring heterogeneity in tumour data using Markov chain Monte Carlo. Stat Med 22:1691-707
Curtis, S B; Luebeck, E G; Hazelton, W D et al. (2002) A new perspective of carcinogenesis from protracted high-LET radiation arises from the two-stage clonal expansion model. Adv Space Res 30:937-44
Wick, David; Self, Steven G (2002) What's the matter with HIV-directed killer T cells? J Theor Biol 219:19-31
Luebeck, E Georg; Moolgavkar, Suresh H (2002) Multistage carcinogenesis and the incidence of colorectal cancer. Proc Natl Acad Sci U S A 99:15095-100
Gregori, Giovanni; Hanin, Leonid; Luebeck, Georg et al. (2002) Testing goodness of fit for stochastic models of carcinogenesis. Math Biosci 175:13-29
Hazelton, W D; Luebeck, E G; Heidenreich, W F et al. (2001) Analysis of a historical cohort of Chinese tin miners with arsenic, radon, cigarette smoke, and pipe smoke exposures using the biologically based two-stage clonal expansion model. Radiat Res 156:78-94
Curtis, S B; Luebeck, E G; Hazelton, W D et al. (2001) The role of promotion in carcinogenesis from protracted high-LET exposure. Phys Med 17 Suppl 1:157-60
Wick, D; Self, S G (2000) Early HIV infection in vivo: branching-process model for studying timing of immune responses and drug therapy. Math Biosci 165:115-34

Showing the most recent 10 out of 19 publications