The overarching goal of this proposal is to continue development and application of the class of stochastic models known as branching processes for various biological/biomedical applications, all of which involve cellular population dynamics. The models will be used to explain data, estimate parameters, assess specific hypotheses, and make predictions. Specific projects are (1) prion dynamics, (2) bacterial persistence, (3) telomere dynamics, (4) cell cycle desynchronization, (5) bacterial lag phase, and (6) Muller's ratchet of accumulation of deleterious mutations. There is a great need for mathematical modeling a quantitative data analysis biology in general and in cellular population dynamics in particular. Although different mathematical approaches have been taken to many of these problems, branching processes seem to offer significant improvement in that they are inherently stochastic, taking into account the natural random variation in cell cycle times, mutations, etc. They are also conceptually clear, starting from modeling behavior on the individual level, for example by modeling the cell cycle, and drawing conclusions based on population-level data. The proposed research is intended to have a direct impact on the biological applications mentioned above.

Public Health Relevance

The biomedical problems that are here proposed to be addressed with mathematical methods are all relevant to public health. The problem of desynchronization of cell populations is relevant to cancer therapy, the problem of shortening of telomeres is relevant to processes of aging and also to cancer therapy, the problem of bacterial lag phase estimation is relevant to food safety, the problems of accumulation of mutations is relevant to genetic disease, and the problems of prion dynamics and bacterial persistence are obviously relevant to public health. The proposed mathematical methods (branching processes) are conjectured to improve modeling and estimation in the cellular population processes involved in these problems.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Academic Research Enhancement Awards (AREA) (R15)
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Special Emphasis Panel (ZRG1-BST-T (90))
Program Officer
Lyster, Peter
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Trinity University
Biostatistics & Other Math Sci
Schools of Arts and Sciences
San Antonio
United States
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Olofsson, Peter; Ma, Xin (2011) Modeling and estimating bacterial lag phase. Math Biosci 234:127-31