The overarching goal of this proposal is to apply and develop a class of stochastic models known as general branching processes for various biological/biomedical applications that involve cellular population dynamics. These models will be used to explain data, estimate parameters, assess specific hypotheses, and make predictions. Specific projects are: (1) asynchronicity in cell populations, relevant to cancer therapy;(2) telomere dynamics, relevant to processes of aging and also to cancer therapy;(3) bacterial lag phase estimation, relevant to food microbiology;and three projects relevant to degenerative genetic disease: (4) accumulation of deleterious mutations in mitochondrial DNA, (5) cell populations with conflicting levels of selection where one type of DNA may be favored inside the cell but not for selection among cells, and (6) the """"""""ratchet"""""""" process of accumulation of harmful mutations. There is a great need for mathematical modeling and quantitative data analysis in 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 be able to offer great improvement in that they are inherently stochastic, taking into account the natural randomness in cell cycle times, mutations, etc. and that they are conceptually clear, starting from modeling behavior on the individual level, for example by modeling the cell cycle and estimating relevant parameters. The proposed research is intended to develop models and estimation techniques that are biologically sound and improve accuracy in computation and estimation. 1

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, and the problems of accumulation of mutations is relevant to genetic disease. The proposed mathematical methods (general branching processes) are conjectured to improve modeling and estimation in the cellular population processes involved in these problems. 1

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-Q (52))
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; Livingstone, Kevin; Humphreys, Joshua et al. (2016) The probability of speciation on an interaction network with unequal substitution rates. Math Biosci 278:1-4
Campbell, Ian M; Stewart, Jonathan R; James, Regis A et al. (2014) Parent of origin, mosaicism, and recurrence risk: probabilistic modeling explains the broken symmetry of transmission genetics. Am J Hum Genet 95:345-59
Campbell, Ian M; Yuan, Bo; Robberecht, Caroline et al. (2014) Parental somatic mosaicism is underrecognized and influences recurrence risk of genomic disorders. Am J Hum Genet 95:173-82
Sindi, Suzanne S; Olofsson, Peter (2013) A Discrete-Time Branching Process Model of Yeast Prion Curing Curves. Math Popul Stud 20:1-13
Livingstone, Kevin; Olofsson, Peter; Cochran, Garner et al. (2012) A stochastic model for the development of Bateson-Dobzhansky-Muller incompatibilities that incorporates protein interaction networks. Math Biosci 238:49-53
Olofsson, Peter; Ma, Xin (2011) Modeling and estimating bacterial lag phase. Math Biosci 234:127-31