We develop predictive computational models for three problems in pathogen evolution. First, we use computational chemistry to analyze the evolutionary changes in molecular shape and charge that recurred during multiple zoonotic transfers of influenza A from birds to humans. The repeated evolutionary patterns from past zoonotic transfers provide the basis for computational models that predict the risk of future pandemic strains. Our computational models will be useful for screening viruses sampled during surveillance of recurring human outbreaks of influenza derived from avian hosts. Second, we develop computational models to predict the efficacy of alternative vaccination strategies when vaccinating repeatedly against a rapidly evolving pathogen. Smith et al. et al. recently suggested that repeat vaccination should avoid cross reactivity with past vaccines and, at the same time, target predicted epidemic strains of the pathogen. We extend this idea by using our previously developed method for predicting influenza evolution based on patterns of positive selection. We use existing influenza data and computational methods to test the hypothesis that improved vaccine efficacy can be achieved by vaccinating with the influenza strain that we predict will dominate a few epidemics into the future. We will also develop an expanded computational model to analyze vaccination strategies for other rapidly evolving pathogens. Third, we construct mathematical and computer models to study the conditions that maintain co-circulating pathogen strains and the conditions that favor one strain to replace another. We will evaluate whether interference competition between strains mediated by cross-reactive host immunity can explain the observed patterns of fluctuating influenza strains. Based on our influenza work, we will extend our models to other pathogens to predict when new strains may arise and outcompete current strains. Finally, we will collate and make publicly available nucleotide sequence and antigenicity data for influenza from the unpublished CDC and WHO archives. This research will help to identify emerging pathogen strains that pose significant risk of causing widespread human pandemics. This research will also expand the range of vaccination strategies for use against rapidly evolving pathogens such as influenza.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZGM1-CBCB-2 (MI))
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Anderson, James J
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University of California Irvine
Schools of Arts and Sciences
United States
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Feher, Victoria A; Randall, Arlo; Baldi, Pierre et al. (2013) A 3-dimensional trimeric ?-barrel model for Chlamydia MOMP contains conserved and novel elements of Gram-negative bacterial porins. PLoS One 8:e68934
Frank, Steven A; Rosner, Marsha Rich (2012) Nonheritable cellular variability accelerates the evolutionary processes of cancer. PLoS Biol 10:e1001296
Frank, S A (2012) Natural selection. III. Selection versus transmission and the levels of selection. J Evol Biol 25:227-43
Frank, S A (2012) Natural selection. IV. The Price equation. J Evol Biol 25:1002-19
Flanagan, M L; Parrish, C R; Cobey, S et al. (2012) Anticipating the species jump: surveillance for emerging viral threats. Zoonoses Public Health 59:155-63
Frank, S A; Smith, E (2011) A simple derivation and classification of common probability distributions based on information symmetry and measurement scale. J Evol Biol 24:469-84
Frank, S A (2011) Natural selection. II. Developmental variability and evolutionary rate. J Evol Biol 24:2310-20
Frank, S A (2011) Natural selection. I. Variable environments and uncertain returns on investment. J Evol Biol 24:2299-309
Frank, Steven A; Crespi, Bernard J (2011) Pathology from evolutionary conflict, with a theory of X chromosome versus autosome conflict over sexually antagonistic traits. Proc Natl Acad Sci U S A 108 Suppl 2:10886-93
Frank, S A (2011) Measurement scale in maximum entropy models of species abundance. J Evol Biol 24:485-96

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