Pathogens evolve rapidly to circumvent drug treatments and immune surveillance, which dramatically impacts public health. Research and treatment are complicated by high genetic diversity of some viruses within and across infected individuals, as well as their complex evolutionary mechanisms, including selection, random genetic drift, and temporal variation in a host environment. Moreover, many pathogens have a large number of linked sites approximately 102 -103 for HIV and hepatitis C virus (HCV) that evolve simultaneously and inter-dependently through two different effects, "epistasis" due to interaction between proteins and signaling network, and co-inheritance linkage ("clonal interference"). My previous research focused on developing mathematical tools that predict evolution of pathogens with strong linkage effects, including analytic and computational methods and estimators of evolutionary parameters from sequence data. I have developed analytic and computational methods and estimators of evolutionary parameters from sequence data.
The last decade has seen explosive progress in mathematical modeling of microbial populations and high-fidelity sequencing. Taking advantage of these developments, my team will address evolution of microbes (yeast, bacteria) and viruses (HIV, influenza, polio, CHIKV, Dengue, HCV). Launching from my previous mathematical and applied studies, we are applying existing methods and models to study the viral evolution under time-dependent conditions, develop new mathematical techniques and improve existing phylogenetic tools, and identify some key factors of HIV pathogenesis. Our multi-disciplinary team fuses the recent mathematical discoveries with multiple-scale modeling and software tools. We are especially interested in the evolutionary effects of epistasis, recombination, and the theory of phylogenetic relationships in the presence of selection and the other factors. The project is designed to create significant clinical impact by fostering research into novel classes of drugs to control viral adaptation rate and achieve viral containment. Our software will facilitate personalized medicine and vaccine design against the pathogens escaping treatment and immune responses. The results are published and diffused in higher education and public presentations.
Rise and fall of the Universal Footprint of Epistasis.
(a–f) Estimate of Universal Footprint of Epistasis (Pedruzzi et al 2018) in Monte-Carlo simulation plotted against the actual epistatic strength, E, set in the simulation. Cyan and blue symbols correspond to the subset of epistatic pairs known a priori. Red and magenta correspond to a random subset of pairs of the same size. The hue shows two different methods of averaging over site pairs. We observe in (a-f) the progressive rise of UFE approaching the analytic prediction (diagonal line). The timescale, 10 generations, corresponds to t ~ 1/s
(2018) A direct measure of epistatic interaction in terms of Darwinian fitness from the haplotype frequencies of a genomic site pair is developed
(2018) A model describing the antigenic evolution of a virus in a host population with immune memory is analyzed and compared to data for influenza A
(2016) The theoretical prediction of the existence of an adaptation optimum in the mutation rate is confirmed by experiments on polio virus in mice (collaboration with UCSF)
(2015) A model for the Trojan horse effect of virus latency in HIV transmission is analyzed and compared to data from patients
(2013, 2016) Virus escape from the treatment with a defective interference particle is investigated
(2012) The traveling wave theory is generalized for arbitrary distribution of mutation effect on fitness (collaboration with UCSB, Harvard, and U. Goettingen)
(2005-2010) Recombination is incorporated into the traveling wave theory
(2003) A traveling wave theory is developed to predict the adaptation of asexual populations
(2003) The nature of parasitemia oscillations in malaria is investigated using an age-structured model with inter-cell communication
(1999, 2011) Evolutionary parameters of HIV are estimated (effective population size, average selection coefficient, recombination rate)
(1999) Rapid evolution and high diversity of HIV is explained from the emergence of mutations compensating primary mutations conferring escape from the immune recognition
(1999-2001) First introduction of stochastic models of evolution in the presence of selection into virology