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Fusion Of Modeling And Data Analysis To Study The Evolution Of Pathogens – PathoEvo

Submission summary

Pathogens evolve rapidly to circumvent drug treatments and immune surveillance, which dramatically impacts public health. To develop effective treatments, we must interpret pathogen’s genetic sequence; however, our efforts are complicated by their high genetic diversity 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 10^2–10^3 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, Big Data bioinformatics, 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, I will apply 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. My multi-disciplinary team will fuse the recent mathematical discoveries with the Big Data bioinformatics, multiple-scale modeling, and software tools. The project will 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 immune responses. The results will published, posted as online tools, and disseminated in higher education.

Project coordination

Igor Rouzine (Laboratoire de Biologie Computationnelle et Quantitative UMR7238 UPMC-CNRS)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


LCQB Laboratoire de Biologie Computationnelle et Quantitative UMR7238 UPMC-CNRS

Help of the ANR 596,752 euros
Beginning and duration of the scientific project: May 2016 - 48 Months

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