The lab employs a coupled computational experimental approach that relies on quantitative time-lapse fluorescence microscopy with mathematical modeling to study the gene-expression circuits controlling viral latency and to understand how fluctuations, or ‘noise’, in gene expression affects viral regulation.
Dormancy is just one of the challenges to treating infectious pathogens. Treatments for infectious disease face many universal challenges, including: (i) the ability of pathogens to evolve resistance and ‘escape’ treatments, (ii) behavioral barriers such as the difficulty in maintaining adherence to treatments, and (iii) the presence of high-risk infectious ‘superspreaders’ who are hard to treat and can drive disease spread. These challenges present enormous barriers. To overcome these barriers, our lab is engaged in developing a new class of single-administration antivirals for HIV-1 with the potential to be ‘resistance-proof’ and the potential to target the highest-risk groups most in need of therapy.
We use a coupled computational-experimental approach that relies on live microscopy ‘filming’ of single infected cells. Computational and mathematical models allow us to quantify and interpret the resulting single-cell movies. These quantitative models allow us to predict which biochemical and genetic perturbations have the greatest impact upon a circuit's output. This engineering-based approach allows us to develop and test new antiviral approaches with the potential to commit the virus to a less pathogenic life-style.
- Proposed a new class of transmissible therapies to reach hard-to-treat "high-risk" groups most in need of therapy and demonstrated that these therapies could overcome the major barriers to universal HIV control (Metzger et al., PLoS Computational Biology, 2011).
- First demonstration that a gene-regulatory network harnesses stochastic noise in gene expression to control a cell-fate decision (Weinberger et al., Cell, 2005).
- Succeeded in biasing HIV-1 toward dormancy by over-expressing a cellular anti-aging gene SirT1 (Weinberger et al., Nature Genetics, 2008).
- Determined the molecular source of noise in HIV-1 (Singh et al. Biophysical Journal 2010).
Some questions addressed in ongoing studies
- What design criteria are required to engineer antivirals for HIV that can automatically target high-risk groups?
- What are the fundamental molecular mechanisms that allow a single cell to choose between different developmental outcomes and how does stochastic noise influence cell-fate and viral decisions?