Dominik Wodarz
Oncolytic virus
therapy: Replicating viruses that
specifically infect tumor cells (but not healthy cells) can be used to treat
cancers. They spread throughout a tumor and the goal is to ultimately eliminate
it. Mathematical models are developed to describe the dynamics between a growing
tumor and a replicating virus population, and to define optimal treatment
strategies.
Targeted cancer therapy:
Drugs have been developed that
target specific molecular defects responsible for driving cancer progression.
The most prominent example is the treatment of chronic myeloid leukemia (CML)
with Imatinib (Gleevec). While significant treatment responses have been
observed in early stage patients, eradication of the cancer seems difficult to
achieve, and resistance poses an important obstacle. Mathematical models are
used to study the evolutionary dynamics of resistance to CML therapy.
HIV disease
progression: HIV-infected patients
typically develop AIDS within 10-15 years in the absence of drug treatment. Some
so called long term non-progressors have remained healthy for 15-20 after
infection. Monkeys that are naturally infected with SIV never develop disease,
despite the fact that they can have high virus loads and a high degree of virus
diversity, typical of disease progression in humans. Mathematical models are
used to study the evolutionary dynamics of HIV in vivo in relation to its
ability to drive disease progression, and to explore scenarios under which
disease progression does not occur.
CTL responses to viruses in mice: Cytotoxic T lymphocytes or CTL are an important branch of the immune system in the fight against viral infections. They kill infected cells. Virus infections of mice provide an important experimental tool to investigate and manipulate the dynamics of CTL responses in vivo. The best studied example is lymphocytic choriomeningitis virus, LCMV. Specific mathematical models can be constructed to describe these dynamics, and mathematical predictions can be tested experimentally, e.g. by using different virus strains with different characteristics, or by using knockout mice that lack certain components of the immune system.
| Infectious Disease / Immune system | Cancer |
| HIV/SIV: acute phase dynamics, progression vs. non-progression | Oncolytic virus therapy |
| Treatment of immunosuppressive infections (e.g. HIV) | Resistance against targteted cancer therapy (e.g. Imatinib therapy of CML) |
| Dynamics of CTL responses to viral infectons, especially in mice (e.g. LCMV) | Somatic evolution of cells: DNA damage, genetic instability, and cancer progression |
| Immunological memory | Tumor-microenvironment interactions: promotion & inhibition |
| Evolution of immunity |
People
Albert Do
(graduate student): mathematical models of virus
infections
Former
members:
Ryan
Zurakowski, now Assistant Professor at the
Nika Bagheri (undergraduate researcher): mathematical models of oncolytic virus therapy
Laura DiChiacchio (undergraduate
researcher): mathematical models of CTL responses
Note for students: Projects are available for potential graduate and
undergraduate students. Due to the nature of my research area, some skills in
computer programming and quantitative methods will be required. My research is
computational in nature, and I do not have a wet-lab and do not perform
experiments.
Books & Edited
Volumes
Book: Killer Cell
Dynamics
An introduction
to the modeling of CTL responses to viral infections
Book: Computational Biology of
Cancer
Issue
of Seminars in Cancer Biology on “Somatic Evolution of Cancer
Cells”:
Provides a collection
of articles which examine the topic of somatic evolution and
cancer
both from a
mathematical and an experimental point of view.
Teaching:
Virus dynamics
simulation: t1.exe
Virus dynamics
simulation: t2.exe