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Research within phymbie


Modelling hepatitis C dynamics in vitro
(by Kenneth Blahut and Catherine Beauchemin)

We are interested in creating accurate models of the spread of infection with the hepatitis C virus (HCV) within a monolayer of human hepatoma (Huh-7) cells. Part of constructing a good model is making sure we accurately capture how the infection spreads from cell-to-cell. Taking pictures of infected cell cultures to see where the infection localizes is the only way to do this. Below are images representing a small part of an infection well (part of 24-well plate) taken with a 100x magnification microscope. Pictures were taken in full light (left), or using a light which excites the fluorescence marking the infected cells (centre), or once we've merged the two pictures (right). Click on the images below to see them at full size.



And here are our latest infection tests:







Modelling influenza dynamics within hosts and in vitro

Influenza (flu) is a growing concern for health authorities worldwide. The annual cost of flu illness and the threat of an imminent pandemic make it all the more necessary to better understand the mechanisms that drive this disease.

The objective of this research is to describe and understand how various factors can affect the spread of flu within an individual. This will be done by suggesting hypotheses in the form of mathematical and computer models and seeing how their behaviour compares to that of experimental systems. For example, fitting the models to experimental data will help determine key parameters of an influenza infection like viral production rate which would be difficult or impossible to obtain experimentally.

The research concentrates on 4 particular aspects of the disease:

  1. how anti-influenza drugs act on the various phases of virus reproduction and thus influence viral population growth;
  2. how to accurately model and predict the emergence of drug resistance;
  3. how the variety of cell types in the lungs affects how fast the disease spreads in certain parts of the lungs compared to others; and
  4. how the chemical signals (chemokines) released by the cells infected with or combating the flu infection are affecting how sick a patient will feel or how quickly she/he will recover.

Answers to these questions will help us get a better understanding of what drives this disease and how we can best combat it. Additionally, the models and approaches that will be used in this research should be applicable to other respiratory diseases such as SARS and the metapneumovirus.




Flu spread through RT (by Nada Younis)

The following are a series of animated gif to show the progression of an influenza infection in the respiratory tract of a human host. The graphs show the fraction of uninfected target cells (T, black), infected cells (I, red), dead cells (D, brown) and the concentration of virus (V, blue) as a function of time and at depths ranging from 0 to 30 cm down the repiratory tract/lung under different assumptions about cell regeneration:

  1. In the absence of any cell regeneration.

  2. When cell regeneration rate is proportional to the current number of dead cells (instantaneous regeneration).

  3. When cell regeneration rate is proportional to the number of dead cells one day ago (regeneration is delayed by 1 day).

  4. When cell regeneration rate is proportional to the current number of dead cells AND target cells (instantaneous regeneration).

  5. When cell regeneration rate is proportional to the number of dead cells AND target cells one day ago (regeneration is delayed by 1 day).



Antiviral combination therapy (by Keith Poore)

We have simulated the progression of an influenza infection through a cell culture under therapy. We then used the simulator to evaluate the amount of synergy or antagony for the dual combination of oseltamivir and amantadine over a wide range of concentration of the two antivirals. Here, the synergy calculation is based on the reduction of the viral titer measured at a given time post-infection for treatment with both or either of the two antivirals. Experimentally, this is done at only one time post-infection for a few antiviral concentrations. This video shows how the synergy/antagony map obtained from viral titer measurments varies as a function of the time post-infection when the measurements are taken.




Last modified: December 04, 2012, 22:43.
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