Mathematical models of infectious disease evolution
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Project details

Project 1.  Molecular evolution of viruses

Many viruses are known to mutate at a very fast rate of around 0.5 to 1 mutations per genome per replication. This fascinating observation has important implications for the spread and control of viral diseases. Why are these rates so high?  It is sometimes argued that high mutation rates are selected due to the challenge presented by the fast changing environment of the virus, in particular the immune response of hosts.  Others have questioned this argument and instead suggest that there is a biochemical trade-off between replication efficiency and accuracy. Since there is strong selection for viruses to replicate fast, their accuracy must be low and their mutation rates must therefore be high. This project aims to understand the process of viral mutation from an evolutionary perspective using mathematical and computational models.

Reference: Regoes RR, Hamblin S, Tanaka MM (2013) Viral mutation rates: modelling the roles of within-host viral dynamics and the trade-off between replication fidelity and speed. Proc Biol Sci. 2013 Jan 7;280(1750):20122047.

 

Project 2.   Modelling the dynamics of drug resistant strains of bacteria

The evolution of antibiotic resistance poses a challenge to efforts to control bacterial infections in human and other populations.  The emergence of multiple drug resistant strains of bacteria makes the situation worse. Some strains of Mycobacterium tuberculosis, the cause of tuberculosis, are extensively drug resistant (XDR-TB): they are resistant to two of the front-line drugs and also to one or more of the second-line drugs.  The epidemiological consequences of XDR-TB and other multi-drug-resistant bacteria are largely unknown.  Although drug resistant bacteria outcompete sensitive bacteria in the presence of the antibiotics in question, they are also believed to bear a fitness cost.  This fitness cost may be reduced through compensatory mutations.  In this project you will mathematically model the population level outcomes of the evolution of multiple drug resistance.  The project may also involve developing computer simulation models.

References:  Luciani F, Sisson SA, Jiang H, Francis AR, Tanaka MM (2009) The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis. Proc Natl Acad Sci U S A. 106(34):14711-5.

Tanaka MM, Valckenborgh F (2011) Escaping an evolutionary lobster trap: drug resistance and compensatory mutation in a fluctuating environment. Evolution.  May;65(5):1376-87.

Relevant Links:

http://www.emi.unsw.edu.au/~tanakalab/

https://research.unsw.edu.au/people/associate-professor-mark-tanaka