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Mathematical modelling explains evolutionary phenomenon behind treatment resistance


Modern medicine and treatments for bacterial infections and cancer have significantly increased life spans and improved quality-of-life. However, drug-resistance is eroding the effectiveness of treatment for some patients.   

Dan Nichol of the Department of Computer Science at Oxford, and Dr Alexander Anderson of the Moffitt Cancer Center’s Integrated Mathematical Oncology (IMO) Program, are leading research which uses mathematical models to explain how bacteria and cancer cells exploit an evolutionary process known as bet-hedging to resist medical intervention. They have just shared their findings in a study which was published in the December issue of Genetics.

Dr Anderson explains, 'Treatment resistance occurs partly because cell populations are heterogeneous – consisting of a mixture of cells with differing characteristics, some of which are impervious to therapy.' Where this heterogeneity arises through mechanisms other than genetic mutation, it is referred to as bet-hedging.

Researchers performed mathematical modeling, coupled with extensive simulations, to predict the evolutionary origin and fate of bet-hedging. They have found that biological redundancy can lead to bet-hedging through the introduction of random genetic mutations that initially have no impact on the characteristics of a species. The researchers used their simulation results to suggest an alternative explanation for cancer development based on bet-hedging.

The results of this study should lead to improved treatment strategies to combat drug resistance.

Read more about the research here: