Project SAGE
Speed of Adaptation in Population Genetics and Evolutionary Computation
University of Nottingham
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Applications of SAGE

Our research and knowledge has many varied and far reaching applications, such as:

  • Self-optimising mobile networks: One of the central and critical component of modern European infrastructure is mobile networks, and such an application would therefore put a strong demand on the dependability of the evolutionary process which is of a crucial importance to future self-optimising mobile networks. The key to guarantee dependability of such a system is a strong theoretical foundation.
  • Evolutionary medicine: Conditions such as asthma, autoimmune disease and cancer can result from mismatches between ancient adaptations and modern environments. Likewise, the pathogenicity of virus and bacteria infecting human populations require an evolutionary explanation. Elucidating our evolutionary history can provide a path towards better therapeutic strategies, and a better understanding of the dynamics of evolution.
  • Developing drugs: Evolutionary algorithms used in the AutoDock software were instrumental in discovering the first clinically-approved antiviral drug for HIV (Isentress) by Merck Pharmaceutical Company. It is therefore not inconceivable that downstream impact from SAGE will contribute to developing novel drugs and better therapeutic strategies for cancer, which would have immediate economical and societal benefits.
  • Prevention of drug resistance:  A thorough understanding of the dynamics of evolution and a focus on future prediction will be a major factor for the effective design of therapeutic strategies for the use of antibiotics that slow down the evolution of resistance, which has become an increasingly important problem in the UK and in other countries. For example, the cases of Carpanem (an important class of antibiotics) resistance increased from less than five cases a year in 2007 to more than 600 in 2011 in the UK. These strains are evolving under selection pressure originating from the widespread use of antibiotics. This represents a relatively simple case of biological adaptation. Recently, there has been considerable effort from the scientific community to identify the mutations that cause this resistance as it represents one of the most pressing issues in evolutionary biology. It was found that these mutations are typically found in the same genes and that the order in which these mutations fix conditions the probability of acquiring resistance.
  • Management of biodiversity in natural habitats: The impeding threat of climate change stresses the need for better strategies for the preservation of biodiversity. Current models rarely take into account evolutionary processes, but this shortcoming has been recently recognised. In order to achieve effective predictions of the dynamics of genetic diversity, models of adaptive dynamics that are realistic enough will be required. These models will have to be able to deal with multiple loci and to be flexible enough to include effects of a changing environment.
  • Policy making: The EU has one of the most stringent regulations on genetically modified organisms (GMOs) in the world. All new GMOs are approved on a case by case, based on risk assessment to human health or environmental damage, all of these based on scientific evidence. One of the biggest concerns is that GMO genes start to introgress with native species by gene flow. For this, special buffer zones are set in place where hybrids can co-exist. However, legislation on the size of these buffer zones varies wildly from country to country. SAGE considers evolutionary dynamics in spatially distributed populations, which can be applied to this setting and provide a means of calculating effective buffer zones to prevent this gene-flow.


SAGE Project