The Optimal Deployment of Antibiotics: Whether, How and When to Switch

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Optimal control is a term used to describe the research that made much of the space-race possible. Neil Armstrong s moon landing was made possible by understanding how to manipulate a space rocket with minimal fuel expenditure. The idea behind this work is to take the same basic engineering science and to apply it by asking whether the evolution of resistance that blights antibiotic treatments can be somehow minimised by understanding how to deploy those antibiotics in an optimal way.We can already do this, in theoretical terms, and we are able to create weird and wonderful ways of deploying antibiotic to a biological system in order to maximise its health. However, a lot of biological theory based on mathematical modeling has been proposed in the past that turned out to be plain wrong when the right experiments were done. This is especially true of population biology, the branch of biology that treats interacting populations of organisms such as microbes and other cells. Now, the mathematical modeling in this work is a mix of population biology, cell biology and genetics; while space researchers have Newton s laws to lean on, population biology has no intrinsic, physical laws. As a result, it is sometimes a feature of the field that theoretical concepts stick before they are truly tested. So, we want to test our theories to destruction to make sure they work in the lab before we eventually go on to try and persuade medical practitonners that cycling different antibiotics is a good thing to do. Certainly, it is better to cycle than to mix them into a single treatment, at least that s what our theories always say. This research will go a long way to proving that what works in theory, also works in the lab, and hopefully in practise too.