Computational models of innate immune response to respiratory pathogens

Lung infections, such as pneumonia and influenza, are responsible the deaths of many people every year. We are frequently exposed to the infections that cause these diseases, however, most of the time we remain healthy. This is because our body?s immune system is able to control these infections and thus we do not become ill. The reason why this happens is poorly understood. If we understood more about how this part of the immune system worked then it may be possible to develop new therapies against lung diseases.

I have been carrying out research into the immune response two important causes of lung infections one of the viruses responsible for influenza, the influenza A virus and the bacterium responsible for the most common cause of pneumonia, Streptococcus pneumonia. However, as the immune system is very complicated it can be difficult to understand fully what is happening in experimental systems. It is possible to develop computer models that have behaviours that closely resemble biological systems. We are able to change conditions in these models that would not be possible using traditional experimental approaches. Such computer models can be simple which can be useful to look at the behaviour of simple biological systems, but for complex biological systems there are models, called agent based models, which are able to model the unpredictable behaviour of more complex systems. I plan to learn how to use and develop both of these types of computer model and I will then use these models to help answer important questions about the immune system.

Respiratory infections remain a leading cause of death worldwide. The incidence of pneumonia is actually surprisingly low given the frequency of exposure to relevant pathogens. This is due to the efficiency of the immune system in preventing pneumonia in most cases. The innate immune response is the first component of the immune response and is of particular importance in the early stages of the host response. Tissue macrophages have a critical role in the innate immune response against infections as they can phagocytose and kill micro-organisms and regulate inflammation. Macrophages can release cytokines, produce antimicrobial factors and regulate the survival of a number of cells including monocytes, neutrophils and lymphocytes. Cytokines produced result in the recruitment and activation of inflammatory cells which contribute to killing of micro-organisms but may lead to tissue injury. Recruited cells undergo apoptosis and are phagocytosed and cleared by macrophages.

Manipulation of the innate immune response to improve host defense has the potential to be an effective therapeutic strategy, however such manipulation would have to be precise to avoid unwanted effects. A greater understanding of the processes involved in the host response to respiratory pathogens is therefore needed in order to identify suitable targets for manipulation. Computational models are ideal addition to the methods used to investigate the complex interactions that are involved in the immune response. They can be used to further understand the information gathered from previously generated data sets and can be used to inform further hypotheses and to refine future experiments.

The discipline hopping grant will enable me to spend a year in the Computational Biology group in the Department of Computer Science to allow me to develop skills in the development and application of computational modelling. The research currently being undertaken in the Computational Biology group make this the ideal environment in which to undertake this development. I would be working under the mentorship of Prof Rod Smallwood whose research is in computational modelling of cellular interaction in epithelial tissues (FLAME - Flexible Largescale Agent Modelling Environment, part of the Epitheliome Project funded by EPSRC).

At the end of the discipline hopping activity I would have the necessary skills to perform a programme of research which combines appropriate computational models with in vitro and in vivo models to investigate the innate immune response to respiratory pathogens.