When: 28th September 2017 13:00 – 14:00
Where: Cole 1.33b
Series: Systems Seminars Series
Michael Pitcher’s abstract
Tuberculosis (TB) is one of the world’s most deadly infectious diseases, claiming over 1.4 million lives every year. TB infections typically affect the lungs and treatment regimens are long and arduous, requiring at least 6 months of daily chemotherapy. Previous investigations have shown TB to have unique localisations within the lung at varying stages of infection. The initial implant and the primary lesion which arises from it can occur anywhere in the lungs, with a greater probability of occurrence in the lower to middle regions of the lung. However, reactivation of a previously latent form of disease always involves cavitation of the tissue at the apical regions. This difference in spatial location of TB infections suggests two important factors: i) bacteria are able to disseminate across the lung in some manner, and ii) the environment at the top of the lung has some properties that make it preferential for TB replication.
In this project, we aim to build a whole-organ model of the lung and surrounding lymphatics which incorporates both bacterial dissemination possibilities and lung tissue spatial heterogeneity in order to understand their impact on TB. We develop ComMeN (Compartmentalised Metapopulation Network), a Python framework designed to allow the easy creation of complex network-based metapopulations with spatial heterogeneity upon which interaction dynamics can be applied, with discrete event modelling using the Gillespie Algorithm. We then extend this framework to create a TB-specific model, PTBComMeN, which models a TB infection occurring over lung tissue which is divided into patches, each of which contains spatial attributes appropriate to its position in the lung, such as ventilation, perfusion and oxygen tension. Events dictate the interactions between cells and bacteria and their interaction with the environment, with dissemination occurring between edges joining patches on the lung network. This model allows experimentation into studying the effects spatial heterogeneities and bacterial dissemination may have on the progression of disease and the model is designed to provide insight into the factors that result in long treatment times for TB.
Xue Guo’s abstract
By the year 2050, the global urban population will reach 2.5 billion. While the fast pace of urbanisation brings improved quality of life initially, the surging population will inevitably lead to unique urban issues. Emerging research fields, with the aim of creating smarter cities, plan to counteract these problems. To facilitate this research, we need solid models to generate ’fake cities’, which cannot be easily produced by existing random graph algorithms due to spatial constraints. Therefore, we propose a new model for the co-evolution of city and population, which can show how street network forms, how population spreads and how settlements emerge and diminish. The new model will be a random city generator, which could be used to backtrack the history and predict the future of a city, or act as test cases for the validation and evaluation of urban optimisation algorithms.