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Direction of future investigations Our theme activities have highlighted several important directions for future research:
Stochastic effects in microbial infection:
1. Developing models which bridge the gap between biological realism and mathematical tractability
2. Using labelling experiments combined with models to disentangle the effects of growth, switching and environmental change.
3. Developing large-scale computational models which combine intracellular genetic network dynamics with spatially-resolved population dynamics.
1. Development of models that move from studying properties of individuals to studying behaviours of heterogeneous populations.
2. Development of an Evolutionary Systems Biology that combines mechanistic models with evolutionary processes and can be used to predict evolutionary outcomes.
3. Close collaboration between experimental and computer scientists to meet the grand challenge of predicting phenotype from genotype in the context of evolving genomes.
Metabolic Networks and Modelling
1. Exploration of alternative theoretical approaches, since similar problems are faced in other fields.
2. Exploring how the genotype maps to the metabolome.
3. Consolidating existing work on dynamic Flux Balance Analysis, an important method which was introduced nearly 10 years ago but remains to be standardized.
Changes in the way we do e-Science The work of this theme has highlighted very clearly the importance of close collaboration between experimentalists and computational modellers. For example, while many theoretical models have been developed to explain the role of stochastic switching in microbial population dynamics, these generally do not address the same questions as those being posed by microbiologists. Likewise interaction with modellers can help microbiologists to move beyond the search for molecular mechanisms and ask broader and more general scientific questions. By bringing together microbiologists and modellers, this theme has highlighted this issue and already gone some way towards increasing the interaction of e-scientists with experimental biologists; we hope this trend will continue in further work.