1. eSI workshop Stochastic Effects in Microbial Infection The National e-Science Centre Edinburgh September 28-29, 2010

2. Infectious diseases Major global health burden Economic costs and Human welfare Infectious agents: Bacteria: Salmonellosis; urinary tract infections, meningitis, MRSA, Campylobacter, drug resistant TB Viral: HIV, influenza etc Parasites: malaria, sleeping sickness] Hospital acquired and Community acquired

3. host pathogenenvironment Infectious Diseases Microbiology Epidemiology Immunology Genetics Vaccination Drug therapies Modification of human behavior, environment Ecology

4. Challenges? Complexity of both host and pathogen Emerging infectious diseases Resistance to existing therapies New drug development high costs, low success rate, long process, bottlenecks Little investment from pharmaceutical industries Animal welfare (3Rs- reduce refine replace) Work towards predictive biology underpinned by experimentation in an iterative, interdisciplinary fashion

6. This Workshop Stochastic effects in microbial infection

7. host pathogenenvironment Evolution Individual and Population molecules, cells, organisms and intervention Bistability Phase variation Biofilms Immune evasion Drug resistance Epidemiology Immunology. Ecology

8. Aim: interaction between microbiologists and modellers What can we do with modelling? Molecular level: simulations of drug-receptor interactions Genetic level: modelling gene regulatory networks (eg the fim switch) Multi-cell level: modelling biofilms, population dynamics models (eg switching cells in switching environments) Longer timescale: modelling evolution of new pathogens Larger lengthscale: modelling spread of epidemics

9. What kinds of modelling can we do? Spatially-revolved versus spatially homogeneous cell growth in a biofilm versus a chemostat; modelling cell division versus cell metabolism Time-resolved versus steady-state variability between cells in average gene expression; variability in time to full induction after stimulus Stochastic versus deterministic probability few cells survive antibiotic treatment; modelling growth of a large population Analytical theory versus computer simulation simple model (may be far from reality); complex realistic model (may be hard to understand)

10. What kinds of modellers are there? (thanks to Martin Howard) Mathematicians Like to solve well-posed questions analytically. Engineers Practical; tend to see the system as a machine. Computer Scientists Good at simulating complex systems, informatics, databases. Physicists Can do theory or simulations. Tend to want to simplify the problem.

11. Questions to think about Where are stochastic effects most important in infection? How can stochastic modelling best be used to help understand microbial infection? For what topics could combining modelling and experiments be productive? Are there things we should be doing differently?