Transcript Stochastic effects in microbial infection - National e

eSI workshop
Stochastic Effects in Microbial
Infection
The National e-Science Centre
Edinburgh
September 28-29, 2010
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
Infectious Diseases
Genetics
host
environment
Vaccination
Immunology
Ecology
Microbiology
Epidemiology
Drug therapies
pathogen
Modification of human behavior, environment
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
This Workshop
Stochastic effects in microbial infection
host
Immunology
Immune evasion
Drug resistance
Evolution
Bistability
Phase variation
Biofilms
Ecology
environment
Epidemiology
pathogen
Individual and Population
. molecules, cells, organisms and intervention
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
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)
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.
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?