Module Information

Aim: To acquaint students with state of the art modelling and statistical techniques in the systems biology of gene networks. It is expected that students who have taken the course will have mastered the basic set of ideas required in order to carry out further research in network inference in systems biology, and many of the statistical techniques will be appropriate to a broader research base in systems biology.

• Stochastic simulation; probability-integral transform, rejection sampling

• Stochastic processes; Poisson processes, Markov chains, birth and death processes, diffusion processes, Fokker-Planck equation
  

• Stochastic models of transcription and translation

• Master equations with examples, Gillespie algorithm

• Inference of stochastic differential equations

• Models of gene networks.
  

• Network inference
  

• Network modelling and inference using hidden variables.
  

• Use of MCMC in inference; Gibbs and Metropolis-Hastings samplers
  

• Constructing MCMC algorithms.
  

• Data integration models.
  
  

Illustrative Bibliography:

D. Wilkinson “Stochastic Modelling for Systems Biology”, Chapman & Hall

N. Lawrence, M. Girolami, M. Rattray, G. Sanguinetti “Learning and Inference in Computational Systems Biology”, MIT Pres

Assessment:

3 hour written exam to be held in the April exam session : 70%

4-6 assessed exercises, including a programming exercise: 30%

The assessed exercises will be set approximately every 2 weeks, and due 1 week later.

There will also be a number of unassessed exercises, which will be reviewed in the Examples Class.

Schedule:

Term 2 Weeks 15-24, Room 325 Coventry House

Mondays 2-4pm

Tuesdays 11-12 noon

Examples class: every other week, Wednesdays 4:30-6pm