Systems modelling and network analysis
This module provides comprehensive knowledge of the main, current techniques used in computational Systems Biology simulations.
- Constraint-based modelling of genome scale metabolic networks
- Computer simulation of Ordinary Differential Equation models
- Metabolic Control Analysis
- Exact stochastic simulation of molecular interaction networks
- Hybrid Computer Simulation of multiscale models
- Petri Net representation of molecular interaction networks
- Model checking
This online module aims to:
- Provide comprehensive knowledge and hands on experience of the main techniques currently used in computational Systems Biology simulations.
- Enable students to independently conduct computational analyses of a system and how to interpret the results.
- To increase awareness of the mathematical concepts underpinning quantitative/systems biology research in the biosciences.
- To increase awareness of the utility and need of computational and mathematical sciences in the biosciences.
- To increase awareness of the major molecular concepts relevant to current systems biology research.
- To enable delegates to evaluate the quality of data and information.
- To enable delegates to access publicly available information for the development of in silico network models.
On successful completion of this module, students will be able to:
- Comprehensively describe the current computational techniques for Systems Biology simulations.
- Demonstrate an ability to independently carry out simulations given a data set.
- Critically evaluate the suitability of different experimental data sets for different simulation approaches.
- Critically evaluate and interpret the output of a particular simulation.
Professor of Systems Biology
At least a 2.2 degree (or equivalent) in a relevant biological sciences discipline. Applicants not possessing these qualifications may be considered, depending on the length and quality of their practical experience and recommendations from employers/supervisors.Apply Now