
 About the Biomedical Informatics Hub
 Training
 Data Processing with AWK and UNIX
 Genomics
 Image Processing with ImageJ
 Introduction to Mathematical Modelling for Bioscientists
 Introduction to multilevel linear modelling with Stata
 Introduction to Python
 Introduction to R
 MATLAB
 Python for data analysis
 RNAseq
 Unix Workshop
 Meet the team
 Accessing Hub support
Mathematical models attempt to capture the essence of a biological system in the form of equations. The construction and analysis of such models allow us both to interrogate the biological system in ways which are not possible using experimentation alone, and to make quantitative predictions about the behaviour we expect the system to support. In contrast to the, perhaps more wellknown, statistical approaches, mathematical models attempt to illuminate the mechanisms driving specific phenomena and to simplify the analysis of the overall system by identifying the key components of such mechanisms.
Mathematical models can be used to describe systems of any scale, from the genetic up to the ecosystem level and typically are used in situations in which the variables of interest are timedependent. The beauty of mathematics is that it exposes universal properties that are preserved across biological systems, even when their scales vary greatly. By studying the geometry of the mathematical equations, we can make concrete statements about how the system should behave as well as how perturbations to it affect the resulting dynamics.
Workshop Aims:
 Introduction to developing mathematical models
 Simulation of mathematical models
 Phasespace analysis and prediction
 Code development to solve mathematical models
Prerequisites:
Knowledge of basic programming desired but not essential
Programme:
Day 1
 Defining mathematical models
 Contrasting mathematical with statistical models
 Developing a first model  worked example of the Brusselator system
 Simulating mathematical models using xppaut/MATLAB
Day 2
 Writing mathematical models in MATLAB/xppaut
 Introduction to phasespace analysis
 Using phasespace analysis to predict behaviour
Day 3
 Further analysis of mathematical models
 Introduction to bifurcation theory
 Using xppaut to predict dynamic behaviour
 Short introduction to parameter estimation (time allowing)
Workshop files
 Introduction to Mathematical Modelling for Bioscientists  Day 1
 Introduction to Mathematical Modelling for Bioscientists  Day 2
 Introduction to Mathematical Modelling for Bioscientists  Day 3
 Introduction to Mathematical Modelling for Bioscientists  Keynote slides
If you have any queries regarding this course, please contact Danny Galvis.
Dates:  10/07, 17/07, 24/07, 13:3016:30 

Instructors:  Dr Danny Galvis 
Location:  Hatherly B12 