ScrumPy - Metabolic Modelling in Python
- ScrumPy Model Description Language
- Analysis of Models With ScrumPy
- Secondary Analysis of Model Results
- Automatic Model Building
- Bioinformatics Functions
- The Utility Package
1.1. Metabolic Modelling
1.2. Design Philosophy
Scrumpy is unusual, but not unique, in that the primary user interface is a language (it is an oversimplification to refer to it as a command line interface) rather than a more conventional GUI. The underlying reason for this choice is a simple one: A GUI restricts the user to only those actions which the programmer mpredicted the user might wish to perform. In some contexts this is not a problem, simple text editing and web-browsing being examples.
However, in metabolic modelling (and scietific/research contexts in general) it is much harder for the programmer to predict what a user may wish to do. MORE HERE -
Furthermore, in the twenty or so years in which I have been involved in the field, I have lost count of the number of presentations I've listened to for software (not only modelling or scientific) making the claim that the software is intuitive and user friendly, to the extent that this has become a mantra to be uttered at the begining of every presentation. Most of it has been unconvincing at best.
1.3.1. Introduction to Python
1.3.2. Introduction to ScrumPy
1.4. Python Notes
2. ScrumPy Model Description Language
3. Analysis of Models With ScrumPy
3.1. The ScrumPy Modelling Environment
3.1.1. Running ScrumPy
3.1.2. Loading Models
3.1.3. Structural Modelling
''Structural modelling'' covers computations on the stoichiometry matrix of the model, including identification of orphan metabolites and dead reactions, conservation relationships, null space analysis and elementary modes. (Latter two to be added.)
3.1.4. Linear Programming
''Linear Programming'' is for finding optimal flux patterns in a metabolic network, also known as Flux Balance Analysis.
3.1.5. Kinetic Modelling
''Kinetic Modelling'' covers dynamic models where rate functions of the models are specified. Finding and examining steady states is covered as well as simulating time courses. ??Control analysis here or as a separate section??
4. Secondary Analysis of Model Results
4.1. Data sets
4.2. Fitting and Optimisation
5. Automatic Model Building
6. Bioinformatics Functions
7. The Utility Package