|
Size: 359
Comment:
|
Size: 1359
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 2: | Line 2: |
| = ScrumPy Manual = | = ScrumPy - Metabolic Modelling in Python = <<TableOfContents>> |
| Line 4: | Line 6: |
| === Metabolic Modelling === === Design Philosophy === === Python === ==== Why Python ? ==== ==== Knowing syntax vs programming ==== ==== Inbuilt types and classes ==== int, float, str, list and dict |
|
| Line 5: | Line 14: |
| === Why Python ? === | ==== Classes and sub-classes ==== ==== Repetiton and Decisions ==== ==== Functions ==== ==== Modules and Packages ==== ==== Standard libraries ==== Not all of them ! In practice math and random are the most commonly with {{{ScrumPy}}}. sys and os are also useful - any other suggestions ? |
| Line 7: | Line 21: |
| 'cause it's great. | == ScrumPy Model Description Language == [[SpyMDL#Overview|Overview]] |
| Line 9: | Line 24: |
| [[SpyMDL#Identifiers|Identifiers]] | |
| Line 10: | Line 26: |
| === What ScrumPy Is === | [[SpyMDL#Reactions|Reactions]] |
| Line 12: | Line 28: |
| === What ScrumPy Isn't === | [[SpyMDL#Directives|Directives]] |
| Line 14: | Line 30: |
| === Quick examples === | == Analysis of Models With ScrumPy == === The ScrumPy Modelling Environment === === The Matrix Class === Fully described in utility section - enough here to understand SMs, datasets and monitors. |
| Line 16: | Line 35: |
== ScrumPy model format == == Structural Analysis of Models == == Kinetic Analysis of Models == |
=== Anatomy of a ScrumPy Model === === Kinetic Modelling === === Structural Modelling === === Linear Programming === == Secondary Analysis of Model Results == === Data sets === === Fitting and Optimisation === == Automatic Model Building == |
| Line 24: | Line 44: |
| == The Utility Package == === Dynamic Matrices === |
ScrumPy - Metabolic Modelling in Python
Contents
1. Introduction
1.1. Metabolic Modelling
1.2. Design Philosophy
1.3. Python
1.3.1. Why Python ?
1.3.2. Knowing syntax vs programming
1.3.3. Inbuilt types and classes
int, float, str, list and dict
1.3.4. Classes and sub-classes
1.3.5. Repetiton and Decisions
1.3.6. Functions
1.3.7. Modules and Packages
1.3.8. Standard libraries
Not all of them ! In practice math and random are the most commonly with ScrumPy. sys and os are also useful - any other suggestions ?
2. ScrumPy Model Description Language
3. Analysis of Models With ScrumPy
3.1. The ScrumPy Modelling Environment
3.2. The Matrix Class
Fully described in utility section - enough here to understand SMs, datasets and monitors.
3.3. Anatomy of a ScrumPy Model
3.4. Kinetic Modelling
3.5. Structural Modelling
3.6. Linear Programming
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