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| In order to perform the analysis we will be introducing a new class of object from !ScrumPy, the !''DataSet. ''As the name implies, it's purpose is to store and maniplulate data either generated by model, or from an external source, or both. It has many of the same properties of a matrix, but in addition is capable of simple graph plotting and statistical analysis. | In order to perform the analysis we will be introducing a new class of object from !ScrumPy, the !DataSet. As the name implies, it's purpose is to store and maniplulate data either generated from models, from external source, or from both. It has many of the same properties of a matrix, but in addition is capable of simple graph plotting and statistical analysis. In this exercise it will be used to store a set of lp solutions generated over a range of constraints imposed on the oxygen uptake rate. |
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1. This should result in two new directories: "Model" and "Analysis". Note the strict seperation of the model definition components and the analysis components. This ensures (amongst other things) that an identical analysis could be performed on any other model, as long as naming conventions for the transport steps is mainitained. 1. Change directory to the Analysis directory: {{{$ cd Analysis}}} |
Practical 7
The effect of varying oxygen availabilty on Geobacillus
In this practical we will be investigating the the potential effects of limiting oxygen availability to a model of the organism Geobacillus thermoglucosidasius described here. In order to do this, we will be using the constraint scanning approach described in lecture 7. Geobacillus is a facultative aerobe this has been demonstrated experimentally and in the paper linked above we investigated aerobic and anaerobic metabolism. What we did not investigate is the situation in which oxygen is reduced but not completely absent, so, as well as being a new practical, this is also a new investigation - results generated here will be genuinely new - no-one will ever have seen them before!
In order to perform the analysis we will be introducing a new class of object from ScrumPy, the DataSet. As the name implies, it's purpose is to store and maniplulate data either generated from models, from external source, or from both. It has many of the same properties of a matrix, but in addition is capable of simple graph plotting and statistical analysis. In this exercise it will be used to store a set of lp solutions generated over a range of constraints imposed on the oxygen uptake rate.
Download the file P7.tgz into the area in whch you have been using for your other practicals.
- This is a compressed archive file and you will need to extract the files before they can be used:
$ tar -zxf P7.tgz
- This should result in two new directories: "Model" and "Analysis". Note the strict seperation of the model definition components and the analysis components. This ensures (amongst other things) that an identical analysis could be performed on any other model, as long as naming conventions for the transport steps is mainitained.
Change directory to the Analysis directory: $ cd Analysis