Introduction to ScrumPy
This is a very brief introduction to ScrumPy, with emphasis on structural analysis. A more complete documentation can be found here.
Start ScrumPy
Assuming you have ScrumPy already installed on your computer (for example, by following the instructions on this page:
Open a terminal window. If you are running the virtual machine, click on Terminal under the Applications/Accessories menu item.
Virtual machine users can navigate to the supplied models directory by typing cd Modelling/models. Users of other systems can make an empty models folder and enter it.
Type ScrumPy, and it will start.
Loading and defining models
A pre-existing model is loaded by creating an instance of a model object ScrumPy.Model(model) with model being the name of the file (string-type ending with the ScrumPy file extension, .spy, all enclosed in quotes) containing the description of the model, provided that the file is stored in the directory from where ScrumPy was launched. If no name is provided, a GUI for selecting a model to open, is launched.
1 >>> m = ScrumPy.Model('toy_model.spy')
If this file has not yet been created, the instruction will open a new empty editor window; copy and paste the model description below. Having done that, select "compile" from the ScrumPy menu in the editor, and the model is loaded. toy_model.spy is the small structural model described by:
Structural()
A_tx:
x_A -> A
~
R_1:
A -> B
~
R_2:
B -> C
~
R_3:
C -> E
~
R_4:
B -> D
~
R_5:
D -> E
~
R_6:
D -> F
~
E_tx:
E -> x_E
~The reactions with the suffix "_tx" are transporters, i.e. they convert external metabolites (with prefix "x_"), which can be consumed or produced, to internal metabolites, which have no net consumption or production at steady state.
Properties of structural models
The class Model has a range of methods, of which some are only useful for kinetic models (which are also structural models, but the opposite is not true). The methods are actions on the model object (m in this case), and are invoked by instructions such as x = m.Method(), where the outcome is a new data object x that contains the result. Among the structurally relevant methods we find ConsMoieties() - which returns a list of conserved moieties; DeadReactions() - which returns a list of reactions that cannot carry steady state flux; FindIsoforms() - which identifies reactions from the model that are redundant, i.e. a set of reactions that have identical stoichiometry; ElModes() - which returns an elementary modes object; Externals() - which returns a list of external metabolites.
The stoichiometry matrix
The fields Model.sm and Model.smexterns are the two stoichiometry matrices associated with a model - the former is the internal matrix, the latter the external. The external matrix contains infomation about external metabolites, whereas the internal does not. All instances of ScrumPy matrices (subclasses of DynMatrix) have the fields cnames - column names and rnames - row names.
Useful methods of sm (and smexterns) include ReacToStr(reac),
and InvolvedWith(name),
Reaction reversibility
ScrumPy accepts three reversibility symbols: "->" - left to right irreversible, "<-" - right to left irreversible, and <> - reversible. Reaction reversibility is handled by the stoichiometry matrix.
Nullspace analysis
The kernel of the stoichiometry matrix can be calculated using the sm.NullSpace() method (smexterns also has the method, but the kernel of the external matrix is only related to the futile cycles of the model, not possible steady-state solutions that involve consumption of externals).
Even if the signs of some of the coeffients indicate thermodynamically infeasible solutions (e.g. all active reactions in the first column have negative coefficients, even though they are irreversible) a lot of useful information can be obtained form k. For instance, we see that the row associated with R_6 is a null-vector, indicating that there is no steady-state solution involving R_6. In fact this is how ScrumPy detects dead reactions with the method DeadReactions().
Also, note that some of the row-vectors are proportional to each other - R_2, R_3; R_4, R_5; and E_tx, A_tx, R_1. This implies that these sets must carry flux in a coordinated fashion, e.g. any flux solution involving R_4 must also involve R_5. These sets of coordinated reactions are referred to as enzyme subsets and can be determined using the Model method EnzSubsets(). This method returns a dictionary object where keys are subset names (or reaction name if a reaction is in a singleton set) and values are nested dictionaries where keys are reaction names and values are the flux ratios of the reactions. The key DeadReacs maps to a list of dead reactions.
The elementary modes of a model can be analysed using the method Model.ElModes(). The field mo is a matrix similar to k.
The relationship between modes and metabolites is stored in the sto matrix.
The methods Modes() and Stos() returns a string with with same information as the matrices above.