= Introduction to ScrumPy =

This is a very brief introduction to {{{ScrumPy}}}, with emphasis on structural analysis. A more complete documentation can be found [[ScrumPy/Doc | here]].

== Start ScrumPy ==
Assuming you have !ScrumPy already installed on your computer (for example, by following the instructions on [[ScrumPyVM|this page]]:
 1.  Open a terminal window.  If you are running the virtual machine, click on ''Terminal'' under the ''Applications/Accessories'' menu item.
 1.  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.
 1.  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.



{{{#!python
>>> 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:

{{{#! python
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.

{{{#!python
>>> m.sm.cnames
['R_1', 'R_2', 'R_3', 'R_4', 'R_5', 'R_6', 'E_tx', 'A_tx']
>>> m.sm.rnames                #but m.smexterns.rnames will be longer
['A', 'B', 'C', 'E', 'D', 'F']
}}}

Useful methods of {{{sm}}} (and {{{smexterns}}}) include {{{ReacToStr(reac)}}},
{{{#!python
>>> print m.sm.ReacToStr('R_2')
R_2:
	1/1 B -> 1/1 C
	~

}}}

and  {{{InvolvedWith(name)}}},
{{{#!python
>>> m.sm.InvolvedWith('R_2')
{'C': mpq(1,1), 'B': mpq(-1,1)}
>>> m.sm.InvolvedWith('C')
{'R_2': mpq(1,1), 'R_3': mpq(-1,1)}
}}}

=== 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.

{{{#!python
>>> m.sm.GetIrrevs()
['R_1', 'R_2', 'R_3', 'R_4', 'R_5', 'R_6', 'E_tx', 'A_tx']
>>> m.sm.MakeRevers('R_2')      
>>> m.sm.GetIrrevs()
['R_1', 'R_3', 'R_4', 'R_5', 'R_6', 'E_tx', 'A_tx']
>>> m.Reload()

>>> m.sm.GetIrrevs()
['R_1', 'R_2', 'R_3', 'R_4', 'R_5', 'R_6', 'E_tx', 'A_tx']
}}}

== 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). 

{{{#!python
>>> k = m.sm.NullSpace()
>>> k
    c_0 c_1 

 R_1 -1/1  0/1 
 R_2 -1/1  1/1 
 R_3 -1/1  1/1 
 R_4 0/1  -1/1 
 R_5 0/1  -1/1 
 R_6 0/1  0/1 
 E_tx -1/1  0/1 
 A_tx -1/1  0/1 
}}}

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()}}}.

{{{#!python
>>> m.DeadReactions()
['R_6']
}}}

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.

{{{#!python
>>> ess=m.EnzSubsets()
>>> ess
{'Ess_3': {'R_4': mpq(-1,1), 'R_5': mpq(-1,1)}, 'Ess_2': {'R_2': mpq(1,1), 'R_3': mpq(1,1)}, 'Ess_1': {'E_tx': mpq(1,1), 'R_1': mpq(1,1), 'A_tx': mpq(1,1)}, 'DeadReacs': {'R_6': mpq(1,1)}}
}}}

The elementary modes of a model can be analysed using the method {{{Model.ElModes()}}}. The field {{{mo}}} is a matrix similar to {{{k}}}.
{{{#!python
>>> elmo = m.ElModes()
>>> elmo.mo
    ElMo_0 ElMo_1 

 R_1 1/1  1/1 
 R_2 1/1  0/1 
 R_3 1/1  0/1 
 R_4 0/1  1/1 
 R_5 0/1  1/1 
 R_6 0/1  0/1 
 E_tx 1/1  1/1 
 A_tx 1/1  1/1 
}}}

The relationship between modes and metabolites is stored in the {{{sto}}} matrix.
{{{#!python
>>> elmo.sto
    ElMo_0 ElMo_1 

 x_A -1/1  -1/1 
 A 0/1  0/1 
 B 0/1  0/1 
 C 0/1  0/1 
 E 0/1  0/1 
 D 0/1  0/1 
 F 0/1  0/1 
 x_E 1/1  1/1 
}}}

The methods {{{Modes()}}} and {{{Stos()}}} returns a string with with same information as the matrices above.
{{{#!python
>>> print elmo.Modes()
ElMo_0, 1/1 E_tx, 1/1 R_1, 1/1 R_2, 1/1 R_3, 1/1 A_tx

ElMo_1, 1/1 E_tx, 1/1 R_1, 1/1 A_tx, 1/1 R_4, 1/1 R_5

>>> print elmo.Stos()
ElMo_0:
	1/1 x_A -> 1/1 x_E
	~
ElMo_1:
	1/1 x_A -> 1/1 x_E
	~
}}}