Tutorial 2 — Construct a functional small model¶

This exercise deals with a small glycolysis model in RAVEN format and shows the most basic aspects of stoichiometric modelling: building a model from scratch, setting parameters and running simple simulations.

You start from empty.xml (an incomplete model containing the first reaction of glycolysis) and extend it until it forms a functional network. The companion script tutorial2_solutions.m reads the provided solution model small.yml directly with readYAMLmodel.

Goal

Build a model of glycolysis and answer: how many units of ATP can be generated from one unit of sucrose?

Step by step¶

1. Import the starter model¶

smallModel = importModel('empty.xml');
sol = solveLP(smallModel);
printFluxes(smallModel, sol.x, true);

When you import the (incomplete) model, RAVEN warns that some internal metabolites are used in only one reaction, so the only feasible solution is zero flux. If sol.f equals zero the problem is not solvable, and you need to ensure that uptake and excretion of all necessary metabolites are added to the model before running solveLP again.

2. Understand internal vs. external metabolites¶

Under the steady-state assumption, the production rate of each internal metabolite must equal its consumption rate. A metabolite that participates in only one reaction can therefore carry no flux. The fix is to introduce external (boundary) metabolites, which need not be mass-balanced. Reactions involving them are exchange reactions.

3. Add the glycolysis reactions and exchanges¶

Add the remaining reactions of glycolysis, then add exchange and transport reactions so that the cell can take up sucrose and excrete pyruvate and water.

Re-import the model with importModel to check the structure as you go.

4. Handle cofactors and define the objective¶

Two problems remain:

Regenerating NAD⁺ from NADH. Either extend the model (e.g. ethanol production via pyruvate decarboxylase + alcohol dehydrogenase) or add "fake" exchange reactions for NAD⁺/NADH.
Measuring ATP yield. Add a "fake" ATP hydrolysis reaction and maximise its flux — because production must match consumption, this reports how much ATP the network can make. Watch the directionality so you don't accidentally allow free ATP synthesis.

Constrain the sucrose uptake to one unit and set the ATP-hydrolysis reaction as the objective to maximise.

5. Solve¶

Once the model is complete, solve the linear programming problem and print the fluxes. The completed model is provided as small.yml, which the solutions script reads directly:

smallModel = readYAMLmodel('small.yml');
sol = solveLP(smallModel);
printFluxes(smallModel, sol.x, true);                                          % exchange fluxes
printFluxes(smallModel, sol.x, false, 10^-5, [], '%rxnID (%rxnName):\n\t%eqn\n\t%flux\n');  % all fluxes

See Tutorial 2 in RAVEN tutorials.docx for more details and the worked answer.

Full script¶

% tutorial2
%   This exercise deals with the a small glycolysis model in RAVEN
%   compatible Excel format and shows the most basic aspects of the
%   stoichiometric modelling. It is shown how to build a simple model from
%   scratch, set parameters and perform simple simulations.
%   See Tutorial 2 in "RAVEN tutorials.docx" for more details.

%Import the model into a RAVEN model structure
smallModel=importModel('empty.xml');

%This solves the linear programming problem.
%NOTE: if sol.f is equal to zero then the problem is not solvable. Ensure
%that uptake and excretion of all necessary stuff are added to the model
%and run the solveLP again.
sol=solveLP(smallModel);

%Print the resulting exchange fluxes
printFluxes(smallModel,sol.x,true);

%Print all fluxes
printFluxes(smallModel,sol.x,false,10^-5,[],'%rxnID (%rxnName):\n\t%eqn\n\t%flux\n');