MinCell 2

The code for this example is available in our main GitHub repository. https://github.com/TP-Watson/py_cFBA

We continue with the simple toy model of a minimal cell. This is the same example that is presented in our article [1]. A schematic of the model with its corresponding S matrix can be seen in the following scheme:

../_images/MinCell2_1.jpg

In this example we will simulate the minimal cell including a cost asociated to the cycling of the Storage metabolite. Simply put, the ‘S’ matrix is modified to include a loss of Intermediate in the degradation of Storage. We will follow the same steps as in MinCell 1.

You can follow this step-by-step implementation with the provided codes available at our GitHub. In all the examples below, uptake of substrate is only possible during the first 3 timepoints of the simulation.

Now, we will take you through each step in the step-by-step guide:

Change the stroichiometry of the model

First of all, the model stoichiometry needs to be modified to include the cost of storage cycling. Simply put, the reactions of the model will be:

  • Storage synthesis: - 1 Intermediate + 1 Storage

  • Storage degradation: - 1 Storage + 0.8 Intermediate

Since the model is very similar to that in MinCell 1, we will only highlight the steps that are different.

Generate and popullate the excel sheet

The stoichiometric matrix was modified in excel and can be read from an excel sheet named ‘Model S matrices.xlsx’, sheet name ‘Basic model 2’.

S_mat = pd.read_excel('Models S matrices.xlsx',sheet_name='Basic model 2',index_col=0, header = 0)

# Create model backbone for cFBA
data = cFBA_backbone_from_S_matrix(S_mat)
generate_cFBA_excel_sheet(S_mat, data, 'MinCell_02.xlsx')

The input for this functions are the same as in MinCell1. Further, the excel sheet can be filled out with the specifics of the model. The only difference with the previous model lies in the lower bounds. Since this model includes a reaction for the syntehsis and degradation of storage, then both reactions are irreversible.

../_images/MinCell2_2.jpg

Note

These changes are already included in the files provided on our GitHub. For this reason, the code is initially commented out.

Generate SBML file and perform simulations

Next we generate the SBML file.

# Create SBML file for the model
excel_file = 'MinCell_02.xlsx'        # Input Excel file
output_file = 'MinCell SBMLA_02.xml'  # Output SBML file
excel_to_sbml(excel_file, output_file)

Then the SBML file generated is used to perform optimization.

# Load the SBML file and set up the cFBA model
sbml_file = "MinCell SBMLA_02.xml"  # SBML file for the model
quotas = []  # List of quotas (none in this case)
# Generate the Linear Programming (LP) model components for cFBA
cons, Mk, imbalanced_mets, nm, nr, nt = generate_LP_cFBA(sbml_file, quotas, dt)

# Find the optimal alpha value
print('Time simulation:')
alpha, prob = find_alpha(cons, Mk, imbalanced_mets)
print('Growth of the system: {:.2f}'.format(alpha))  # Print the optimal alpha value

The function find_alpha prints the time it takes to compute the search. the current code should give the following output:

Time simulation:
0.04 min
Growth of the system: 1.70

You can plot the metabolites and fluxes just as we did in MinCell 1. You could try out what the effect of more expensive storage cycling is to the model. At some point, storage becomes so expensive, that the model will stagnate at a given growth without using storage.

With this, you have finalized the tutorial on MinCell 2. You can move onto the next examples in which:

  • In MinCell 3 we include three different enzymes as catalysts.

  • In MinCell 4 we include a temporal limit on enzyme synthesis.