Enterocyte specific analysis

If you are interested in the interaction of an organism with the human small intestine, the global usage has been adapted in two specific functions. In this case, one inputted model is joined to a small intestinal epithelial cell (sIEC) in a pairwise ecosystem.

Enterocyte model

The sIEC is adapted from the one built in “Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells”, Sahoo et al, 2013, It has been translated from matlab to sbml, and its namespace has been adapted to the BiGG’s namespace, which is compatible with CarveMe reconstruction. Therefore, the model inputted should be built in the BIGG’s namespace to be compatible. An option enabling the analysis in the AGORA / Virtual Metabolic Human namespace is in development, but requires further testing to be reliable.

Infering interaction score and type

The arguments model, medium and undescribed_metabolites_constraint are the same as in global usage. The sIEC has two external compartments : the lumen and the blood. The constraints inputted in the argument “medium” are applied to the lumen compartment, as it is the medium shared between the enterocyte and the other organism. The blood medium is by default constrained based on an “Average American Diet (AAD) extracted from from https://doi.org/10.1093/hmg/ddt119.

int_score, int_type = mimeco.analysis.enterocyte_interaction_score_and_type(model1, Western_diet,
                                                                  undescribed_metabolites_constraint="as_is", solver = "gurobi")

  • The optional argument medium_blood allows you to input your custom blood medium. It should be a pandas.DataFrame of the following format:

    • Index : Exchanged metabolites with the blood (except default AAD where it is exchange reactions)

    • column 1 : header = “lb”, lower_bound to constrain the reaction with

    • column 2 : header = “ub”, upper_bound to constrain the reaction with

  • The optional argument plot is set to “False” by default. When set to “True”, the function will show a matplotlib plot of the Pareto front to ease the analysis.

int_score, int_type = mimeco.enterocyte_interaction_score_and_type(model1, Western_diet,
                                                                  undescribed_metabolites_constraint="as_is",
                                                                  solver = "gurobi"
                                                                  namespace="BIGG", plot = False)

Predicting cross-feeding between models

This function predicts exchanged metabolites that favor enterocyte maintenance. Its options are the same as in the corresponding functions in global usage.

potential_crossfeeding = mimeco.analysis.enterocyte_crossfed_metabolites(model = model1, medium = Western_diet, undescribed_metabolites_constraint = "as_is",
                                                              solver = "gurobi", model_biomass_id = model1_biomass_id, namespace = "BIGG",
                                                              plot = False, sample_size = 1000)