Dataset - Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties

Data needed to reproduce the results from the manuscript “Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties" by L. Miskovic, J. Beal, M. Moret, and V. Hatzimanikatis

1. Data generated with the ORACLE workflow that was used in the iSCHRUNK training:

• Classification label vectors for the three analyzed metabolic concentration cases:
  • Reference case: class_vector_train_ref.mat
  • Extreme1 case: class_vector_train_ex1.mat
  • Extreme2 case: class_vector_train_ex2.mat
• Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ_A, which is constrained between 0 and 1. 
  • Reference case: training_set_ref.mat
  • Extreme1 case: training_set_ex1.mat
  • Extreme2 case: training_set_ex2.mat
• Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.
  • Reference case: ccXTR_ref.mat
  • Extreme1 case: ccXTR_ex1.mat
  • Extreme2 case: ccXTR_ex2.mat
• Thermodynamics-based Flux Analysis (TFA) models for the three cases:
  • Reference case: tfa_ref.mat
  • Extreme1 case: tfa_ex1.mat
  • Extreme2 case: tfa_ex2.mat
• Parameter names identical for the three cases
  • parameterNames.mat

2. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 4).

• Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.
  • ccXTR_ValidNeg.mat
• Parameter sets used in validation
  • validation_set_neg.mat

3. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Table 3).

• Negative control:
  • Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.
    • Reference case: ccXTR_ValidRef_neg_agg.mat
    • Extreme1 case: ccXTR_ValidEx1_neg_agg.mat
    • Extreme2 case: ccXTR_ValidEx2_neg_agg.mat
  • Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ_A, which is constrained between 0 and 1. 
    • Reference case: validation_set_ref_neg_agg.mat
    • Extreme1 case: validation_set_ref_neg_agg.mat
    • Extreme2 case: tvalidation_set_ref_neg_agg.mat

• Positive control:
  • Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers.
    • Reference case: ccXTR_ValidRef_pos_agg.mat
    • Extreme1 case: ccXTR_ValidEx1_pos_agg.mat
    • Extreme2 case: ccXTR_ValidEx2_pos_agg.mat
  • Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ_A, which is constrained between 0 and 1. 
    • Reference case: validation_set_ref_pos_agg.mat
    • Extreme1 case: validation_set_ex1_pos_agg.mat
    • Extreme2 case: validation_set_ex2_pos_agg.mat

4. Reassignment study: validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 6 and Table 4).

• Negative control:
  • Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers.
    • Reference case: ccXTR_Valid_reassignment_neg.mat
  • Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ_A, which is constrained between 0 and 1. 
    • Reference case: validation_set_neg_reassignment.mat
• Positive control:
  • Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers.
    • Reference case: ccXTR_Valid_reassignment_pos.mat
  • Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ_A, which is constrained between 0 and 1. 
    • Reference case: validation_set_pos_reassignment.mat