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The mRFP1 model was generated by homology-modelling using server Swiss-Model.

miR-155-deficient, breast cancer mouse model was generated by mating, as described previously.

Molecular graphics for the best binding model was generated using Discovery Studio Visualizer 2.0.

After a refinement process, the final model was generated.

A highly predictive and statistically significant model was generated.

An empirical model, namely second-order polynomial regression model was generated and adjusted to CCD data.

Artificial neural network (ANN) model was generated to validate the RSM predictions.

A fourth order discrete time model was generated from the data, and showed excellent prediction results.

A significant (R2 = 0.9645, P < 0.0001) quadratic polynomial mathematical model was generated.

A homology model was generated for Nce103 based on the crystal structure of Can2.

Further, Artificial Neural Network (ANN) model was generated to validate the RSM predictions.