Metabolizer integrates three different pathway tools:

• Activity allows you to see how module activity changes in different conditions.
• Knockout allows you to simulate knock outs or over-expressions of one or several genes or the effect of a drug in metabolic module genes.
• Prediction allows you to train a prediction model and test it with different data.

Metabolizer is able to integrate RNA-Seq and microarray data to produce an accurate result.

Metabolizer-Activity allows you to compute a activity (overall flux) each metabolic module and each one of your data samples. Therefore, it allows you to compare the activity values:

• Between two groups, in order to see which module is changed and how

In order to check how to use these options please see Differential signaling.

Metabolizer-Knockout allows you to modify the expression value of a set of genes and then check the effect that this change has at the level of module activity. In this way, you can simulate knock outs (KO) or over-expressions of one or several genes or the effect of drug(s) in the activity of metabolic modules. The simulations can be done:

• Using single sample. This option calculates fold-change of module activity between after and before KO.
• Using multiple samples. This option uses one selected sample to calculate effect of in silico manipulation of gene expression on module activity. Rest of the samples are used to train a prediction model. Metabolizer uses this trained model to calculate class probability of selected sample including before and after simulation.
• User can use the options below by selecting single/multiple genes or drug(s) on interactive pathway panel. Metabolizer also allows to auto-KOs. Selecting auto KOs calculates effect of simple KOs using all module genes by one by.

Metabolizer allows you to train, download and test a prediction model for your dataset using different machine learning algorithms. Prediction can be done using following methods; LDA, SVM, Random Forest. The model can be trained and test:

• Using different groups (2 and more groups) of samples.

In order to check how to use these options please see Prediction.