The Analyzer Server has an extensible catalog of combination interaction models. Each defined combination interaction model is instantiated within the context of the experimentally observed data which is the subject of the requested analysis. The following tables summarize the built-in combination interaction models and the analytical output associated with each model.
Built-In Models
An unlimited number of combination interaction models can be defined using the Chalice Analyzer SDK, the default built-in models are summarized in the following table.
| Prototype | Name | Description |
|---|---|---|
 | Loewe Additivity (ADD) | The most widely used combination reference is Loewe additivity1,2, or "dose additivity" which describes the trade-off in potency between two agents when both sides of a dose matrix contain the same compound. |
 | Bliss Independence (BLISS) | The Bliss independence model1 corresponds to a multiplicative effect in growth measures, and is the preferred reference for synergy in some contexts2, especially genetics3. |
 | Bliss Boosting (BOOST) | Bliss boosting1, which models boosts in efficacy at high concentrations different from what the single agents can achieve, is adapted the Bliss independence model2 that corresponds to a multiplicative effect in growth measures. |
 | Highest Single Agent (HSA) | The Highest Single Agent (HSA) model, or Gaddum’s non-interaction reference (Berenbaum 1989), is based on the intuition that if a combination’s effect exceeds those of its constituents, there must be some interaction. |
 | Potentiation (POT) | The power-law "potentiation" model describes strong shifts in potency1. |
Combination Interaction Analytical Output
The Analyzer Server produces a defined set of individual analytical outputs for each defined combination interaction model. The analytical outputs are named according to the model abbreviation and the analytical output name. The following table summarizes the combination model analytical outputs.
| Name | Description |
|---|---|
| [?] Model | The model attribute is a visual representation of the instance of the model for the data item that is the subject of the analysis. The model attribute is used to evaluate the model effect predicted for each dose and ratio of the test substances present in the data item that is the subject of the analysis. Each location in the display matrix is labeled with the numerical effect value for the model, and a color that corresponds to the intensity of the value. |
| [?] Excess | The excess attribute is a visual representation of the deviation between the observed data and the instance of the model for the data item that is the subject of the analysis. The excess attribute is used to evaluate the magnitude of the observed data that exceeds the model effect predicted for each dose and ratio of the test substances present in the data item that is the subject of the analysis. Each location in the display matrix is labeled with the numerical excess value, and a color that corresponds to the intensity of the excess. |
| [?] Volume | The volume is the sum of the individual deviations between the observed data and the instance of the model for the data item that is the subject of the analysis. The volume attribute is used to assess the overall magnitude of the combination interaction excess. The volume attribute can be used to compare different data items to assess thier relative combination interaction magnitude. |
| [?] Volume Error | The volume error is the standard deviation for the individual deviations between the observed data and the instance of the model for the data item that is the subject of the analysis. The volume error is used to assess the magnitude of the uncertainty in the given volume value. |
| [?] Average | The average is the mean of the individual deviations between the observed data and the instance of the model for the data item that is the subject of the analysis. The average attribute is used to assess the breadth of the combination interaction excess compared to the overall volume. The volume attribute can be used to compare different data items to assess thier relative combination interaction magnitude. |
| [?] Average Error | The average error is the sample variance divided by the number of samples for the individual deviations between the observed data and the instance of the model for the data item that is the subject of the analysis. |
| [?] Parameters | The parameters are the computed parameters, if any, for the instance of the model for the data item that is the subject of the analysis. |
| [?] Log Chi Squared | The log chi squared is the base 10 logarithm of the chi squared for the comparison of the observed data to the instance of the model. |