1. A computer-implemented method for classifying a biological specimen, the method comprising: (A) determining whether a model precondition included within a selected model in a plurality of models for determining whether a biological specimen has a disease state or has a drug respondent state has been satisfied, wherein each model in the plurality of models is associated with a disease state or a drug respondent state; wherein the model precondition classifies the biological specimen as to the presence or absence of a disease state or drug respondent state, and wherein the selected model refines the classification of said biological specimen by said model precondition to a more specific disease state or drug respondent state; each model in said plurality of models comprising a plurality of calculations, a calculation algorithm, and an aggregation algorithm; wherein each calculation in each said plurality of calculations specifies an identity of one or more cellular constituents in said biological specimen to be used in a calculation algorithm; wherein satisfaction of the model precondition for the selected model requires another model, when tested, in the plurality of models to have a specific predetermined result that is the presence of a particular disease state or a particular drug respondent state with which that another model is associated; wherein said determining comprises testing the another model by: (1) choosing a calculation from the plurality of calculations in the another model; (2) obtaining a cellular constituent abundance value for each of said one or more cellular constituents specified by said calculation from said biological specimen; (3) applying the calculation algorithm of the another model to the one or more cellular constituent abundance values obtained from the biological specimen; (4) repeating steps (1) through (3) for one or more other calculations in the another model; and (5) aggregating each calculation computed in step (3) for the another model in accordance with the aggregation algorithm in the another model; thereby characterizing the another model with respect to the disease state or drug respondent state associated with the another model, and when the model precondition for the selected model has not been satisfied, the selected model is not used; and when the model precondition for the selected model has been satisfied, the method further comprises automatically passing process control to a method that tests the selected model by: (i) choosing a calculation from the plurality of calculations in the selected model; (ii) obtaining a cellular constituent abundance value for each of said one or more cellular constituents specified by said calculation from said biological specimen; (iii) applying the calculation algorithm of the selected model to the one or more cellular constituent abundance values obtained from the biological specimen; (iv) repeating steps (i) through (iii) for one or more other calculations in the selected model; and (v) aggregating each calculation computed in step (iii) for the selected model in accordance with the aggregation algorithm in the selected model; thereby characterizing the selected model with respect to a disease state or drug respondent state that is more specific than the disease state or drug respondent state associated with the another model; and (B) performing said steps (i) through (v) with at least one other model in the plurality of models, thereby classifying the biological specimen, wherein said determining step (A) and said performing step (B) are executed using a suitably programmed computer.