Model Description Language (MDL): A Standard for Modeling and Simulation

MOTIVATION Models are now used not just for data analysis, but for knowledge representation integrating across a wide range of data sources and model types. MDL provides the means to describe models in a clear and consistent manner for modelers and those using the models, and, together with the other DDMoRe exchange standards—Pharmacometrics Markup Language (PharmML), which defines the XML‐based software interchange standard; probability distribution ontology and knowledge‐base (ProbOnto), which provides a consistent basis for definition of probability distributions across MDL and PharmML and how these distributions are encoded in various target software tools; and the Standard Output (SO) definition, which defines a consistent XML representation of output from target software tools—provides standards for model definition, software input, output and interoperability, and knowledge management through metadata annotation using suitable ontologies. Estimation X Initial Values X MLX task properties Bayesian estimation X X X BUGS task properties Visual Predictive Check X Estimated Parameters X NONMEM task properties Prediction/simulation (X) X Estimated Parameters X simulx task properties Optimal design/evaluation X Estimated Parameters X PFIM or PopED task properties 1The Task Properties Object contains settings for the specific modeling and simulation task relevant to the target software tool for that task. 2For prediction or simulation, a Data Object can be used as an alternative to the Design Object. MDL AS A COMMUNICATION TOOL Without clear communication of all aspects of the model, including structural form, model hierarchy, distributional properties of random variables, covariate relationships, mathematical and statistical aspects there is little hope of accurately conveying knowledge imbued within the model.