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Introductory Survey of MML

This page is for the older JSim version 1.6. Click here for the latest JSim 2.0 version.

MML (for "Mathematical Modeling Language") is a concise, ASCII text description of model calculations that is used by JSim. This document is an introductory survey of MML, with pointers to further information.



A Simple MML Model

Below is a simple model using trig functions:

(Java plugin required)

Items of note:

Ordinary Differential Equations (ODEs)

ODEs are used in research and education for models ranging from very simple to exceedingly complex. A simple MML model of exponential decay using an ODE is as follows:

(Java plugin required)

To be solvable by JSim, an ODE variable must have two constraints:

  1. an initial condition (IC) that sets the initial value of the variable using the "when" clause;
  2. an ODE state equation relating the variable's derivative to the variable itself or other model varibles.

Note that MML uses the colon (:) operator to represent differentiation. See Using ODEs in MML for more information.

Physical Units

MML allows (but does not require) defining physical units (e.g. m/sec^2) for variables. Along with making the model easier to read, this allows JSim to perform integrity checks that catch common errors, thus reducing debugging time. Two related issues are involved:

  1. Inserting conversion factors. For example, when adding centimeters to millimeters, one variable must be adjusted by a factor of 10;
  2. Detecting illegal constructs. For example, adding meters/second to meters/second^2 is fundamentally incorrect.

When JSim compiles a model containing the optional "unit correction on" clause, appropriate conversion factors are inserted into constructs of the 1st sort, while constructs of the 2nd sort generate a compilation error. Consider the model:

(Java plugin required)

Items of note:

Driving Data and Test Signals

Many scientific analyses require driving model inputs with experimentally measured data or idealized external test signals. JSim's approach is to provide these at run-time, rather than to encode them directly in MML. This allows easy switching back and forth between various driving functions at run-time. In MML, the "extern" declaration is used to label variables that will be supplied externally via the run-time environment. The following MML models the concentration in a stirred tank whose inflow is supplied externally:

(Java plugin required)

Parameters and Parameter Sets

When JSim compiles an MML model, it classifies variables as either "input" or "output":

JSim model "parameters" are values that affect model calculations that the user may alter without model recompilation. There are 3 types of parameters:

  1. MML variables classified as "inputs" above;
  2. numeric solver controls, e.g. which ODE solver algorithm to use (more info);
  3. function generator controls, e.g. the amplitude of a test signal (more info).

A collection of values for all the parameters in a JSim model is called a "parameter set". The values generated during a model run reflect both the model's MML code (which provides default parameter values) and the model's "current parameter set". The JSim GUI allows users to save and restore distinct parameter sets for modeling varying experimental conditions (e.g. "baseline", "elevated heart rate"). See JSim Parameter Sets for further info.

New User Warning: Values in the current parameter set are preserved between compiles. This usually saves modelers work, because most editing is the fine-tuning of model calculations, however it can cause a confusing scenario for new users. When an MML model is first compiled, the default values (e.g. V=20 in the above model) are loaded into the current parameter set. If you were to modify the MML to V=25 and recompile, the value shown in the JSim GUI after recompilation would still be 20, a value restored from the current parameter set. The compilation has altered V's default value to 25 (which you can see by selecting "Revert to model defaults" from the ParSet menu), but not its current value. This behaviour is somewhat controversial and is further discussed here.

Understanding Constraints

MML is a declarative language, not a procedural one. MML models primarily consist of

The most common type of constraints are mathematical equations. Consider the following:

(Java plugin required)


"extern" is another type of constraint. Substituting the following for eqn 3 in the above model would require the JSim user to specify a value for "a" before the model can run:

extern a;

Understanding constraints is important because JSim requires all model variables to be completely constrained, that is, it ensures there is no ambiguity in the model calculations. If any one of the 3 equations in the above model were omitted, compilation would abort with a "model underconstrained" error message. Alternatively, adding a 4th equation such as

b=6  // eqn 4

would cause compilation abort with a "model overconstrained" error message. This may seem needlessly picky, but it is actually an advantage. This approach guarantees that model equations are complete and consistent, ultimately clarifying thinking, reducing debugging and improving scientific communication.

For simple, equation-based models, a fully constrained model will have as many variables as equations. The current JSim compiler is not smart enough to detect consistent systems with more equations that variables, as might occur in the above model with the added relation "b=4". In this case, the compile would abort with a "model overconstrained" error message. Modelers can avoid this situation by matching the number of equations with the number of variables to be solved.

Some exceptions to the #variables = #constraints include the following (follow links for details):

  1. ODE variables require 2 equations - an initial condition and a state equation;
  2. 1D PDE variables (more info)require 4 equations - an initial condition, two boundary conditions and a state equation;
  3. Event variables (more info)require an initial condition and one or more event statements;
  4. MML procedures (more info) may constrain multiple variables with a single statment.

By framing models in terms of constraints, MML can easily intermix mathematics (explicit & implicit equations, ODEs, PDEs), discrete events and procedural code (e.g. Java, C, Fortran). This allows modelers to naturally mix computational methods as required by the science, rather than by shoehorning all model calculations into a single computational framework.

Further Topics

Most MML modeler writers will need to be familiar with the previous topics. Other features of MML are usually best learned as need arises, to wit:

[This page was last modified 02Feb11, 3:22 pm.]

Model development and archiving support at provided by the following grants: NIH/NIBIB BE08407 Software Integration, JSim and SBW 6/1/09-5/31/13; NIH/NHLBI T15 HL88516-01 Modeling for Heart, Lung and Blood: From Cell to Organ, 4/1/07-3/31/11; NSF BES-0506477 Adaptive Multi-Scale Model Simulation, 8/15/05-7/31/08; NIH/NHLBI R01 HL073598 Core 3: 3D Imaging and Computer Modeling of the Respiratory Tract, 9/1/04-8/31/09; as well as prior support from NIH/NCRR P41 RR01243 Simulation Resource in Circulatory Mass Transport and Exchange, 12/1/1980-11/30/01 and NIH/NIBIB R01 EB001973 JSim: A Simulation Analysis Platform, 3/1/02-2/28/07.