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GNU MCSim manual, version 5.6.6
GNU MCSim is a general purpose modeling and simulation program which can performs "standard" or "Markov chain" Monte Carlo simulations. It allows you to specify a set of linear or nonlinear algebraic equations or ordinary differential equations. They are solved numerically using parameter values you choose or parameter values sampled from statistical distributions. Simulation outputs can be compared to experimental data for Bayesian parameter estimation (model calibration).
Reference Manual | ||
---|---|---|
1. Software and Documentation Licenses | GNU MCSim is under GNU General Public License | |
2. Overview | the gist of it | |
3. Installation | for GNU/Linux, Unix and other platforms | |
4. Working Through an Example | highly recommended! | |
5. Setting-up Structural Models | use and syntax of model definition files | |
6. Running Simulations | syntax of simulation specification files | |
7. Common Pitfalls | errors you will make one day or the other... | |
8. XMCSim | a graphical user’s interface to GNU MCSim | |
Bibliographic References | examples of applications | |
Appendices | ||
A. Keywords List | a list of the reserved keywords | |
B. Examples | examples of models and input files | |
Index Table | ||
Concept Index |
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GNU MCSim is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
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GNU MCSim is a simulation and statistical inference tool for algebraic or differential equation systems. Other programs have been created to the same end, the Matlab family of graphical interactive programs being some of the more general and easy to use. Still, many available tools are not optimal for performing computer intensive and sophisticated Monte Carlo analyses. GNU MCSim was created specifically to this end: to perform Monte Carlo analyses in an optimized, and easy to maintain environment. The software consists in two pieces, a model generator and a simulation engine:
- The model generator, "mod", was created to facilitate
structural model definition and maintenance, while keeping execution
time short. You code your model using a simplified syntax and
mod
translates it in C.
- The simulation engine is a set of routines are linked to your model to produce executable code. After linking, you will be able to run simulations of your structural model under a variety of conditions, specify an associated statistical model, and perform Monte Carlo simulations.
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Model building and simulation proceeds in four stages:
emacs
) a model
description file. The reference section on mod
, later in this
manual gives you the syntax to use (see section Setting-up Structural Models). This syntax allows you to describe the model variables,
parameters, equations, inputs and outputs in a C-like fashion without
having you to actually know how to write a C program.
mod
, to preprocess your
structural model description file. Mod
creates a C file, called
‘model.c’.
gcc
. After compiling and linking, an
executable simulation program is created, specific of your particular
model. These preprocessing and compilation steps can be performed in
Unix with a single shell command makemcsim
(in which case, the
‘model.c’ is created only temporarily and erased afterward). This
produces the most efficient code for your particular machine.
mcsim
program. These simulation files
describe the kind of simulation to run (simple simulations, Monte
Carlo etc.), various settings for the integration algorithm if needed,
and a description of one or several simulation conditions (eventually
with a statistical model and data to fit) (see section Running Simulations). The simulation output is written to standard ASCII
files.
Little or no knowledge of computer programming is required, unless you want to tailor the program to special needs, beyond what is described in this manual (in which case you may want to contact us).
Under Unix, a graphical user interface written in Tcl/Tk, XMCSim
(called by the command xmcsim
), is also provided. This
menu-driven interface automatizes the compilation and running tasks. It
also offers a convenient interface to 2-D and 3-D plotting of the
simulation results.
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Five types of simulations are available:
MonteCarlo()
specification).
MCMC()
specification). In MCMC simulations the random choice of
a new parameter value is influenced by the current value. They can be
used to obtain the Bayesian posterior distribution of the model
parameters, given a statistical model, prior parameter distributions
(that you need to specify) and data for which a likelihood function can
be computed. The program handles hierarchical (e.g., random effects
and mixed effects) statistical models (see section Setting-up statistical models).
SetPoints()
specification). You can create these parameter
sets yourself (on a regular grid, for example) or use the output of a
previous Monte Carlo or MCMC simulation.
OptimalDesign()
specification).
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autoconf
, automake
and libtool
. This
should make GNU MCSim easier to install and more
portable.
MCMC()
specification.
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autoconf
version 2.69 which fixes a potential security problem in the
installation. That should be transparent to the user.
mod
utility can now generate C model files suitable for use
with the R package deSolve
. Use mod -R
for that.
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End
is now mandatory at the end of every
model. This is not backward compatible (you will need to modify your
older models accordingly) but we had to implement this for technical
reasons.
StartTime()
specification, setting the initial time for
integration can now accept a symbolic parameter. That allows you to
treat the initial time as a random variable in error-in-variable
problems (when the initial time is an unknown).
PrintStep()
specification can read a list of variables to
print (as Print()
does).
CalDelay
function.
Events()
specification.
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GNU MCSim is written in ANSI-standard C language. We are distributing the source code and you should be able to compile it for any system, provided you have an ANSI C compliant compiler.
Starting with version 5.0.0 GNU MCSim is using a few routines
from the GNU Scientific Library (libgsl
). We recommend that you
install version 1.5 (or higher) of the shared GSL library, gslcblas
library, and GSL include files on your system. Otherwise a few obscure
features will not be available (you’ll get a error message if you are
trying to use them.)
Version 5.4.0 and higher of GNU MCSim can take advantage of
(libSBML
) to read SBML models. If you choose to install libSBML
on your system, we recommend that you use version 3.3.2 (or higher) of
libSBML. LibSBML needs an XML parser library (either Expat, Xerces, or
libxml2). The Expat library has worked well for us under Linux.
On any system we recommend the GNU gcc
compiler (freeware). The
automated installation script checks for the availability on your
system of the tools needed for compilation and proper running of the
software. It should warn you of missing component and eventually adapt
the installation to your needs (for example by installing the package
locally if you do not have superuser’s priviledges).
To run the graphical user interface XMCsim, you need a GNU/Linux or Unix system with "XWindows", "Tcl/Tk" and "wish" installed.
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GNU MCSim source code is available on Internet through:
- http://savannah.gnu.org/projects/mcsim.
Packaged distributions are available at:
- http://ftp.gnu.org/gnu/mcsim,
- http://www.gnu.org/software/mcsim,
and mirror sites of the GNU project.
Three mailing lists are available for GNU MCSim users:
General info on GNU MCSim is broadcasted through:
- http://lists.gnu.org/archive/html/info-mcsim
You can subscribe to the info list by going to:
- http://lists.gnu.org/mailman/listinfo/info-mcsim.
You can request help from us, and from other GNU MCSim users, by sending email to:
(see http://lists.gnu.org/mailman/listinfo/help-mcsim for subscribing).
Help archives are found at:
- http://news.gmane.org/gmane.comp.gnu.mcsim,
- http://lists.gnu.org/archive/html/help-mcsim.
You can report bugs to us, by sending email to:
(http://lists.gnu.org/mailman/listinfo/bug-mcsim for subscribing).
Bugs archives are located at:
- http://news.gmane.org/gmane.comp.gnu.mcsim.bugs,
- http://lists.gnu.org/archive/html/bug-mcsim.
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To install on a Unix or GNU/Linux machine, download (in binary mode)
the distributed archive file to your machine. Place it in a directory
where there is no existing ‘mcsim’ subdirectory that could be
erased (make sure you check that). Decompress the archive with GNU
gunzip (gunzip <archive-name>.tar.gz
). Untar the decompressed
archive with tar (tar xf <archive-name>.tar
) (do man tar
for further help). Move to the ‘mcsim’ directory just created and
issue the following commands:
./configure make make check |
The first command above checks for the availability of the tools needed
for installation and proper running of the software. The second compiles
the mod
program and the dynamic libmcsim.so
library and
eventually compiles this manual in various formats. The third checks
whether the software is running and producing meaningful results in test
cases. In case of error messages, don’t panic: check the actual
differences between the culprit output file and the file ‘sim.out’
produced by the checking. Small differences may occur from different
machine precision. This can happen for random numbers, in which case the
Markov chain simulations (MCMC) can diverge greatly after a
while.
If you are logged in as "root" or have sufficient access rights, you can then install the software in common directories in ‘/usr’ by typing at the shell prompt:
make install |
If this system-wide installation is successful the executable files
mod
, makemcsim
, xmcsim
are installed in
‘/usr/local/bin’. The library libmcsim
is placed in
‘/usr/local/lib’. A copy of the ‘mcsim’ source directory
(with the ‘mod’, ‘sim’, ‘doc’, ‘samples’, and
‘xmcsim’ subdirectories) is placed in ‘/usr/local/share’. If
you have the GNU info
system available, an mcsim
node is
added to the main info
menu, so that info mcsim
will
show you this manual. Finally, a symbolic link to
‘/usr/local/share/mcsim/doc’, which contains the documentation
files and this manual (if it was generated), is created as
‘/usr/share/doc/mcsim’.
If you do not have the necessary access rights and want to install GNU MCSim in a directory such as ‘/home/me’, type:
./configure prefix=/home/me |
This will copy or move ‘mod’, ‘makemcsim’, and ‘xmcsim’
in a ‘/bin’ directory in the ‘/home/me’ directory, creating
it if necessary. The library libmcsim.so
will be moved to the
‘/home/me/lib’ directory, etc.
On certain platforms (Linux...), you will also need to do one of
the following:
1) run ’ldconfig’ (see the man page if this is unfamiliar)
2) set the LD_LIBRARY_PATH (or equivalent) environment variable to
contain the path "/usr/local/lib" or whatever you set so that programs can
find the libSBML library at run-time.
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Under other operating systems (Windows, etc.) or if everything else
fails you should be able to both uncompress and untar the archive with
widely distributed archiving tools. Refer to the documentation of your C
compiler to create an executable ‘mod’ file from the source code
files (getopt.c, lex.c, lexerr.c, lexfn.c, mod.c, modd.c, modi.c,
modiSBML.c, modiSBML2.c, modo.c, strutil.c) provided in the ‘mod’
directory. If you want to process SBML models it is best to install the
libSBML library first. You would then compile mod with the HAVE_LIBSBML
flag defined (option -DHAVE_LIBSBML
) and link with the library
(using the -lsbml
directive). Place then the executable
‘mod’ on your command path.
The ‘sim’ directory contains all the source files (delays.c,
getopt.c, lex.c, lexerr.c, lexfn.c, list.c, lsodes1.c, lsodes2.c,
matutil.c, matutilo.c, mh.c, modelu.c, optdsign.c, random.c, sim.c,
simi.c, siminit.c, simmonte.c, simo.c, strutil.c, yourcode.c) to create
a dynamic library or a set of objects to link with the ‘model.c’
files generated by mod
after processing your models. Compilation
also requires reference to the ‘config.h’ file sitting in the main
folder (one level above the ‘sim’ directory). The -I.. option
should make the compiler aware of the correct location of
‘config.h’. Alternatively, ‘config.h’ can be copied into the
‘sim’ directory to make the package complete (apart of model.c).
The final product should be an executable able to run your
model. Linking with the GNU Scientific Library (gsl
) is
recommended (but not mandatory. In that case, define the
HAVE_LIBGSL
flag and link with the -lgsl
and
-lgslcblas
(in that order!).
You are now ready to use GNU MCSim. We recommend that you go through the next section of this manual, which walks you through an example of model building and running.
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Several models and simulation specification files are provided with the package as examples (they are in the ‘samples’ directory. You can try any of them. The linear regression model is particularly simple, but to be more complete we will try here a nonlinear implicit model, specified through differential equations.
Pharmacokinetics models describe the transport and transformation of chemical compounds in the body. These models often include nonlinear first-order differential equations. The following example is taken from our own work on the kinetics of tetrachloroethylene (a solvent) in the human body (Bois et al., 1996; Bois et al., 1990) (see section Bibliographic References).
Go to the ‘mcsim/samples/perc’ directory (installed either
locally or by default in ‘usr/share’ under Unix or
GNU/Linux). Open the file ‘perc.model’ with any text editor
(e.g., emacs
or vi
under Unix). This file is an
example of a model definition file. It is also printed at in Appendix
the end of this manual (see section ‘perc.model’: A sample model description file). You can use it as a
template for your own model, but you should leave it unchanged for
now. In that file, the pound signs (#) indicate the start of
comments. Notice that the file defines:
States = {Q_fat, # Quantity of PERC in the fat (mg) Q_wp, # ... in the well-perfused compartment (mg) Q_pp, # ... in the poorly-perfused compartment (mg) Q_liv, # ... in the liver (mg) Q_exh, # ... exhaled (mg) Q_met} # Quantity of metabolite formed (mg) |
Outputs = {C_liv, # mg/l in the liver C_alv, # ... in the alveolar air C_exh, # ... in the exhaled air C_ven, # ... in the venous blood Pct_metabolized, # % of the dose metabolized C_exh_ug} # ug/l in the exhaled air |
Inputs = {C_inh, # Concentration inhaled (ppm) Q_ing}; # Quantity ingested (mg) |
LeanBodyWt = 55; # lean body weight (kg) |
This model definition file as a simple syntax, easy to master. It
needs to be turned into a C program file before compilation and
linking to the other routines (integration, file management etc.) of
GNU MCSim. You will use mod
for that. First, quit the editor
and return to the operating system.
To start mod
under Unix just type mod perc.model
. After a
few seconds, with no error messages if the model definition is
syntactically correct, mod
announces that the ‘model.c’ file
has been created. It should operate similarly under other operating
systems.
The next step is to compile and link together the various C files that will constitute the simulation program for your particular model. Note that each time you want to change an equation in your model you will have to change the model definition file and repeat the steps above. However, changing just parameter values or state initial values does not require recompilation since that can be done through simulation specification files.
makemcsim
script. Just
type makemcsim
and compilation will be done automatically
(see section Using makemcsim
to fully process model files). An executable ‘mcsim.perc’ is
created. You can rename it if you wish.
make
or
its equivalent to compile and link together the ‘model.c’ file
created by mod
and the other C files of the ‘sim’ directory
(see section Installation). That should create an application (you should
give it a name specific to the model you are developing, e.g.,
‘mcsim.perc’). Refer to your compiler manual for details on how to
use your programming environment. Your executable ‘mcsim.perc’
program is now ready to perform simulations.
To start your GNU MCSim program just type mcsim.perc
(if you
gave it that name) under Unix. After an introductory banner (telling
in particular which model file the program has been compiled with),
you are prompted for an input file name: type in perc.lsodes.in
(see section ‘perc.lsodes.in’, to see this file in Appendix), then a space,
and then type in the output file name: perc.lsodes.out. After a
few seconds or less (depending on your machine) the program announces
that it has finished and that the output file is
‘perc.lsodes.out’. You can open the output file with any text
editor or word processor, you can edit it for input in graphic
programs etc.
You can try the various demonstration models provided in the ‘samples’ directory and observe the output you obtain. You can then start programming you own models and doing simulations. The next sections of this manual reference the syntax for model definition and simulation specifications.
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The model generator, "mod", was created to facilitate structural
model definition and maintenance, while keeping short execution time
through compilation. This chapter explains how to use mod
, and
how to code your models using a simplified syntax that mod
can
translate in C (creating thereby a ‘model.c’ file).
After compiling and linking of the newly created ‘model.c’ file
together with the other C files of the ‘mcsim/sim’ directory (or
after linking with a dynamic library ‘libmcsim.so’), an executable
simulation program is created, specific of your particular model. These
preprocessing and compilation steps can be performed in Unix with a
single shell command makemcsim
(in which case, the ‘model.c’
is created only temporarily and erased after that).
Several examples of model simulation files are included in the ‘mcsim/samples’ directory. Some of them are reproduced in Appendix (see section Examples).
5.1 Using mod to preprocess model description files | how to process a model definition file | |
5.2 Using makemcsim to fully process model files | a simple command to preprocess and compile a model | |
5.3 Syntax of the model description file | how to write a model definition file | |
5.4 Reading SBML models and applying a template | how to specify a list of SBML models and template model | |
5.5 Working with the R package deSolve | how to use an MCSim model compiler for R deSolve |
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mod
to preprocess model description filesThe mod
program is a stand-alone facility. It takes a model
description file in the "user-friendly" format described below
(see section Syntax of the model description file) and creates a C language file
‘model.c’ which you will compile and link to produce the
simulation program. Mod
allows the user to define equations for
the model, assign default values to parameters or default initial
values to model variables, and to initialize them using additional
algebraic equations. Mod
lets the user create and modify models
without having to maintain C code. Under Unix or GNU/Linux, the
command line syntax for the mod program is:
mod [input-file [output-file]] |
where the brackets indicate that the input and output filenames are optional. If the input filename is not specified, the program will prompt for both. If only the input filename is specified, the output is written by default to the file ‘model.c’. Unless you feel like doing some makefile programming, we recommend using this default since the makefile for GNU MCSim assumes the C language model file to have this name. You have to have prepared a text file containing a description of the model following the syntax described in the following (see section Syntax of the model description file).
The following options are available:
-h, -H gives a short online help.
-R generate a C file of the format requested for use by the
deSolve
package of the R
software for statistical
analysis; deSolve
implements differential equations solvers
with interesting capabilities.
Most error messages given by mod
are self-explanatory. Where
appropriate, they also give the line number in the model file where
the error occurred. Beware, however, of cascades of errors generated
as a consequence of a first one; so don’t panic: start by fixing the
first one and rerun mod
. Note that when using the -R option,
care has to be taken to adopt the deSolve
code conventions (see
the deSolve
manual on R CRAN). If you get really stuck you can
send a message to the help mailing list (see section Installation) or to
the authors of this manual.
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makemcsim
to fully process model filesmakemcsim
is a Unix sh
shell script that further
facilitates preprocessing and compilation. You run makemcsim
by entering it at the command prompt:
makemcsim [model-file] |
where the brackets indicate that the model filename is optional. If a
model filename is not specified, the first file having extension
‘.model’ (by alphabetical order) is used. Makemcsim calls
mod
if the model file has changed since last compilation,
compiles the ‘model.c’ generated, links it to the shared
‘libmcsim.so’ library to create an executable
‘mcsim.<root-model-name>’. The extension ‘root-model-name’
corresponds to your model filename (without its last extension if it has
one; i.e.,typically, without the ‘.model’ extension). The
‘model.c’ file is deleted afterward; if you want to inspect it (for
example, if you got error messages from mod
), run mod
on
your model definition file.
Two variants of makemcsim
are also available: makemcsims
,
which creates a standalone version (no dynamic libraries needed), and
makemcsimd
, which creates a standalone version with debugging
symbols included (so that you can use gdb, for example, to check what
the code does).
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The model description file is a text (ASCII) file that consists of several sections, including global declarations, dynamics specifications (with derivative calculations), model initialization ("scaling"), and output computations. Here is a template for such a file (for further examples see section Examples):
# Model description file (this is a comment) <Global variable specifications> Initialize { <Equations for initializing or scaling model parameters> } Dynamics { <Equations for computing derivatives of the state variables> } Jacobian { <Equation for the Jacobian of the state derivatives> } CalcOutputs { <Equations for computing output variables> } End. # mandatory ending keyword |
Initialize
, Dynamics
, Jacobian
and
CalcOutputs
are reserved keywords and, if used, must appear as
shown, followed by the curly braces which delimit each section
(see section Model initialization; Dynamics section; Output calculations). Please note that at least one of the sections
Dynamics
or CalcOutputs
should be defined, and that
Dynamics
must be used if the model includes differential
equations. Finally the model definition file must have the End
keyword at the beggining of a line, eventually preceeded by white spaces
or tabs. Text after the End
keyword is ignored.
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The general syntax of the model description file is as follows:
<variable-name> '=' <constant-value-or-expression> ';' |
The equal sign is needed. The right-hand side expression can be a valid C mathematical expression including numerical constants, already defined variables, standard ANSI C mathematical functions (see https://en.wikipedia.org/wiki/C_mathematical_functions), and GNU MCSim’s "special functions" (see section Special functions) or "input functions" (see section Input functions). Special functions can take already defined variables, constant values or expressions as parameters. Input functions can only be used on the right hand side of assignments to input variables.
Colon conditional assignments have the following syntax:
<variable-name> = (<test> ? <value-if-true> : <value-if-false>); |
For example:
Adjusted_Param = (Input_Var > 0.0 ? Param * 1.1 : Param); |
In this example, if ‘Input_Var’ is greater than 0, the parameter
‘Adjusted_Param’ is computed as the product of ‘Param’ by
‘1.1’; otherwise ‘Adjusted_Param’ is equal to
‘Param’. Note that conditional assignments can be nested (i.e.,
<value-if-true> or <value-if-false> can themselves be a conditional
expression). The comparison operators allowed are the equality operator
==
, and non-equality operators !=
, <
, >
,
<>
, <=
and >=
.
More complex conditions can also be specified, but the Boolean AND, OR and NOT operations have not yet been implemented. You can use:
(('A'*'B')>0)
for AND
(('A'+'B')>0)
for OR
('A'==0)
for NOT
Vectors’ declaration: To declare a variable as a vector use the one of the two following syntaxes when you first define it:
<variable-name> '[' <integer> ']' <variable-name> '[' <integer> '-' <integer> ']' |
The variable name is immediately followed by an opening square bracket (’[’). The array index bounds (which define the valid indices) can be given as (long) positive or null integers separated by an hyphen (’-’) (spaces are allowed). In this case the second integer must be higher the first. They are followed by a closing bracket (’]’). The hyphen and second integer are optional. If only one bound (integer) is given, only the component with corresponding index is declared. Both syntaxes can be mixed. For example:
States = {y[0-9]}; alpha[0-2] = 1; beta[0] = 1; beta[1] = 2; beta[2-4]; |
The previous lines define a state variable ‘y’ as a vector of length 10, with valid indices ranging between 0 and 9, included. The parameter vector ‘alpha’ is defined with range 0 to 2, each component being initialized to value 1. For parameter ‘beta’, components 0, 1 and 2 to 4 are initialized separately (components 2 to 4 are initialized with default value 0).
Accessing vectors’ components: After declaration, vector’s components can be accessed individually using the square bracket syntax:
<variable-name> '[' <integer> ']' |
For example:
Outputs = {x[0-1]}; beta[0] = 0; beta[1] = beta[0] + 1; CalcOutputs { x[0] = beta[0] * t; x[1] = beta[1] * t; } |
In the above example, ‘beta[0]’, ‘beta[1]’, ‘x[0]’, and ‘x[1]’ are accessed individually. The variable ‘t’ refers to the implicit variable ’time’.
Vectorization of equations: The equations specifying the model,
which consist in assignments, can be vectorized in the
Initialize
, Dynamics
and CalcOutputs
sections
(but not in the global section) (see section Global variable declarations). Vectorization allows you to specify an operation for an
entire vector or parts of it. The following syntax should be
used:
<var-name>'['<integer>'-'<integer>']' = <vectorized-expression>; |
On the right-hand side, the vectorized expression should be a valid C mathematical expression including numerical constants, already defined state, input, output, other (parameter) variables or vectors, and standard ANSI C mathematical functions or special functions (see section Special functions). Here also, input functions (see section Input functions) can only be used on the right hand side of assignments to input variables. Vector indices on the right-hand side can take the special form of "bracketed expressions". Bracketed expressions can be composed of integers, the 4 basic arithmetic operators (’+’, ’-’, ’*’, ’/’), parentheses and the index letter ’i’. The running index ’i’ points in turn to each component in the range specified on the left-hand side (imagine that the range given on the left-hand side corresponds to a ’for’ loop with index ’i’ running from the lower bound to the upper bound). This is best understood by looking at some code. In the previous example, the assignments to x[0] and x[1] obviously deserve vectorization. This is achieved by the following statements:
CalcOutputs { x[0-1] = beta[i] * t; } |
Here, the index ’i’ refers to the values 0 and 1. Here is another example:
Outputs{x[1-10]}; CalcOutputs { x[1] = 0; x[2-10] = x[i-1] + 1; } |
This is equivalent to:
Outputs{x[1-10]}; CalcOutputs { x[1] = 0; x[2] = x[1] + 1; ... x[10] = x[9] + 1; } |
and will assign value 1 to ‘x[2]’, 2 to ‘x[3]’, etc. On the right-hand side, more complicated bracketed expressions like ‘[(2*i-1)/(i+3)]’ can be used. Another, working, example of vector use is given in the ‘mcsim/samples/pde2’ directory.
Alternative ’underscore’ (’_’) syntax: Individual vector components can be declared and used (everywhere in the model file) with the following syntax:
<variable-name>'_'<integer> |
The integer indicates which component of the vector is referred to. For example ‘x_1’ is strictly equivalent to ‘x[1]’. Note!: No space are allowed between the variable name, the underscore and the integer. Note also!: This syntax is the only one that can be used in simulation specification files. If you declare a parameter ‘beta[1-10]’ in your model definition file, the only way to refer to it in the simulation input file will be through its individual components ‘beta_1’, ‘beta_2’, ... etc. This limitation will be removed in a future release of the software.
End
keyword must used to indicate model termination.
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Commands not specified within the delimiting braces of another section are considered to be global declarations. In the global section, you first declare the state, input, and output variables. There should be at least one state or output variable in your model.
Dynamics
section (see section Dynamics section)
(higher orders or partial differential equations are not
allowed).
Dynamics
or CalcOutputs
sections.
The format for declaring each of these variables is the same, and
consists of the keyword States
, Inputs
or Outputs
followed by an equal sign and a list of the variable names enclosed in
curly braces as shown here:
States = {Qb_fat, # Benzene in the fat Qb_bm, # ... in the bone marrow Qb_liv}; # ... in the liver and others Inputs = {Q_gav, # Gavage dose C_inh}; # Inhalation concentration Outputs = {Cb_exp, # Concentration in expired air Cb_ven}; # ... in venous blood |
After being defined, states, inputs and outputs can then be given initial values (constants or expressions). Inputs can also be assigned input functions, described below (see section Input functions). Some examples of initialization are shown here:
Qb = 0.1; # Default initial value for state variable Qb # Input variable assigned a periodic exponential input function Q = PerExp(1, 60, 0, 1); # Magnitude of 1.0, # period of 60 time units, # T0 in period is 0, # Rate constant is 1.0 |
If a state, input, or output variable is not explicitly given an initial
value, that value will be set to zero by default. Initial values are
reset to their specified value by the simulation program at the start of
each Simulation
(see section Simulation
sections).
All the other variables are "parameters". Model parameters you want to be able to change in simulation input files should be declared in the global section. For example:
Wind_speed; # (m/s) Local wind speed |
Parameters are by default assigned a value of zero. To assign a different nominal values, use the assignment rules given above. For example:
BodyWt = 65.0 + sqrt(15.0); # Weight of the subject (in kg) |
All parameters and variables are computed in double precision
floating-point format. Initial values should not be such as to cause
computation errors in the model equations; this is likely to lead to
crashing of the program (so, for example, do not assign a default
value of zero to a parameter appearing alone in a denominator). Note
that the order of global declarations matters within the global
section itself (i.e., parameters and variables should be defined
and initialized before being used in assignments of others), but not
with respect to other blocks. A parameter defined at the end of the
description file can be used in the Dynamics
section which may
appear at the beginning of the file. Still, such an inverse order
should be avoided. For this reason, the format above, where global
declarations come first, is strongly suggested to avoid confusing
results. Note again that the name IFN
, in capital letters, is
reserved by the program and should not be used as parameter or
variable name. Finally, if a parameter is defined several times, only
the first definition is taken into account (a warning is issued, but
beware of it).
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This section deals with structural models. Statistical models that you setup for model calibration and data analysis are defined in the simulation input files, through statistical distribution functions. They are dealt with later in this manual (see section Setting-up statistical models).
GNU MCSim can easily deal with purely algebraic structural
models. You do not need to define state variables or a Dynamics
section for such models. Simply use input and output variables and
parameters and specify the model in the CalcOutputs
section. You
can use the time variable t
if that is natural for your model. If
your model does not use t
, you will still need to specify "output
times" in Print()
or PrintStep()
statements to obtain
outputs: you can use arbitrary times. If you do not use t
as
"independent" model variable, you will also need do define a
Simulation
section (see section Simulation
sections) for each
combination of values for the independent variables of your model. This
may be clumsy if many values are to be used. In that case, you may want
to use the variable t
to represent something else than
time.
Ordinary differential models, with algebraic components, can be easily
setup with GNU MCSim. Use state variables and specify a
Dynamics
section. Time, t
is the integration variable,
but here again, t
can be used to represent anything you
want. For partial differential equations some problems might be solved
by implementing line methods (see examples in
‘mcsim/samples/pde1’ and ‘mcsim/samples/pde2’)...
You can use GNU MCSim for discrete-time dynamic models (or difference
models). That is a bit tricky. Assignments in the CalcOutputs
section are volatile (not memorized), so the model equations have to be
given in a Dynamics
section. But the model variables should still
be declared as outputs, because they should not be updated by
integration. However, you need at least one true differential equation
in the Dynamics
section, so you should declare a dummy state
variable (and assign to its derivative a constant value of zero). You
also want the calls to Dynamics
to be precisely scheduled, so it
is best to use the Euler
integration routine (see section Integrate()
specification) which uses a constant step. Since Euler
may call
repeatedly Dynamics
at any given time, you want to guard against
untimely updating... Altogether, we recommend that you examine the
sample files in the ‘mcsim/samples/discrete’ directory provided
with the source code for GNU MCSim.
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The following special functions (whose name is case-sensitive) are available to the user for assignment of parameters and variables in the model definition file:
BetaRandom(alpha, beta, a, b)
: returns a
Beta distributed variate on the interval [a,b] with shape
parameters alpha and beta;
BinomialBetaRandom(E, alpha, beta)
: return
random variate, of mathematical expectation E, drawn from a
binomial distribution with probability p, p being Beta
distributed with parameters alpha and beta;
BinomialRandom(p, N)
: returns a binomially
distributed random variate;
CauchyRandom(s)
: returns a Cauchy distributed
random variate with scale s;
CDFNormal(x)
: the normal cumulative density function;
Chi2Random(dof)
: returns a Chi-squared random variate with
dof degrees of freedom;
erfc(x)
: the complementary error function;
ExpRandom(beta)
: returns an exponential variate with
inverse scale beta;
GammaRandom(alpha)
: returns a gamma distributed random
variate with shape parameter alpha and inverse scale equal to
1;
GetSeed()
: returns the current value of the random generator
seed;
GGammaRandom(alpha, beta)
: returns a gamma
distributed random variate with shape parameter alpha and
inverse scale beta;
InvGGammaRandom(alpha, beta)
: returns an inverse
gamma distributed random variate with shape parameter alpha and
scale parameter beta;
lnDFNormal(x, mean, sd)
: the natural logarithm
of the normal density function;
lnGamma(x)
: the natural logarithm of the gamma function;
LogNormalRandom(mean, sd)
: returns a lognormally
distributed variate with geometric mean mean and geometric
standard deviation sd (i.e., the log of the returned variate
is normally distributed with mean
log(mean) and standard deviation log(sd));
LogUniformRandom(a, b)
: returns variate log-uniformly
distributed on the interval [a,b];
NormalRandom(mean, sd)
: returns a normally
distributed random variable with prescribed mean and standard
deviation;
PiecewiseRandom(min, a, b, max)
: the
distribution of the returned variate has the form of a truncated
triangle, with base from min to max and a plateau between
a and b. If
a = b,
the distribution is the triangular distribution;
PoissonRandom(mu)
: returns a Poisson-distributed random
variate, of rate mu;
SetSeed(seed)
: sets the current value of the pseudo-random
generator seed to the specified seed. That seed can be any
positive real number. Seeds between 1.0 and 2147483646.0 are used as is,
the others are rescaled within those bounds (and a warning is
issued);
StudentTRandom(dof, mean, sd)
:
returns a Student t distributed random variate with dof
degrees of freedom and given mean and standard deviation;
TruncInvGGammaRandom(alpha, beta, a, b)
:
returns a truncated inverse gamma distributed random variate with shape
parameter alpha and scale beta, in the range
[a,b]. Explicit specification of a,b is
required;
TruncLogNormalRandom(mean, sd, a, b)
:
returns a truncated lognormal variate with geometric mean mean and
geometric standard deviation sd, in the range
[a,b]. Explicit specification of a,b is
required;
TruncNormalRandom(mean, sd, a, b)
:
returns a truncated normal variate with prescribed mean and standard
deviation, in the range [a,b]. Explicit specification of
a,b is required;
UniformRandom(min, max)
: returns a uniformly
distributed random variable, sampled between min and max. The algorithm
used is that of Park and Miller (Barry, 1996; Park and Miller, 1988;
Vattulainen et al., 1994) (see section Bibliographic References). A default
random generator seed (314159265.3589793) is used.
Note: for all the above random number generating functions, a default
random generator seed is used. It can be changed with the function
SetSeed
. Note also that assignment of a random number
generating function to a state variable derivative will define a form
of stochastic differential equation. GNU MCSim’s integration
routines are not particularly suited to the resolution of such
equations. If you wish to try it anyway, you may want to consider
using the "robust" Euler method (see section Integrate()
specification).
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These functions can be used in special assignments, valid only for input variables. Inputs can be initialized to a constant or to a standard mathematical expression, or assigned one of the following input functions:
PerDose()
specifies a periodic input of constant
<magnitude>. The input begins at <initial-time> in the
<period> and lasts for <exposure-time> time
units. Syntax:
<input variable> = PerDose(<magnitude>, <period>, <initial-time>, <exposure-time>); |
PerExp()
specifies a periodic exponential input. At time
<initial-time> in the <period> the input rises
instantaneously to <magnitude> and begins to decay exponentially
with the constant <decay-constant>. The input is turned off once
the magnitude reaches a negligible fraction
of its original value. Note that the input does not accumulate across
periods, it resets at each period start. Syntax:
<input variable> = PerExp(<magnitude>, <period>, <initial-time>, <decay-constant>); |
NDoses()
specifies a number of stepwise inputs of variable
magnitude and their starting times. The first argument, <n>, is
the number of input steps and start times. Next come a list of
magnitudes and a list of corresponding initial times. Each list is
comma-separated. The duration of each input step is computed
automatically by difference between the listed times. Currently this
function can only be used in the simulation description file, and not
in the model description file (which simply implies that you cannot
use it as a default). Syntax:
<input variable> = NDoses(<n>, <list-of-magnitudes>, <list-of-initial-times>); |
Note that the list of times must begin at the starting time of the simulation (typically time zero), even if the magnitude at that first time is zero.
Spikes()
specifies a number of instantaneous inputs of variable
magnitude and their exact times of occurrence. The first argument,
<n>, is the number of inputs and input times. Next come a list of
magnitudes and a list of times. Each list is comma-separated.
Currently this function can only be used in the simulation description
file, and not in the model description file (which simply implies that
you cannot use it as a default). Syntax:
<input variable> = Spikes(<n>, <list-of-magnitudes>, <list-of-times>); |
The arguments of input functions can either be constants or variables. For example, if ‘Mag’ and ‘RateConst’ are defined model parameters, then the input variable ‘Q_in’ can be defined as:
Q_in = PerExp(Mag, 60, 0, RateConst); |
In this way the parameters of input functions can, for example, be
assigned statistical distributions in Monte Carlo simulations
(see section Distrib()
specification). Variable dependencies are resolved
before each simulation specified by a Simulation
section
(equivalently Experiment
) (see section Simulation
sections).
For each of the periodic functions, a single exposure beginning at time initial-time can be specified by giving an effectively infinite period, e.g. 1e10. The first period starts at the initial time of the simulation. Magnitudes change exactly at the times given.
Input variables assigned input functions can be combined to give a lot
of flexibility (e.g., an input variable can be declared as the sum
of others). Separate inputs can also be declared in the global section
of the model definition file and combined in the Dynamics
(see section Dynamics section) and CalcOutputs
(see section Output calculations) sections.
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Inline()
functions can be placed in the various sections of a
model file to introduce standard C code (or whatever) in your
models. Text placed between the parentheses of an Inline
function will be passed as is to the C compiler. That text can
span several lines but its size should not exceed MAX_EQN (defined in
‘lex.h’); In case it does, you can increase MAX_EQN (and
recompile ‘mod’...) or you can split you text between any number
of Inline()
in a row. It is your responsibility to make sure
that the code passed can be compiled without errors!
Example:
Inline( printf("hello/n"); ); |
Note also that the inlined code is likely to be dependent on whether
or not you are using the -R
option of mod
.
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The model initialization section begins with the keyword
Initialize
(the keyword Scale
is obsolete but is still
understood) and is enclosed in curly braces. The equations given in this
section will define a function (subroutine) that will be called by
GNU MCSim after the assignments specified in each
Simulation
section are done (see section Simulation
sections). They
are the last initializations performed. The model file in
‘mcsim/samples/perc’ gives an example of the use of
Initialize
(see section ‘perc.model’: A sample model description file, in Appendix).
All model variables and parameters, except inputs, can be changed in
this section. Modifications to state variables affect initial values
only. In this section, state variables, outputs and parameters (but not
input variables) can also appear at the the right-hand side of
equations. The integration variable can be accessed if referred to as
t
Warning: Assignments to state variables in the Initialize
section
override the same assignments made in input files.
Additional parameters (to those declared in the global section) may be
used within the section. They will be declared as local temporary
variables and their scope will be limited to the Initialize
section (i.e., their value and existence will be forgotten outside
the section).
The dt()
operator (see section Dynamics section) cannot be
used in this section, since derivatives have not yet been computed when
the scaling function is called.
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The dynamics specification section begins with the keyword
Dynamics
and is enclosed in curly braces. The equations given in
this section will be called by the integration routines at each
integration step. Dynamics
must be used if the model includes
differential equations.
Additional parameters (to those declared in the global section) may be used for any calculations within the section. They will be declared as local temporary variables. (Note, for example, the use of ‘Cout_fat’ and ‘Cout_wp’ in the ‘perc.model’ sample file). Local variables are not accessible from the simulation program, or from other sections of the model definition file, so don’t try to output them.
Each state variable declared in the global section must have one
corresponding differential equation in the Dynamics
section. If a differential equation is missing, mod
issues an
error message such as:
Error: State variable 'Q_foo' has no dynamics. |
and no ‘model.c’ file or executable program will be created.
The derivative of a state variable is defined using the dt()
operator, as shown here:
dt(state-variable) '=' constant-value-or-expression ';' |
The right-hand side can be any valid C expression, including standard math library calls and the special functions mentioned above (see section Special functions). Note that no syntactic check is performed on the library function calls. Their correctness is your responsibility.
The dt()
operator can also be used in the right-hand side of
equations in the dynamics section to refer to the value of a derivative
at that point in the calculations. For example:
dt(Qm_in) = Qmetabolized - dt(Qm_out); |
The integration variable (e.g., time) can be accessed if referred
to as t
, as in:
dt(Qm_in) = Qmetabolized - t; |
Output variables can also be made a function of t
in the
Dynamics
section.
Note that while state variables, input variables and model parameters
can be used on the right-hand side of equations, they cannot be assigned
values in the Dynamics
section. If you need a parameter to
change with time, you can declare it as an output variable in the global
section. Assignments to states, inputs or parameters in this section
causes an error message like the following to be issued:
Error: line 48: 'YourParm' used in invalid context. Parameters cannot be defined in Dynamics{} section. |
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GNU MCSim can solve delay differential equations.
Delay differential equations are equations that depend on past values of the state variables, say at time t-tau instead of at time t.
This is done very easily in GNU MCSim models with the
CalcDelay
funtion. Its syntax is:
CalcDelay (<variable>, <delay>); |
The variable
must be a declared state or output
variable. CalcDelay()
will return its past value (at time
delay). The delay specified must be either a declared
parameter or a constant floating point or integer value. For
example:
tau = 100; dt (Q1) = k * CalcDelay(Q3, tau); dt (Q2) = k * CalcDelay(Q3, 10); |
An example of a model using CalcDelay()
and input file is given
in the ‘example/delay_diff_eqns’ folder. Note that currently, the
CalcDelay()
function cannot be used with the -R
option for
mod
(i.e., for use with the deSolve R package).
GNU MCSIm stores required variables past values in a arrays of size MAX_DELAY (equal by default to 1000). If the needed past recall exceeds that capacity you will need to increase the value of MAX_DELAY in the C file ‘delays.c’ and recompile the library and your model.
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The output calculation section begins with the keyword
CalcOutputs
and is enclosed in curly braces. The equations
given in this section will be called by the simulation program at each
output time specified by a Print()
or PrintStep()
statement (see section Print()
specification, and see section PrintStep()
specification). In this way, output computations are done
efficiently, only when values are to be saved.
Only variables that have been declared with the keyword Outputs
,
or local temporary variables, can receive assignments in this section.
Assignments to other types of variables cause an error message like the
following to be issued:
Error: line 56: 'Qb_fat' used in invalid context. Only output and local variables can be defined in CalcOutputs section. |
Any reference to an input or state variable will use the current value
(at the time of output). The dt()
operator can appear in the
right-hand side of equations, and refers to current values of the
derivatives (see section Dynamics section). Like in the
Dynamics
section, the integration variable can be accessed if
referred to as t
, as in:
Qx_out = DQx * t; |
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For your model file to be readable and understandable, it is useful to use a consistent notation style. The example file ‘perc.model’ tries to follow such a style (see section ‘perc.model’: A sample model description file). For example we suggest that:
These conventions are suggestions only. The key to have a consistent notation that makes sense to you. Consistency is one of the best ways to:
Last, but not least, do use comments to annotate your code! Also: make sure your comments are accurate and update them when you change your code. In our experience, an enormous number of hours has been wasted in trying to figure out inconsistencies that existed only because of inaccurate comments (e.g., erroneous comments about the reasons for choice of default parameter values). That does not decrease the value of good comments, however...
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To read models written in SBML, you simply need to create a text file
of the same format as an MCsim model definition file (comments
starting with #, file ending with End
, etc.) In that
file, you specify a list of one or more SBML model files with the
keyword SBMLModels
followed by an equal sign and a list of SBML
model file names (of maximum size MAX_FILENAMESIZE, 80 characters, as
defined in mod.h), enclosed in curly braces, as shown here:
#--------------------------------------------------------- # SBML_List.in # Use it as "mod SBML_List.in" #--------------------------------------------------------- SBMLModels = {useID, "default", "C_central.xml", "C_periph.xml"}; End. |
The first two tokens, useID
and "default"
, at the very
beginning of the list, are optional (but if the appear they must both
appear). The first indicates that species are recognized by "IDs" (at
the moment this the only possibility, hardly an option indeed, but in
the future we would like to be able also to use Names"). The second
one gives the name of the default (external) compartment in your SBML
files. Here the default is named "default", which works for
CellDesigner, but JDesigner, for example, uses "compartment"... The
list of files comes after, with the filenames being enclosed with
double quotes. There is no restriction about their extension (".xml" is
just an example). They just have to be valid filenames.
Give that file as input to mod
, by typing in mod
SBML_List.in
on your shell command line. A ‘model.c’ file is
produced, suitable for further compilation by makemcsim
. SBML
model files are typically ASCII text files with a xml extension
(for examples see ‘mcsim/sim/samples/SBML’). SBML Level 1 and
Level 2 are recognized (but some features of Level 2 are not yet
understood by mod
, such as functionDefinition
,
unit
, rateRule
and a few others.) Omitted reaction
stoichiometries are set to value 1 by default. Compartments are
ignored unless a model template for circulating species is given (see
below).
If two or more of the SBML files define a same chemical species,
mod
merges the models: namely, the rate equation for that
species will be the sum of the rate equations implied by each
model. In fact, mod
constructs the rate equations from the
reaction descriptions given by the SBML format. State variables and
parameters keep the same name after merging, so they should be unique
from the start to each SBML model, to avoid confusion.
To automatically extend SBML (level 2) models (e.g., with
transport component terms, an example which will be used throughout
this part of the manual), a template model can be applied to all the
species defined in SBML which are placed in the default compartment
(conventionally called compartment
), and outside of other
compartments whose names are specified by the template
itself. Specifically: the template model should define compartments
and differential equations terms for the transport of an unspecified
species between those compartments (the syntax for that will be
described below). For each species placed in SBML outside one
of the defined compartments, a differential equations is created for
each compartment using the template transport terms. The differentials
for the compartments found in both the template and the SBML models
will contain both transport and kinetic terms. With the exception of
the generic compartment
, the SBML models merged can only use
the compartments defined by the template (compartment name recognition
is case-sensitive). Species placed in SBML inside one of the
defined compartments are not transported and stay local to that
compartment. Their differentials will contain only the reaction
kinetics terms defined by the SBML models. For simplicity, the default
initial values of the model state variables (species) specified by the
user are ignored and set to zero.
Warning: At the moment, libSBML does not guarantee the type of operands
or their format for a division. While 5.4/2.3
will appear correctly
as a float division, 5.0/2.0
is likely to be translated in 5/2
which is an integer division in C! It is probably best to use the multiplication
by the inverse in that case, but that forces you to check SBML models manually
for all divisions...
There might be many applications of the template mechanism, and they are left to the reader to imagine.
The template model to use is specified with PKTemplate
keyword,
followed by an equal sign and the file name of a template model file
(of maximum size MAX_FILENAMESIZE, 80 characters, as defined in
mod.h), enclosed in curly braces, like in:
#--------------------------------------------------------- # SBML_List.in # Use it as "mod SBML_List.in" #--------------------------------------------------------- PKTemplate = {"input_f/2cpt_PBPK.model"}; SBMLModels = {"C_central.xml", "C_periph.xml"}; End. |
The template structure is similar to that of other GNU MCSim
models, with two exceptions: First, the variables and parameters which
are supposed to apply to several chemical species defined in SBML are
preceeded with an underscore (_). Second, the compartments
allowed in SBML and for which the template is defined are declared
using the keyword Compartment
, followed by an equal sign and a
list of the compartment names enclosed in curly braces. Here is an
example of template file:
#--------------------------------------------------------- # Template for pharmacokinetic modeling #--------------------------------------------------------- States = {_central, _periph}; Inputs = {_Dose_rate}; Outputs = {_Q_total}; Compartments = {central, peripheral}; # Species-dependent parameters _PC = 2; _K_urine; # Species-independent parameters V_central; V_periph; Initialize { _K_urine = _K_urine * 2; } Dynamics { _Q_rate_c_p = 5 * (_central - _periph / _PC); # Central compartment quantity _pk_central = (_Dose_rate - _Q_rate_c_p - _K_urine * _central) / V_central; dt(_central) = _pk_central; # Peripheral compartment quantity _pk_periph = _Q_rate_c_p / V_periph; dt(_periph) = _pk_periph; } CalcOutputs { _Q_total = _central / V_central + _periph / V_periph; } End. |
With that template, each species, say S1, defined in SBML to be
outside of the central
or peripheral
compartments will
be cloned to form the state variables S1_central and
S1_peripheral. Those will have associated differential equations
using specific parameters S1_PC or unspecific ones like
V_central. The chemical reactions defined in SBML to take place inside
the central or peripheral compartments will be translated into
specific terms added to the dt(_central)
and
dt(_periph)
equations.
Warning: the automatic creation of a model by merging SBML files with
a template may shuffle the order of the differential equation
declarations. Therefore you should not use an already defined
dt()
term in a subsequent differential equation in the template
model.
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GNU MCSim ‘mod’ model generator can be passed the -R
option. For example:
mod -R perc.model |
In that case, the C code produced can be used by the deSolve
package of the R statistical software (see
http://www.r-project.org/) to perform simulations of your models. The
numerical integators provided by deSolve are improved implementations of
the lsode family of integrators used by GNU MCSim), and
deSolve
provides a few more options than GNU MCSim (see
the deSolve
user manuals). However, if you need raw speed (say,
for Markov chain Monte Carlo simulations) GNU MCSim is probably
the fastest option.
In addition to producing a ‘model.c’ file in C language, ‘mod’
called with the -R
option also generates ‘model_inits.R’
file. That file can be loaded in R and provides the R two
functions initParms
, initStates
and the variable
Outputs, which can be handy in R scripts:
initParms()
without parameters reset the model parameters to
their default values. The newparms parameter takes a vector of named
parameters and values, assign the given values to the corresponding
model parameters and reset the others to their default values.
initStates()
with just the parms parameter reset the model
initial states to their default values. If the newparms parameter
is used it takes a vector of named states and values, assign the given
values as initial values to the corresponding model state variables and
reset the others to their default values.
An example of R script using GNU MCsim
to generate and run a
model is given in the ‘example/R’ folder.
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After having your model processed by mod
or makemcsim
,
and obtained an executable ‘mcsim_...’ file, you are ready to run
simulations. For this you need to write simulation files. This chapter
explains how to write such files with the proper syntax and how to run
the executable program.
You may want to first give a look at the examples given in the ‘mcsim/samples’ directory. An sample file ‘perc.lsodes.in’, which works with the perchloroethylene model ‘perc.model’, is also given in an Appendix to this manual (see section ‘perc.lsodes.in’).
6.1 Using the compiled program | how to process a simulation file | |
6.2 Syntax of the simulation definition file | how to write a simulation file | |
6.3 Analyzing simulation output | beyond GNU MCSim... | |
6.4 Error handling |
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GNU MCSim provides several types of simulations for the models you create. Simulations are specified in a text file of format similar to that of the model description file.
Assume that your model ‘a.model’ has been preprocessed and compiled
by makemcsim
(see section Using makemcsim
to fully process model files) to generate an executable
‘mcsim_my’. If you have renamed the executable file, substitute
‘mcsim_my’ by the name of your executable in the following. In
Unix the command-line syntax to run that executable is simply:
mcsim_a [input-file [output-file]] |
where the brackets indicate optional arguments. If no input and output
file names are specified, the program will prompt you for them. You
must provide an input file name. That file should describe the
simulations to perform and specify which outputs should be printed out
(see section Syntax of the simulation definition file). If you just hit the return key
when prompted for the output name, the program will use the name you
have specified in the input file, if any, or a default name
(see section OutputFile()
specification). If just one file name is given
on the command-line, the program will assume that it specifies the
input file. For the output filename, the program will then use the
name you have specified in the input file, if any, or a default
name.
When the program starts up, it announces which model description file was used to create it. While the input file is read or while simulations are running, some informations will be printed on your computer screen. They can help you check that the input file is correctly interpreted and that the program runs as it should. GNU MCSim can also post error messages, which should be self-explanatory. Where appropriate, they show the line number in the input file where the error occurred. Beware, however, of cascades of errors generated as a consequence of a first one; also errors may be detected after the line in which they really occur and the line number shown will be unhelpful; don’t panic: start by fixing the first error in the input file and rerun your executable. You should not need to recompile your executable, unless you have changed the model itself. If you get really stuck you can send a message to the mailing list "help-mcsim@prep.ai.mit.edu" (see section Installation) or to the authors of this manual.
The program ends (if everything is fine) by giving you the name of the
output file generated. If you want to run the program in batch mode (in
the background), you may want to redirect the screen output and error
messages; refer for this to the man
pages for your shell.
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A simulation specification file is a text (ASCII) file that consists
of several sections, starting with global specifications and
assignments (valid throughout the file), followed by a number of
Simulation
sections (see section Simulation
sections), eventually
enclosed in Level
sections. (The keyword Experiment
is
now obsolete but can still be used as a synonym for
Simulation
.)
Each Simulation
section defines simulation conditions, from an
initial time (or whatever the dependent variable represents,
see section Model types) to a final time. Initial values of the model state
variables, parameter values, input variables time-course, and which
outputs are to be printed at which times, can all be changed in a given
Simulation
section.
In simple cases, the general layout of the file is therefore (see also the sample file in ‘perc.lsodes.in’):
# Input file (text after # are comments) <Global assignments and specifications> Simulation { <Specifications for first simulation> } Simulation { <Specifications for second simulation> } # Unlimited number of simulation specifications End. # Mandatory End keyword. Everything after this line is ignored |
For Markov chain Monte Carlo simulations (see section MCMC()
specification),
the general layout of the file must include Level
sections.
Level
sections are used to define a hierarchy of statistical
dependencies (see section Setting-up statistical models). In that case, the
general layout of the file is:
# Input file <Global assignments and specifications> Level { # Up to 10 levels of hierarchy Simulation { <Specifications and data for first simulation> } Simulation { <Specifications and data for second simulation> } # Unlimited number of simulation specifications } # end Level End. # Mandatory keyword. |
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The general syntax of the file is the same as that of structural model definition files (see section General syntax) except that:
NDoses()
and Spikes()
functions), an Events()
specification or a constant values.
At the program start, all model parameters are initialized to the
nominal values specified in the model description file. Next, after the
input file is read, modifications given in its global section (including
random sampling) are applied, then those specified at each Level
,
and finally any modifications specified by the Simulation
sections. Computations specified in the Initialize
section of the
model definition file are the last initialization statements
executed.
Structural changes to the model (e.g., addition of a state, input,
output or parameter) cannot be done here and must be done in the model
description file. The simulation specification file is read until a
mandatory End
keyword is reached.
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Input variables can be assigned all the input functions defined previously (see section Input functions). Briefly, these are:
PerDose()
:
PerDose(<magnitude>, <period>, <initial-time>, <exposure-time>); |
PerExp()
:
PerExp(<magnitude>, <period>, <initial-time>, <decay-constant>); |
NDoses()
:
NDoses(<n>, <list-of-magnitudes>, <list-of-initial-times>); |
Spikes()
:
Spikes(<n>, <list-of-magnitudes>, <list-of-times>); |
In addition, they can be assigned a Events()
specification
(see section Events()
specification for state discontinuities).
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In the global section you can modify, by assignment, the value of
already defined state or input model variables or parameters (you cannot
assign a value to an output variable). These assignments will be in
effect throughout the input file, unless they are overridden later in
the file. Here is an exemple of assignment (assuming that x
and
Pi
have been properly defined in the model definition
file):
x = 10; # set the initial value if x is a state variable Pi = 3; # to stop worrying about little decimals... |
In the global section, you can also give specifications relevant to all
Simulation
or Level
sections. These specifications are not
needed if you just want to perform simple simulations. They should also
not appear inside Simulation
or Level
sections (with the
notable exception of Distrib()
specifications which can appear
inside Level
sections). They are used to call for and define the
parameters of special computations (e.g., the number of Monte Carlo
simulations to run, which sampling distributions to use for a given
parameter, the data likelihood, etc.) These specifications are the
following:
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OutputFile()
specificationThe OutputFile()
specification allows you to specify a name for
the output file of basic simulations. If this specification is not given
the name ‘sim.out’ is used if none has been supplied on the
command-line or during the initial dialog. The corresponding syntax
is:
OutputFile("<OutputFilename>"); |
where the character string <OutputFilename>, enclosed in double quotes, should be a valid file name for your operating system.
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Integrate()
specificationThe integrator settings can be changed with the Integrate
specification. Two integration routines are provided: Lsodes
(which originates from the SLAC Fortran library and is originally based
on Gear’s routine) (Gear, 1971b; Gear, 1971a; Press et al., 1989)
(see section Bibliographic References) and Euler
(Press et al.,
1989).
The syntax for Lsodes
is:
Integrate(Lsodes, <rtol>, <atol>, <method>); |
where <rtol> is a scalar specifying the relative error tolerance for each integration step. The scalar <atol> specifies the absolute error tolerance parameter. Those tolerances are used for all state variables. The estimated local error for a state variable y is controlled so as to be roughly less (in magnitude) than rtol*|y| + atol. Thus the local error test passes if, for each state variable, either the absolute error is less than <atol>, or the relative error is less than <rtol>. Set <rtol> to zero for pure absolute error control, and set <atol> to zero for pure relative error control. Caution: actual (global) errors may exceed these local tolerances, so choose them conservatively.
The <method> flag should be 0 (zero) for non-stiff differential systems and 1 or 2 for stiff systems. If you specify <method> 2 you should provide the Jacobian of your differential system (see the “Perc” model in the example folder); otherwise the Jacobian will be computed by numerical differentiation (which is about as fast if the Jacobian is dense). You should try flag 0 or 1 and select the fastest for equal accuracy of output, unless insight from your system leads you to choose one of them a priori. In our experience, a good starting point for <atol> and <rtol> is about 1e-6.
The syntax for Euler
is:
Integrate(Euler, <time-step>, 0, 0); |
where <time-step> is a scalar specifying the constant time increment for each integration step. The next two scalars are reserved for future use and should be set to zero.
Note: if the Integrate()
specification is not used, the default
integration method is Lsodes
with parameters
1e-5, 1e-7 and 1.
We recommend using Lsodes
, since is it highly accurate and
efficient. Euler
can be used for special applications
(e.g., in system dynamics) where a constant time step and a simple
algorithm are needed.
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MonteCarlo()
specificationMonte Carlo simulations (Hammersley and Handscomb, 1964; Manteufel,
1996) (see section Bibliographic References) randomly sample parameter
values and run the model for each parameter set so generated. The
statistical distribution of the model outputs can be studied for
uncertainty analysis, sensitivity analysis etc. Such simulations
require the use of two specifications, MonteCarlo()
and
Distrib()
, which must appear in the global section of the file,
before the Simulation
sections. They are ignored if they appear
inside a Simulation
section.
The MonteCarlo
specification gives general information required
for the runs: the output file name, the number of runs to perform, and a
starting seed for the random number generator. Its syntax is:
MonteCarlo("<OutputFilename>", <nRuns>, <RandomSeed>); |
The character string <OutputFilename>, enclosed in double quotes, should be a valid filename for your operating system. If a null-string "" is given, the default name ‘simmc.out’ will be used. The number of runs <nRuns> should be an integer, and is only limited by either your storage space for the output file or the largest (long) integer available on your machine. The seed <RandomSeed> of the pseudo-random number generator can be any positive real number. Seeds between 1.0 and 2147483646.0 are used as is, the others are rescaled within those bounds (and a warning is issued). Here is an example of use:
MonteCarlo("percsimmc.out", 50000, 9386.630); |
The parameters’ sampling distributions are specified by a list of
Distrib()
specifications, as explained in the following
(see section Distrib()
specification). The format of the output file of
Monte Carlo simulations is discussed later (see section Analyzing simulation output).
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MCMC()
specificationMarkov chain Monte Carlo (MCMC) can be defined as stochastic simulations following a Markov chain in a given parameter space. In MCMC simulations, the random choice of a new parameter value is influenced by the current value. They can be used to obtain a sample of parameter values from complex distribution functions, eventually intractable analytically. Such complex distribution functions are typically encountered during Bayesian data analysis, under the guise of posterior distributions of a model’s parameters. The reader wishing to use the MCMC capabilities of GNU MCSim is referred to the published literature (for example, Bernardo and Smith, 1994; Gelman, 1992; Gelman et al., 1995; Gelman et al., 1996; Gilks et al., 1996; Smith, 1991; Smith and Roberts, 1993) (see section Bibliographic References).
MCMC simulation chains (which in GNU MCSim start from a sample from the specified prior) need to reach "equilibrium". Checking that equilibrium is obtained is best achieved, in our opinion, by running multiple independent chains (cf. Gelman and Rubin, 1992, and other relevant statistical literature). GNU MCSim does not deal (yet) with convergence issues.
The Bayesian analysis of data with GNU MCSim requires you to setup:
Distrib()
specification).
Setting-up a statistical model requires Level
sections and
Data()
specifications. Assigning priors and likelihoods is
achieved through the Distrib()
statements (or its equivalents
Density()
and Likelihood()
). Please refer to the
corresponding sections of this manual, if you are not familiar with
them. The MCMC()
statement, gives general directives for MCMC
simulations and has the following syntax:
MCMC("<OutputFilename>", "<RestartFilename>", "<DataFilename>", <nRuns>, <simTypeFlag>, <printFrequency>, <itersToPrint>, <RandomSeed>); |
The character strings <OutputFilename>, <RestartFilename>, and <DataFilename>, enclosed in double quotes, should be valid file names for your operating system. If a null-string "" is given instead of the output file name, the default name ‘MCMC.default.out’ will be used.
If a restart file name is given, the first simulations will be read from
that file (which must be a text file). This allows you to continue a
simulated Markov chain where you left it, since an MCMC output file can
be used as a restart file with no change. Note that the first line of
the file (which typically contains column headers) is skipped. Also, the
number of lines in the file must be less than or equal to
<nRuns>. The first column of the file should be integers, and the
following columns (tab- or space-separated) should give the various
parameters, in the same order as specified in the list of
Distrib()
specifications in the input file.
If a data file name is given, the observed (data) values for the
simulated outputs will be read from that file (in ASCII format);
otherwise, Data()
specifications (see section Data()
specification)
should be provided. We recommend that you use Data()
specifications rather that the data file, which is much more error
prone. The first line of the data file is skipped and can be used for
comments. The total number of data points should equal the total number
of outputs requested. The data values should be given on the second and
following lines, separated by white spaces or tabs. A data value of "-1"
will be treated as "missing data" and ignored in likelihood
calculations. The convention "-1" can be changed by changing
INPUT_MISSING_VALUE in the header file ‘mc.h’ and recompiling the
entire library.
The integer <nRuns> gives the total number of runs to be performed, including the runs eventually read in the restart file.
The next field, <simTypeFlag> should be between 0 and 4 (included):
InvTemperature()
specification, with the following syntax:
InvTemperature(<nValues>, <value 1>, <...>, <value n>); |
The first number of the specification gives the number of perks to use and is followed by a list of them. Those should be numbers in the interval ]0,infinity[. Values above 1 lead in fact to simulated annealing (sharpening of the posterior distribution), suitable for optimization (see Amzal et al. 2006) (see section Bibliographic References). For simulated tempering (including thermodynamic integration you will want to keep perks lower or equal to 1. You usually want to include the value 1 (since temperature = 1 corresponds to your target distribution.) At perk zero, the posterior distribution is uniform for all parameters (in the case of posterior tempering), or to the prior (in the case of likelihood tempering). Each time the simulated Markov chain reaches perk zero is a regeneration time (Geyer and Thompson, 1995). Samples obtained at perk 1 between regeneration times are guaranteed to be from the posterior distribution, so that only one chains needs to be run and convergence need not to be checked (a significant advantage of simulated tempering). Simulated tempering is also adapted to problems with multiple maxima of the posterior distribution, in which standard samplers or Hamiltonian MCMC usually get stuck in a local mode.
The integer <printFrequency> should be set to 1 if you want an output at each iteration, to 2 if you want an output at every other iteration etc. The parameter <itersToPrint> is the number of final iterations for which output is required (e.g., 1000 will request output for the last 1000 iterations; to print all iterations just set this parameter to the value of <nRuns>). Note that if no restart file is used, the first iteration is always printed, regardless of the value of <itersToPrint>. Finally, the seed <RandomSeed> of the pseudo-random number generator can be any positive real number. Seeds between 1.0 and 2147483646.0 are used as is, others are rescaled silently within those bounds.
In the case of component by component jumps (<simTypeFlag> set to 0), tempered or stochastic optimization (<simTypeFlag> set to 3 or higher), the jump kernel is saved with the same name as the output file, with a .kernel extension. If the simulations are restarted in a continuation mode and if a kernel file with the same name as the restart file (with an added .kernel extension) is present, the jump kernel is restored.
Finally, the format of the output file of MCMC simulations is quite similar to that of straight Monte Carlo simulations and will discussed in a later section (see section Analyzing simulation output).
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SetPoints()
specificationTo impose a series of set points (i.e., already tabulated values for
the parameters), the global section can include a SetPoints()
specification. It allows you to perform additional simulations with
previously Monte Carlo sampled parameter values, eventually filtered.
You can also generate parameters values in a systematic fashion, over a
grid for example, with another program, and use them as input to
GNU MCSim. Importance sampling, Latin hypercube sampling, grid
sampling, can be accommodated in this way.
This command specifies an output filename, the name of a text file containing the chosen parameter values, the number of simulations to perform and a list of model parameters to read in. It has the following syntax:
SetPoints("<OutputFilename>", "<SetPointsFilename>", <nRuns>, <parameter identifier>, <parameter identifier>, ...); |
If a null string is given for the output filename, the set points output will be written to the same default output file used for Monte Carlo analyses, ‘simmc.out’.
The SetPointsFilename is required and must refer to an existing
file containing the parameter values to use. The first line of the set
points file is skipped and can contain column headers, for
example. Each of the other lines should contain an integer (e.g.,
the line number) followed by values of the various parameters in the
order indicated in the SetPoints()
specification. If extra
fields are at the end of each line they are skipped. The first integer
field is needed but not used (this allows you to directly use Monte
Carlo output files for additional SetPoints
simulations).
The variable <nRuns> should be less or equal to the number of lines (minus one) in the set points file. If a zero is given, all lines of the file are read. Finally, a comma-separated list of the parameters to be read in the SetPointsFilename is given. The format of the output file of set points simulations is discussed below (see section Analyzing simulation output).
Following the SetPoints()
specification, Distrib()
statements can be given for parameters not already in the list
(see section Distrib()
specification). These parameters will be sampled
accordingly before to performing each simulation. The shape parameters
of the distribution specifications can reference other parameters,
including those of the list.
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OptimalDesign()
specificationThe "OptimalDesign" procedure optimizes the number and location of
observation times for experimental conditions you specify, in order to
minimize the variance of a parameter or an output you designate. It
requires a structural model (see section Setting-up Structural Models), a
statistical model in the form of a likelihood()
function
(see section Setting-up statistical models), and a random set of parameter
vectors sampled from a prior distribution (using Monte Carlo or MCMC
simulations) (for example and details, see Bois et al., 1999)
(see section Bibliographic References). The statistical model used should be
quite simple and cannot not use Level
sections (and hence cannot
be hierarchical).
The OptimalDesign command has the following syntax:
OptimalDesign("<OutputFilename>", "<ParameterSampleFilename>", <nSamples>, <RandomSeed>, <Style>, <parameter identifier>, <parameter identifier>, ...); |
The character strings <OutputFilename>, and <ParameterSampleFilename>, enclosed in double quotes, should be valid file names for your operating system. If a null-string "" is given instead of the output file name, the default name ‘simopt.default.out’ will be used.
A parameter sample file name must be given (that file must be a text
file). The first line of the file (which typically contains column
headers) is skipped. The number of lines in the file must be less than
or equal to <nSamples>. The first column of the file should be
integers (typically row numbers), and the following columns (tab- or
space-separated) should be values of the various parameters in the
order indicated in the list at the end of the OptimalDesign()
specification. If extra fields are at the end of each line they are
skipped. The first integer field is needed but not used (this allows
you to directly use Monte Carlo output files for OptimalDesign
simulations).
The integer <nSamples> indicates the number of lines to read
from the <ParameterSampleFilename> file. The seed
<RandomSeed> of the pseudo-random number generator can be any
positive real number. Seeds between 1.0 and 2147483646.0 are used as
is, others are rescaled silently within those bounds. The directive
Style should be either the keyword Forward
or the keyword
Backward
. Forward optimization will start from no new data and
will add, sequentially, optimal observation times. Backward
optimization starts with the full set of observation times you propose
and delete the least informative ones, sequentially. We recommand that
you try both options. Finally, a comma-separated list of the
parameters to be read in the ParameterSamplFilename should be
given.
The input file must then contain two sets of Simulation
definitions. You should look at the sample optimal design files
provided in ‘mcsim/samples’.
The first set specifies all experimental conditions and the set of
observation times to optimize, for one or several output variables
given in Print
statements. The output times you specify for
each output variable define an array of observation time values that
the optimization algorithm will rank by order of the estimated
variance reduction they permit for variables or parameters you will
specify in the second set of Simulation
definitions. Data will
be simulated for each of the required output. There must be one Data
statement per output specified (the data values are arbitrary). An
error model must be specified for those data, using a
Likelihood
statement (see section Distrib()
specification).
The second set of Simulation
specifies optimization target
parameters or outputs. The algorithm will select time-points (in the
first section’s Simulation
specifications) that minimize the
estimation variance of those parameters or outputs. When a parameter is
targeted no inputs are needed. If you optimize for an output variable
variance (i.e., for the variance of a model prediction), the
experimental conditions can be very different from those of the
experiment whose conditions you optimize. The link is afforded solely by
the parameters (in the first set you are trying to determine the
conditions that will optimally identify the parameter values
conditioning the predictions – or trivially, the parameters – of the
second set)
The format of the output file of design optimization simulations is
quite specific. The first column is an iteration number. At each
iteration one observation point is added (Forward
mode) or
removed (Backward
mode). Each step is therefore conditioned by
the selection of an observation time-point made by the previous
step. The following columns give, for each observation time point you
specify, the average variance of the target outputs or parameters
achieved if this point is added (Forward
mode) or removed
(Backward
mode). Next the chosen time point at this step is given
(the one minimizing average variance), followed by the variance it leads
to (in expectation) and the corresponding standard deviation. The last
column "Utility" is zero, unless you uncomment the function
Compute_utility
and modify its code in ‘optdesign.c’ to
compute a utility of your own.
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Distrib()
specificationThe specification of distributions for simple Monte Carlo simulations
is quite straighforward. MCMC simulations require the definition of a
full statistical model and the use of distributions is somewhat more
complex in that case, but the use of Distrib()
is basically the
same.
In the context of MonteCarlo()
or SetPoints()
simulations (see section MonteCarlo()
specification, and SetPoints()
specification), one (and only one) Distrib()
specification must
be included for each model parameter to randomly sample. State, input or
output variables cannot be randomly sampled by Distrib()
in this
context. A simulation specification file can include any number of
Distrib()
commands at the global level.
Distrib()
specifies the name of the parameter to sample, and its
sampling distribution. Its syntax is:
Distrib(<parameter identifier>, <distribution-name>, [<shape parameters>]); |
The <parameter identifier> gives the name of the parameter to sample. The <distribution-name> and the corresponding <shape parameters> indicate the sampling distribution to use (Bernardo and Smith, 1994; Gelman et al., 1995) (see section Bibliographic References). They are specified as follow:
Beta
, takes at least two strictly positive real shape parameters:
A and B. By default the Beta distribution is defined over
the interval [0;1]. If a range is given for the beta distribution, the
[0;1] interval is mapped onto the specified range.
Binomial
, needs two strictly positive numbers: the probability
p (a real in the interval [0;1]), and the sample size N, an
integer. If N is not given as an integer it will be rounded down
during the computations.
Cauchy
, takes one strictly positive real number as parameter:
its scale s.
Chi2
, takes one strictly positive real number as parameter:
n. This distribution is the same as Gamma(n/2, 1/2).
Exponential
, uses one strictly positive real number: the
inverse-scale b.
Gamma
, uses two strictly positive real parameter: the shape and
the inverse scale.
HalfCauchy
, takes one strictly positive real number as
parameter: the scale s. The mode is at zero, on the lower
boundary. The random variates returned are strictly positive.
HalfNormal
, takes one real number as parameter: the standard
deviation, strictly positive. The mode is at zero, on the lower
boundary. The random variates returned are strictly positive.
InvGamma
(inverse gamma distribution), needs two strictly
positive real parameters: the shape and the scale.
LogNormal
, takes two reals numbers as parameters: the geometric
mean (exponential of the mean in log-space) and the geometric standard
deviation (exponential, strictly superior to 1, of the standard
deviation in log-space).
LogNormal_v
, is the lognormal distribution with the variance (in
log space!) instead of the standard deviation as second parameter. You
can use it to specify a hierarchical model with a conjugate prior on the
variance (see section Setting-up statistical models).
LogUniform
, with two shape parameters: the minimum and the
maximum of the sampling range (real numbers) in natural space.
Normal
, takes two reals numbers as parameters: the mean and the
standard deviation, the latter being strictly positive.
Normal_v
, is also the normal distribution with the variance
instead of the standard deviation as second parameter. You can use it to
specify a hierarchical model with a conjugate prior on the variance
(see section Setting-up statistical models).
Piecewise
, uses four reals as parameters: the minimum,
A, B, and the maximum. The distribution has the form
of a truncated triangle, with a plateau between A and B. If
A = B,
the distribution is the triangular distribution.
Poisson
, needs a strictly positive real: the rate A.
StudentT
, requires three parameters: its number of degrees of
freedom (an integer), its mean, and its standard deviation.
TruncInvGamma
(truncated inverse gamma distribution), needs
four strictly positive real parameters: the shape, the scale, the
minimum and the maximum.
TruncLogNormal
(truncated lognormal distribution), uses four real
numbers: the geometric mean and geometric standard deviation (strictly
superior to 1), the minimum and the maximum in natural space. For
example:
Distrib(Var, TruncLogNormal, 1, 2.718, 0.01, 10) |
samples ‘Var’ such that log(‘Var’) is a standardized normal variate of mean log(1) and standard deviation log(2.718) - while ‘Var’ is truncated to fall between 0.01 to 10.
TruncLogNormal_v
, is like the truncated lognormal, except that it
takes the variance (in log space!) instead of the standard deviation as
second parameter. You can use it to specify a hierarchical model with a
conjugate prior on the variance (see section Setting-up statistical models).
TruncNormal
(truncated normal distribution), takes four real
parameters: the mean, the standard deviation (strictly positive), the
minimum and the maximum.
TruncNormal_v
, is like the truncated normal distribution with the
variance instead of the standard deviation as second parameter.
Uniform
, with two shape parameters: the minimum and the maximum
of the sampling range (real numbers).
The shape parameters of the above distributions can symbolically reference other model parameters, even if distributions for these have already been defined. For example:
Distrib(A, Normal, 0, 1); Distrib(B, Normal, A, 2); |
In the context of MCMC sampling, GNU MCSim provides
extensions of the above Distrib()
specification syntax.
First, when Distrib()
is used to specify the distribution of a
model parameter, that parameter can also appear as a shape
parameter, if a distribution has already been specified for the
parameter at an upper Level
of the file. For example:
Level { # upper level Distrib(A, Normal, 0, 1); Distrib(B, InvGamma, 2, 2); Level { # sub-level Distrib(A, Normal_v, A, B); ... } # end sub-level } # end upper level |
In that case, the parameter A, used for shape specification (as
the mean of a Normal distribution) in the sub-level, refers to the
"parent" A parameter, for which a standard Normal
distribution is defined at the upper Level
. The sampled
values of the parent parameters A and B will be used as mean
and variance for their "child" parameter, A, when it will be
its turn to be randomly sampled. This forms the basis of the
specification of multilevel (hierarchical) models (see section Setting-up statistical models).
Next, in MCMC simulations, you usually assign a probability
distribution (or a likelihood) to the data you are trying to
analyze. Typically, your model’s state and/or output variables will
attempt to predict some aspect of the observed data distributions
(mean, variance, etc.). GNU MCSim gives you the possibility to
specify a distribution for your data, using model parameters, input,
state, or output model variables, or even other data, to define the
distribution shape. This is achieved through the use of the
Data()
and Prediction()
"qualifiers".
Data()
can be used at the first position of a Distrib()
statement, or as a distribution shape parameter. It uses the following
syntax:
Data(<identifier>) |
where <identifier> corresponds to a valid input, state or output
model variable for which data are available. Model parameters cannot be
used (but you can assign a simple parameter value to an output variable
in your model definition file and use that output here). The actual data
values need to be given later in the simulation input file through
Data()
specifications (which, in addition to a variable
identifier, give a list of numerical data values, see Data()
specification) or in a separate datafile (see section MCMC()
specification).
Working hand in hand with Data()
, and using the same syntax, the
Prediction()
qualifier can be used to designate actual model
inputs, states and outputs for any shape parameter of a specified
distribution (therefore Prediction()
must appear after the
distribution name). The actual predicted values, matching exactly the
corresponding data, need to be given later in the simulation input file
through Print()
or PrintStep()
specifications
[see section Print()
specification and PrintStep()
specification).
Here are some example of use of Data()
and Prediction()
in the extended syntax of a Distrib()
specification:
Distrib (Data(y), Normal, Prediction(y), 0.01); ... Data (y, 0.1, 2, 5, 3, 9.2); Print(y, 10, 20, 40, 60, 100); Distrib (Data(y), Normal, Prediction(y), Prediction(sigma)); ... Data (y, 1.01, 1.20, 0.97, 0.80, 1.02); PrintStep(y, 10, 50, 10); PrintStep(sigma, 10, 50, 10); Distrib (Data(R), Binomial, Prediction(P), Data(N)); ... Data (R, 0, 2, 5, 5, 8, 9, 10, 10); Data (N, 10, 10, 9, 10, 9, 9, 11, 10); Print(P, 10, 20, 30,40, 50,60,70, 80); |
(these could not appear all as such in an input file, they would need to
be embedded in Level
and Simulation
sections.)
Last, for more readable input files, two keywords, Density()
and
Likelihood()
, can be used instead of Distrib()
. They are
equivalent to Distrib()
and have the same syntax.
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SimType()
specificationThis specification is now obsolete and should not be used. It is left for compatibility with old input files. It specifies the type of analysis to perform. Syntax:
SimType(<keyword>); |
The following keywords can be used: DefaultSim
(the list of
specified simulations is simulated), MonteCarlo
, MCMC
(previously Gibbs
), SetPoints
. If MonteCarlo
,
MCMC
, or SetPoints
analyses are requested, additional
specifications are needed (see below).
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Any simulation file must define at least one Simulation
section. Simulation
sections include particular specifications,
which are presented in the following.
Simulation sections | ||
Events() specification for state discontinuities | ||
StartTime() specification | ||
Print() specification | ||
PrintStep() specification |
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Simulation
sectionsAfter global specifications, if any, Simulation
sections must be
included in the input file. Expectedly, these sections start with the
keyword Simulation
and are enclosed in curly braces.
A Simulation
section can make assignments to any state variable,
input variable or parameter defined in the global section of the model
description file. Output variables cannot receive assignments in
simulation input files.
State variables and parameters can only take constant values
(see section General input file syntax). For state variables, this sets the
initial value only. So, for example, in a Simulation
section the
parameter Speed
, if properly defined, can be set using:
Speed = 83.2; |
This overrides any previously assigned values, even if randomly sampled, for the specified parameter.
Inputs can be redefined with input functions (see section Input functions (revisited)) or constant values. Input functions can reference other variables (eventually randomly sampled), as in:
Q_in = PerExp(InMag, 60, 0, RateConst); |
The maximum number of Simulation
sections allowed in an input
file is 200. This can be changed by changing MAX_INSTANCES and
MAX_EXPERIMENTS in the header file ‘sim.h’ and recompiling the
program (this requires re-installation).
Within a Simulation
section, several additional specifications
can be used:
StartTime()
,
Print()
,
PrintStep()
,
Data()
.
The Data()
specification is used only when a statistical model is
set up and will be covered in the corresponding section of this manual
(Setting-up statistical models).
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Events()
specification for state discontinuitiesYou can impose state variables discontinuities at a given set of times
with the Events
specification. Events
has a syntax similar
to the NDoses
input function (see section Input functions). It is in
fact a special type of input function that resets a state variable,
hence the need to assign it to a (dummy) input variable. Its syntax
is:
<input variable> = Events(<state variable>, <n>, <list-of-times>, <list-of-operation>, <list-of-scalars>); |
The first argument, <state variable>, is the state whose value you want to reset at given times. The integer <n> is the number of reset times you want to specify; <list-of-times> is a comma separated list of those reset times; <list-of-operations> is a comma separated list of operations that will affect the given state variable at the specified times (see next paragraph), and <list-of-scalar> is the list of floating point values used for by the operations specified, at the corresponding times.
The three keywords operations are Add
, Multiply
and
Replace
. Add
adds the corresponding scalar to the target
state variable at the specified time; Multiply
multiplies the
state variable by the specified scalar; Replace
simply replaces
the value of the state variable by the given scalar.
The assigned input value takes the value 1 at the specified times and is zero otherwise. If you don’t have a use for such an input, simply define a dummy input variable. For example:
In the model definition file define:
Inputs = {events_v1, events_v2}; |
and in the simulation specification file you can request, within a
Simulation
specification:
events_v1 = Events (v1, 2, 1, 9, Add, Add, 1, 4); events_v2 = Events (v2, 2, 1, 5, Multiply, Replace, 2, 6); |
At time 1, a value of 1 will be added to the state variable v1 and v2 will be multiplied by 2; At time 5 v2 will be reset to 5, and at time 9 the value 4 will be added to v1.
See (and run) the example model and input file provided in the example/events folder.
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StartTime()
specificationThe origin of time for a simulation, if it needs to be defined, can be
set with the StartTime()
specification, whose syntax is:
StartTime(<initial-time>); |
It just shifts the time scale. If this specification is not given, a value of zero is used by default. A parameter can be used as initital time value, so that initial time can be sampled in error-in-variable models, for example:
T0 = 20; StartTime(T0); |
The final time is automatically computed to match the largest output
time specified in the Print()
or PrintStep()
statements. Output times cannot be inferior to the initial
time.
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Print()
specificationThe value of any model variable or parameter can be requested for output
with Print()
specifications. Their arguments are a
comma-separated list of variable names (at least one and up to
MAX_PRINT_VARS, which is set to 10 by default), and a comma-separated
list of increasing times at which to output their values:
Print(<identifier1>, <identifier2>, ..., <time1>, <time2>, ...); |
where <identifier1>, <identifier2> etc. correspond to valid input, state or output model variables, or parameter.
The same output times are used for all the variables specified. The size
of the time list is only limited by the available memory at run
time. The limit of 10 variables names can be increased by changing
MAX_PRINT_VARS in the header file ‘sim.h’ and re-installing the
whole software. The number of Print()
statements you can used in
a given Simulation
section is only limited by the available
memory at run time. The same variable or parameter can appear in more
than one Print()
specification in a given Simulation
section.
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PrintStep()
specificationThe value of any model variable or parameter can be also output with
PrintStep()
specifications. They allow dense printing, suitable
for smooth plots, for example. Their arguments are: a comma-separated
list of variable names (at least one and up to MAX_PRINT_VARS, which is
set to 10 by default), the first output time, the last one, and a time
increment:
PrintStep(<identifier1>, <identifier2>, ..., <start-time>, <end-time>, <time-step>); |
The final time has to be superior to the initial time and the time step
has to be less than the time span between end and start. If the time
step is not an exact divider of the time span the last printing step is
shorter and the last output time is still the end-time specified. The
number of outputs produced is only limited by the memory available at
run time. You can use several PrintStep()
specification, and the
same variable or parameter can appear in more than one
PrintStep()
, in a given Simulation
section.
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With GNU MCSim, you must define a statistical model to use the
MCMC()
specification. MCMC
simulations will give you a
sample from the joint posterior distribution of the parameters that
you designate as randomly sampled through Distrib()
specifications. You do not need to specify explicitly that joint
posterior distribution (in fact, in most case, this is
impossible). The posterior distribution is implicitly defined by a
statistical model, that is simply a set of conditional relationship
between the parameters and some data.
GNU MCSim handles multilevel (hierarchical) random effects and mixed effects statistical models in a Bayesian framework. These models need to be defined in the simulation specification file, rather than in the structural model definition file. Yet, due to compilation constraints, if you need special parameters for your statistical model (e.g., variances) you have to declare them in the structural model file, even if they are not used by the structural model itself.
So, how do we go about specifying a statistical model with GNU MCSim? Take for example the following simple linear regression model:
y_i = N(Mu_i, Sigma^2), Mu_i = Alpha + Beta * (x_i - x_bar).
where the observed (x,y) pairs are (1,1), (2,3), (3,3), (4,3) and (5,5). Assume that the parameters Alpha and Beta are given N(0,10000) priors, and that 1/Sigma^2 is given a Gamma(1e-2,1e-2) prior. x_bar is the average of the above values for x. We want the posterior distributions of Alpha, Beta, and Sigma^2.
The first thing to do is to define a structural (or link) model to compute y as a function of x. Here is such a model (quite similar to the one distributed with GNU MCSim source code (see section ‘linear.model’):
# --------------------------------------------- # Model definition file for a linear model # --------------------------------------------- Outputs = {y}; # Structural model parameters Alpha = 0; Beta = 0; x_bar = 0; # Statistical parameter Sigma2 = 1; CalcOutputs { y = Alpha + Beta * (t - x_bar); } # --------------------------------------------- |
The parameters’ default values are arbitrary, and could be
anything reasonable. They will be changed or sampled through the input
file. Note that
Sigma2
is not used in the model equations, but still
needs to be defined here in order to be part of the statistical
model. On the other hand,
Mu is not defined, since we do not really need it.
Finally x is replaced by the time, t
, for convenience.
An alternative would be to define an input ‘x’ and use it instead
of t
.
We now need to write an input file specifying the distribution of y (i.e., the likelihood), and the prior distributions of the various parameters. Technically, GNU MCSim uses Metropolis sampling and you do not need to worry about issues of conjugacy or log-concavity of your prior or posterior distributions. Here is what a simulation file with a statistical model looks like:
# --------------------------------------------------------------- # Simulation input file for a linear regression # --------------------------------------------------------------- MCMC ("linear.MCMC.out", "", "", 50000, 0, 5, 40000, 63453.1961); Level { Distrib(Alpha, Normal_v, 0, 10000); Distrib(Beta, Normal_v, 0, 10000); Distrib(Sigma2, InvGamma, 0.01, 0.01); Likelihood(Data(y), Normal_v, Prediction(y), Sigma2); Simulation { x_bar = 3.0; PrintStep (y, 1, 5, 1); Data (y, 1, 3, 3, 3, 5); } } # end Level End. # --------------------------------------------------------------- |
The file begins with MCMC()
(see section MCMC()
specification). The
keyword Level
comes next. Level
is used to specify
hierarchical dependences between model parameters. There should be at
least one Level
in every MCMC input file, even for a
non-hierarchical model like the one above. See below for further
discussion of the Level
keyword. You can also look at the MCMC
input files provided as examples with GNU MCSim source code. The
Distrib()
statements define the parameter priors.
Normal_v
specifications are used since we use variances instead
of standard deviations. The inverse-Gamma distribution is used for the
variance component, since the precision is supposed to be
Gamma-distributed. The likelihood is the distribution of the data,
given the model: it is specified by a Likelihood()
specification,
valid for every y data point. Again, note that the
Mu
variable is not used. Instead, the Prediction(y)
specification
designates the linear model output. The distributions and likelihoods
specified are in effect for every sub-level or every Simulation
section included in the current Level
.
The "simulations" to perform, and the corresponding data values, are
specified by a Simulation
section. Only one Simulation
section is needed here, but several could be specified. In this section,
the value of
x_bar
is provided. The different values of x (time in our formulation of
the model) can be specified via PrintStep()
(see section PrintStep()
specification), since they are equally spaced. More generally,
Print()
can also be used (see section Print()
specification). The
data values are given in a Data()
statement (see below).
The following paragraphs deal with Level
sections and Data()
specifications.
Level sections | ||
Data() specification |
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Level
sectionsMarkov chain Monte Carlo simulations require the definition of a
statistical model structured with "levels". Think for example of
the definition of a prior distribution as a top level in a hierarchy,
with the data likelihood being at the lowest level. The hierarchy levels
are defined in GNU MCSim with the help of Level
sections. At
least one Level
section must be defined in the simulation input
file (you cannot use Level
in a structural model definition
file). A Level
section starts with the corresponding keyword and
is enclosed in curly braces (’{}’). It can include any number of
sub-levels or Simulations
sections. Simulations
(where the
data are specified) form the lowest level of the hierarchy
(see section Simulation
sections). In terms of structure, Simulation
sections behave like Level
sections (in particular with regard to
"cloning" of random variables, see below) except that they cannot
include further levels. There must be one and only one top Level
and at most 10 nested sub-levels in the hierarchy. This limit of 10 can
be increased (up to 255) by changing MAX_LEVELS in the header file
‘sim.h’ and re-installing GNU MCSim.
A Level
can specify or change the sampling distribution of any
model parameter properly defined in the global section of the structural
model description file. These distribution specifications apply to all
sub-levels of the Level
where they take place. For
example:
MCMC("samp.out", "", "", 1, 1, 1, 1, 1); # we are in an MCMC context Level { # this is the top level Distrib(A, Uniform, 0, 1); Likelihood(Data(y), Normal, Prediction(y), 1); Level { # sub-level 1 Distrib(A, Normal, A, 1); Simulation { ... } # simulation 1 Simulation { ... } # simulation 2 } # End sub-level 1 } # End top, end file End. |
A Level
can also make simple assignments to any model parameter
(see section General input file syntax). So, for example, in an
simulation, the parameter A could be modified with:
A = 2.0; |
This overrides any previously assigned values for the specified
parameter, even if randomly sampled, and applies to the sub-levels of
the Level
where it take place.
An important concept to grasp here is that of parameter "cloning". Cloning automatically creates, using templates, as many new parameters as you need in your multilevel model. One of the characteristic feature of multilevel models is the same parameters appear at several levels. For example, in a random effect model, a parameter (e.g., size) will be assumed to be randomly distributed in a population of individuals. If you have 100 individuals in your database, your model will have to deal with 100 individual size parameters and an average size. To spare you the tedium of defining the same distribution for many parameters, GNU MCSim creates an appropriate number of parameters for your model on the basis of its level structure. Assume that you have specified a distribution for a parameter A at a given level (that we label L1 for clarity). GNU MCSim will automatically create new parameters ("clones") with the same distribution as A to match the number of immediate sub-levels in L1. For example, if there are three sub-levels included in L1, GNU MCSim creates two clones to form a total of three instances of A (the original and its two clones). This convention saves a lot writing and effort in the long run.
In the sample of code given above, the parameter A, defined at the
top level, will be simply moved to sub-level 1 (cloning is not necessary
since there is only on sub-level directly included in the top level).
Within sub-level 1, the normally-distributed A will be cloned once
in order to create another normal variate with the same
distribution. Each one of those two will be moved to a lower
Simulation
, where they will be conditioned by the data of that
simulation only. A total of three variables of "type" A will be
sampled and will be printed in the output file (coded so that the
position in the hierarchy is apparent): the "parent" A(1), a
priori uniformly distributed, and two "dependents" A(1.1)
and A(1.2), a priori normally distributed around
A(1).
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Data()
specificationExperimental observations of model variables, inputs, outputs, or
parameters, can be specified with the Data()
command. Markov
chain Monte Carlo sampling requires that you specify Data()
statements (see section MCMC()
specification; see section Setting-up statistical models). The data are then used internally to evaluate
the likelihood function for the model. The arguments are the name of
the variable for which observations exist, and a comma-separated list
of data values:
Data(<variable identifier>, <value1>, <value2>, ...); |
This specification can only be used with a matching Print()
or
PrintStep()
for the same variable (see section Print()
specification;
see section PrintStep()
specification). You must make sure that there are as
many data values in the Data()
specification as output time
requested in the corresponding Print()
or PrintStep()
. A
data value of "-1" is treated as "missing data" and ignored in
likelihood calculations. The convention "-1" can be changed by changing
INPUT_MISSING_VALUE in the header file ‘mc.h’ and
recompiling.
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The output from Monte Carlo or SetPoints
simulations is a
tab-delimited text file with one row for each run (i.e., parameter set)
and one column for each parameter and output in the order specified.
Thus each line of the output file is in the following order:
<# of run> <parameters> <outputs for Exp 1> <outputs for Exp2> ... |
The parameters are printed in the order they were sampled or set.
The first line gives the column headers. A variable called name requested for output in an simulation i at a time j is labeled name_i.j.
The output of Markov chain Monte Carlo simulations is also a text file with one row for each run. It displays a column of iteration labels, and one column for each parameter sampled. The last three columns contain respectively, the sum of the logarithms of each parameter’s density given its parents’ values (‘LnPrior’), the logarithm of the data likelihood (‘LnData’), and the sum of the previous two values (‘LnPosterior’). The first line gives the column headers. On this line, parameters names are tagged with a code identifying their position in the hierarchy defined by the Level sections. For example, the second instance of a parameter called name placed at the fist level of the hierarchy is labeled name(2); the first instance of the same parameter placed at the second instance of the second level of the hierarchy is labeled name(2.1), etc.
The tab-delimited file can easily be imported into your favorite spreadsheet, graphic or statistical package for further analysis.
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If integration fails for a imulation
in DefaultSim
simulations no output is generated for that simulation, and the user is
warned by an error message on the screen. In MonteCarlo
or
SetPoints
simulations, the corresponding simulation line is not
printed, but the iteration number is incremented. Finally, in
MCMC simulations, the parameter for which the data likelihood was
computed is simply not updated (which implicitly forbids the
uncomputable region of the parameter space). In all cases an error
message is given on the screen, or wherever the screen output has been
redirected.
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The following mistakes are particularly easy to make, and sometimes hard to notice, or understand at first.
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XMCSim is a menu-driven interface which automatizes the
compilation and running tasks of GNU MCSim. It also offers a
convenient interface to 2-D and 3-D plotting of the simulation
results. Note that you need XWindows
, Tcl/Tk
and
wish
installed to run XMCSim. xemacs
is also
recommended.
Just type xmcsim
at the command promt. A windows appear, with
a menu bar. Menu items are:
File
, which allows you to choose an existing model file or to
exit the program. Once you have chosen a model file, its file name
appears as a reminder at the bottom of the window.
Edit
, which calls xemacs
for you to create a new model
file or edit any file of your choice (for example an input or output
file). Note: if you do not have xemacs
installed you can change
the file ‘xmcsim’ to replace the call to xemacs
by a call to
your editor.
Compile
has two items: Compile model
will compile
the current model file or prompt you for one and will call mod
to
generate a ‘model.c’ file from it; Compile mcsim
will
first call mod
and will then go on to create an executable mcsim
filevia a call to makemcsim
create an executable program.
Run
with three items: Run
which will prompt you for
an executable mcsim file, an input file and an output file (the latter
is optional) and will then launch the executable; Stop
will
just stop a running executable; Debug
will produce a
standalone executable with a name starting with ‘debugmcsim’ and
will launch xemacs
for you (you will then need to call
gdb
or another debugger by yourself; if you find a way to
start gdb on an executable via xemacs on the command line please
tell me...).
Plot
will start an Xgnuplot-based interface to gnuplot
An Help
menu available there to guide you further in the
arcanes of gnuplot
, but we recommend that you also browse
gnuplot
documentation.
At some point GNU MCSim may do symbolic computations, wash dishes, clothes and cars, and write poems, but for now, that’s all, folks!
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Amzal B., Bois F., Parent E. and Robert C.P. (2006). Bayesian optimal design via interacting MCMC, Journal of the American Statistical Association, 101:773-785.
Barry T.M. (1996). Recommendations on the testing and use of pseudo-random number generators used in Monte Carlo analysis for risk assessment. Risk Analysis 16:93-105.
Bernardo J.M. and Smith A.F.M. (1994). Bayesian Theory. Wiley, New York.
Bois F.Y., Gelman A., Jiang J., Maszle D., Zeise L. and Alexeef G. (1996). Population toxicokinetics of tetrachloroethylene. Archives of Toxicology 70:347-355.
Bois F.Y., Smith T.J., Gelman, A., Chang H.Y., Smith A.E. (1999). Optimal design for a study of butadiene toxicokinetics in humans. Toxicological Sciences 49:213-224.
Bois F.Y., Zeise L. and Tozer T.N. (1990). Precision and sensitivity analysis of pharmacokinetic models for cancer risk assessment: tetrachloroethylene in mice, rats and humans. Toxicology and Applied Pharmacology 102:300-315.
Calderhead B. and Girolami M. (2009). Estimating Bayes factors via thermodynamic integration and population MCMC. Computational Statistics and Data Analysis 53:4028-4045.
Gear C.W. (1971a). Algorithm 407 - DIFSUB for solution of ordinary differential equations [D2]. Communications of the ACM 14:185-190.
Gear C.W. (1971b). The automatic integration of ordinary differential equations. Communications of the ACM 14:176-179.
Gelman A. (1992). Iterative and non-iterative simulation algorithms. Computing Science and Statistics 24:433-438.
Gelman A., Bois F.Y. and Jiang J. (1996). Physiological pharmacokinetic analysis using population modeling and informative prior distributions. Journal of the American Statistical Association 91:1400-1412.
Gelman A., Carlin J.B., Stern H.S. and Rubin D.B. (1995). Bayesian Data Analysis. Chapman & Hall, London.
Gelman A. and Rubin D.B. (1992). Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7:457-511.
Geyer C.J. and Thompson E.A. (1995). Annealing Markov chain Monte Carlo with applications to ancestral inference. Journal of the American Statistical Association90: 909-920.
Gilks W.R., Richardson S. and Spiegelhalter D.J. (1996). Markov Chain Monte Carlo In Practice. Chapman & Hall, London.
Hammersley J.M. and Handscomb D.C. (1964). Monte Carlo Methods. Chapman and Hall, London.
Manteufel R.D. (1996). Variance-based importance analysis applied to a complex probabilistic performance assessment. Risk Analysis 16:587-598.
Park S.K. and Miller K.W. (1988). Random number generators: good ones are hard to find. Communications of the ACM 31:1192-1201.
Press W.H., Flannery B.P., Teukolsky S.A. and Vetterling W.T. (1989). Numerical Recipes (2nd ed.). Cambridge University Press, Cambridge.
Smith A.F.M. (1991). Bayesian computational methods. Philosophical Transactions of the Royal Society of London, Series A 337:369-386.
Smith A.F.M. and Roberts G.O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society Series B 55:3-23.
Vattulainen I., Ala-Nissila T. and Kankaala K. (1994). Physical tests for random numbers in simulations. Physical Review Letters 73:2513-2516.
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You should use the following reserved keywords as prescribed when building your models and input files:
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You will find here some examples of model description files and simulation input files.
B.1 ‘linear.model’ | a simple algebraic model | |
B.2 ‘1cpt.model’: A sample model description file | a one-compartment pharmacokinetic model | |
B.3 ‘perc.model’: A sample model description file | a multi-compartment pharmacokinetic model | |
B.4 ‘perc.lsodes.in’ | a sample simulation input file |
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# Linear Model with a random component # y = A + B * time + N(0,SD_true) # Setting SD_true to zero gives the deterministic version #--------------------------------------------------------- # Outputs Outputs = {y}; # Model Parameters A = 0; B = 1; SD_true = 0; SD_esti = 0; CalcOutputs { y = A + B * t + NormalRandom(0,SD_true); } |
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# One Compartment Model # First order input and output #--------------------------------------------------------- # Inputs Inputs = {Dose}; # Outputs Outputs = {C_central, AUC, ln_C_central, ln_AUC, SD_C_computed, SD_A_computed}; # Model Parameters ka = 1; ke = 0.5; F = 1; V = 2; # Statistical Parameters SDb_ka = 0; SDw_ka = 0; SDb_ke = 0; SDw_ke = 0; SDb_V = 0; min_F = 0; max_F = 0; SD_C_central = 0; SD_AUC = 0; CV_C_cen = 0; CV_AUC = 0; CV_C_cen_true = 0; CV_AUC_true = 0; # Calculate Outputs CalcOutputs { # algebraic equation for C_central C_central = (ka != ke ? (exp(-ke * t) - exp(-ka * t)) * F * ka * Dose / (V * (ka - ke))): exp(-ka * t) * ka * t * F * Dose / V); # algebraic equation for AUC AUC = (ka != ke ? ((1 - exp(-ke * t)) / ke - (1 - exp(-ka * t)) / ka) * F * ka * Dose / (V * (ka - ke))) : F * Dose * (1 - (1 + ka * t) * exp(-ka * t)) / (V * ke)); C_central = C_central + NormalRandom(0, C_central * CV_C_cen_true); AUC = AUC + NormalRandom(0, AUC * CV_AUC_true); ln_C_central = (C_central > 0 ? log (C_central) : -100); ln_AUC = (AUC > 0 ? log (AUC) : -100); SD_C_computed = (C_central > 0 ? C_central * CV_C_cen : 1e-10); SD_A_computed = (AUC > 0 ? AUC * CV_AUC : 1e-10); } # End of output calculations End. |
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#--------------------------------------------------------- # perc.model # A four compartment model of Tetrachloroethylene (PERC) # and total metabolites. #--------------------------------------------------------- # States are quantities of PERC and metabolite formed, they can be # output States = {Q_fat, # Quantity of PERC in the fat Q_wp, # ... in the well-perfused compartment Q_pp, # ... in the poorly-perfused compartment Q_liv, # ... in the liver Q_exh, # ... exhaled Qmet}; # Quantity of metabolite formed # Extra outputs are concentrations at various points Outputs = {C_liv, # mg/l in the liver C_alv, # ... in the alveolar air C_exh, # ... in the exhaled air C_ven, # ... in the venous blood Pct_metabolized, # % of the dose metabolized C_exh_ug}; # ug/l in the exhaled air Inputs = {C_inh} # Concentration inhaled # Constants # Conversions from/to ppm: 72 ppm = .488 mg/l PPM_per_mg_per_l = 72.0 / 0.488; mg_per_l_per_PPM = 1/PPM_per_mg_per_l; #--------------------------------------------------------- # Nominal values for parameters # Units: # Volumes: liter # Vmax: mg / minute # Weights: kg # Km: mg / minute # Time: minute # Flows: liter / minute #--------------------------------------------------------- InhMag = 0.0; Period = 0.0; Exposure = 0.0; C_inh = PerDose (InhMag, Period, 0.0, Exposure); LeanBodyWt = 55; # lean body weight # Percent mass of tissues with ranges shown Pct_M_fat = .16; # % total body mass Pct_LM_liv = .03; # liver, % of lean mass Pct_LM_wp = .17; # well perfused tissue, % of lean mass Pct_LM_pp = .70; # poorly perfused tissue, recomputed in scale # Percent blood flows to tissues Pct_Flow_fat = .09; Pct_Flow_liv = .34; Pct_Flow_wp = .50; # will be recomputed in scale Pct_Flow_pp = .07; # Tissue/blood partition coeficients PC_fat = 144; PC_liv = 4.6; PC_wp = 8.7; PC_pp = 1.4; PC_art = 12.0; Flow_pul = 8.0; # Pulmonary ventilation rate (minute volume) Vent_Perf = 1.14; # ventilation over perfusion ratio sc_Vmax = .0026; # scaling coeficient of body weight for Vmax Km = 1.0; # The following parameters are calculated from the above values in # the Scale section before the start of each simulation. # They are left uninitialized here. BodyWt = 0; V_fat = 0; # Actual volume of tissues V_liv = 0; V_wp = 0; V_pp = 0; Flow_fat = 0; # Actual blood flows through tissues Flow_liv = 0; Flow_wp = 0; Flow_pp = 0; Flow_tot = 0; # Total blood flow Flow_alv = 0; # Alveolar ventilation rate Vmax = 0; # kg/minute #--------------------------------------------------------- # Dynamics # Define the dynamics of the simulation. This section is # calculated with each integration step. It includes # specification of differential equations. #--------------------------------------------------------- Dynamics { # Venous blood concentrations at the organ exit Cout_fat = Q_fat / (V_fat * PC_fat); Cout_wp = Q_wp / (V_wp * PC_wp); Cout_pp = Q_pp / (V_pp * PC_pp); Cout_liv = Q_liv / (V_liv * PC_liv); # Sum of Flow * Concentration for all compartments dQ_ven = Flow_fat * Cout_fat + Flow_wp * Cout_wp + Flow_pp * Cout_pp + Flow_liv * Cout_liv; # Venous blood concentration C_ven = dQ_ven / Flow_tot; # Arterial blood concentration # Convert input given in ppm to mg/l to match other units C_art = (Flow_alv * C_inh / PPM_per_mg_per_l + dQ_ven) / (Flow_tot + Flow_alv / PC_art); # Alveolar air concentration C_alv = C_art / PC_art; # Exhaled air concentration C_exh = 0.7 * C_alv + 0.3 * C_inh / PPM_per_mg_per_l; # Differentials dt (Q_exh) = Flow_alv * C_alv; dt (Q_fat) = Flow_fat * (C_art - Cout_fat); dt (Q_wp) = Flow_wp * (C_art - Cout_wp); dt (Q_pp) = Flow_pp * (C_art - Cout_pp); # Quantity metabolized in liver dQmet_liv = Vmax * Q_liv / (Km + Q_liv); dt (Q_liv) = Flow_liv * (C_art - Cout_liv) - dQmet_liv; # Metabolite formation dt (Qmet) = dQmet_liv; } # End of Dynamics #--------------------------------------------------------- # Scale # Scale certain model parameters and resolve dependencies # between parameters. Generally the scaling involves a # change of units, or conversion from percentage to actual # units. #--------------------------------------------------------- Scale { # Volumes scaled to actual volumes BodyWt = LeanBodyWt/(1 - Pct_M_fat); V_fat = Pct_M_fat * BodyWt/0.92; # density of fat = 0.92 g/ml V_liv = Pct_LM_liv * LeanBodyWt; V_wp = Pct_LM_wp * LeanBodyWt; V_pp = 0.9 * LeanBodyWt - V_liv - V_wp; # 10% bones # Calculate Flow_alv from total pulmonary flow Flow_alv = Flow_pul * 0.7; # Calculate total blood flow from the alveolar ventilation rate and # the V/P ratio. Flow_tot = Flow_alv / Vent_Perf; # Calculate actual blood flows from total flow and percent flows Flow_fat = Pct_Flow_fat * Flow_tot; Flow_liv = Pct_Flow_liv * Flow_tot; Flow_pp = Pct_Flow_pp * Flow_tot; Flow_wp = Flow_tot - Flow_fat - Flow_liv - Flow_pp; # Vmax (mass/time) for Michaelis-Menten metabolism is scaled # by multiplication of bdw^0.7 Vmax = sc_Vmax * exp (0.7 * log (LeanBodyWt)); } # End of model scaling #--------------------------------------------------------- # CalcOutputs # The following outputs are only calculated just before values # are saved. They are not calculated with each integration step. #--------------------------------------------------------- CalcOutputs { # Fraction of TCE metabolized per day Pct_metabolized = (InhMag ? Qmet / (1440 * Flow_alv * InhMag * mg_per_l_per_PPM): 0); C_exh_ug = C_exh * 1000; # milli to micrograms } # End of output calculation End. |
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#--------------------------------------------------------- # perc.lsodes.in # #--------------------------------------------------------- Integrate (Lsodes, 1e-4, 1e-6, 1); #--------------------------------------------------------- # The following is a simulation of one of Dr. Monster's # exposure experiments described in "Kinetics of Tetracholoroethylene # in Volunteers; Influence of Exposure Concentration and Work Load," # A.C. Monster, G. Boersma, and H. Steenweg, # Int. Arch. Occup. Environ. Health, v42, 1989, pp303-309 # # The paper documents measurements of levels of TCE in blood and # exhaled air for a group of 6 subjects exposed to # different concentrations of PERC in air. # # Inhalation is specified as a dose of magnitude InhMag for the # given Exposure time. # # Inhalation is given in ppm #--------------------------------------------------------- Simulation { InhMag = 72; # ppm Period = 1e10; # Only one dose Exposure = 240; # 4 hour exposure # measurements before end of exposure # and at [5' 30'] 2hr 18 42 67 91 139 163 Print (C_exh_ug, 239.9 245 270 360 1320 2760 4260 5700 8580 10020 ); Print (C_ven, 239.9 360 1320 2760 4260 5700 8580 10020 ); } END. |
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