This version of Metatool consists partly of script files which are compatible with octave and Matlab and partly of shared libraries that are specific for the operating system and math program. In principle the script files provide all necessary functionality, but usage of the shared libraries makes elementary mode calculation far more efficient. The purpose of this distribution is to make the operations performed by Metatool more easily understandable and to allow the users to quickly adapt and extend the scripts according to their needs. Caveat: Due to currently sparse documentation and too few commentaries in the scripts this may not be so easy after all.
Extract the common files (common-files.tar.gz) and platform/program specific files (see table below) into one directory. Do not mix the specific files for the different math programs in one directory.
GNU octave 2.1.72 (api-v13) |
MATLAB 6 or higher | |
Linux x86 | octave-specific-files.tar.gz | matlab-linux-specific-files.tar.gz |
Windows | n/a | matlab-windows-specific-files.tar.gz |
Mac
OS X |
n/a |
matlab-mac-specific-files.tar.gz |
The Linux libraries require libstdc++.so.5 and libc.so.6 as well as some other standard libraries (type e.g. 'ldd elmo.oct' for the octave installation to check if all dependencies are resolved).
If you want to use the Metatool option for calculation of elementary modes in FluxAnalyzer, you need to download the appropriate MATLAB-specific files for your operating system. Then extract the elmo.* shared library from the archive and copy it into the main installation directory of FluxAnalyzer. In addition, you have to put the kernel.m function from the common files into this directory.
The standard Metatool
input format is described here.
Start your math program
and cd into the directory where you have placed the files (or tell
the program to include the directory in its search path). Then
execute:
ex= parse('example.dat');
This will read the input file and store its content into several fields of the
data structure 'ex' (the field names are: 'st', 'irrev_react', 'ext', 'ext_met',
'int_met', 'react_name'; cf. table below).
If you get an error when executing this command, look in
the known problems section below. The 'parse' command alone is useful when you
want to manipulate the input data before the calculations. You can now call
ex= metatool(ex);
to perform the calculations or if you want both steps in one go you can
directly call
ex= metatool('example.dat');
This will perform most of
the operations that the stand-alone Metatool does and stores the
results in the variable 'ex'. If you run metatool with a second
argument as in
ex= metatool('example.dat', 'example.out');
then it will produce an ouput file similar to the one of the
stand-alone version. There are two drawbacks with this kind of output: First, it
is not possible to load this output later again. Second, for large
systems the process of writing this file is very time-consuming. Therefore it is often
better to use the built-in functions of the math program to save and
load your data.
The variable 'ex' (or whatever you choose to call
it) is a structure that contains several fields, the most important
of which are:
st | stoichiometric matrix (rows correspond to internal metabolites, columns to reactions) |
irrev_react | row vector which contains 0 for a reversible and 1 for an irreversible reaction |
kn | kernel (nullspace) of the stoichiometric matrix |
sub | subset matrix (rows correspond to the subsets, columns to the reactions in st) |
rd | reduced system |
irrev_rd | reversibility of the subsets in the reduced system |
rd_ems | elementary modes of the reduced system (rows correspond to the subsets, columns are elementary modes) |
irrev_ems | row vector which contains 0 for a reversible and 1 for an irreversible elementary mode |
ext | same structure as st, but rows correspond to external metabolites |
int_met | names of the internal metabolites |
ext_met | names of the external metabolites |
react_name | names of the reactions |
When you call metatool with a second argument so
that an output file is produced, there will also be a field calles
'ems' in the return variable. This field contains the elementary
modes of the full system. When you run metatool without producing an
output file, the elementary modes for the full system are not
produced. The reason for this is to save memory since the the number
of elementary modes in large/complex networks can explode. If you
want to expand the elementary modes to the original system do
this:
ex.ems= ex.sub' * ex.rd_ems;
Of course, this will produce
one large matrix. But you can also choose which modes to expand by
using the standard indexing mechanisms (cf. math program
documentation), e.g.
ems_part= ex.sub' * ex.rd_ems(:, 1:10);
will
only expand the first ten elementary modes. To examine the numerical
quality of the result you can look at something likes this:
max(max(abs(ex.rd * ex.rd_ems)))
Ideally, the result should be zero or a very small number. If all your
stoichiometric coefficients were integers and the result is not 0,
something went wrong.
In case you don't have your network as a metatool
input file but want to calculate the elementary modes for a
stoichiometric matrix directly, this is also possible. You simply
have to set up a variable with the two fields 'st' and 'irrev_react'
in the same way as described in the table above. Here is an example
how to calculate the elementary modes of a network with two
metabolites and four reactions:
net.st= [1 1 -1 0; 0 0 1 -1];
net.irrev_react= [1 0 1 1];
net= metatool(net);
After calculation, net.rd_ems contains the elementary modes of the
reduced
system. Since no reaction and metabolite names were given, no
metatool output can be produced.
Because octave and Matlab are not 100% compatible, there are some
parts
in the scripts where it is necessary to determine which math program is
used. This is done by setting the global variable 'MATH_PROGRAM' to '0'
for octave and '1' for Matlab. However, there are slight differences in
the various octave and Matlab versions concerning the declaration and
definition of global variables. As a result, the determination of the
math program can fail leading to different consecutive errors. If you
encounter an error when running Metatool, set the global variable
'MATH_PROGRAM' manually and retry. In order to do this, you first have
to declare:
global MATH_PROGRAM
and then assign this variable '0' if you are using octave and '1' if
you are using Matlab.
The Metatool input files are in ASCII format and it is essential that the linefeed encoding used by them corresponds to conventions of the operating system you are working on. To convert between UNIX and DOS formats use the unix2dos and dos2unix utilities. In addition, the last line in the input file must end with a linefeed.
S. Klamt, J. Gagneur, A. von Kamp: New algorithmic approaches
for computing
elementary modes in large biochemical reaction networks, IEE Systems
Biology, in press.
T. Pfeiffer, I. Sánchez-Valdenebro, J. C. Nuño, F.
Montero
and S. Schuster:METATOOL:
For Studying
Metabolic Networks.
Bioinformatics 15, 1999, 251-257 (describes important concepts, but not
the current implementation).
S. Schuster, D. Fell and T. Dandekar: A General Definition of
Metabolic Pathways
Useful for Systematic Organization and Analysis of Complex Metabolic
Networks. Nature Biotechnology 18 (3), 2000, 326-332. PubMed
(general introduction to
elementary modes analysis; the algorithm described in this paper is not
used for the current
implementation).
R. Urbanczik, C. Wagner:An
improved algorithm for stoichiometric network analysis: theory and
applications. Bioinformatics 21, 2005, 1203-1210 (basis for the
null space algorithm used in the current implementation).
If you have any questions or comments and especially
when you encounter errors please tell me.
Last updated: 19.7.2006