SBMLChecker a Semantic approach for SBML
SBMLChecker, a Semantic approach for SBML model reliability evaluation.
Mathialakan Thavappiragasam, Carol M. Lushbough, Etienne Z. Gnimpieba
Computer Science Department, University of South Dakota, 414 E. Clark St. Vermillion, SD 57069, USA,
{Mathialakan.Thavappi; Carol.Lushbough; Etienne.Gnimpieba}@usd.edu
ABSTRACT
In Systems Biology model design, reliability
evaluation constitutes a requirements challenge. In
order to apply the models on a given process or on
work for in silico study, a systems biologist needs
to be ensured of the models quality. The key
problem remains the relation between the model
and the biologist question. Several algorithms was
designed to validate models but they only check
correctness of syntax (e.g. Online SBML
validator). These algorithms do not consider
semantic annotation of a model defining biological
context of the model. In our approach we have
measured the model reliability using a combination
of meaning (semantic) and syntax. This approach
allows researcher to identify a model that really fits
his needs and application domain. It also provides
unique identification to each model element
(compound, reaction, and compartment) in order to
facilitate any Systems Biology operation such as
merging, splitting, and simulation. It is
implemented in Java and connected to the model
database BIOMODELS using Restful API, our
algorithm implementation called SBMLChecker is
available
online
at
http://jacksons.usd.edu/SBMLC/. The command
line version has been deployed on BioExtract
server, at bioextract.org that it to be integrated in
automatic sharable scientific workflow.
Keywords: semantic, syntax, annotated URL id,
SBML, biological model
1. INTRODUCTION
System Biology Markup Language (SBML) is the
common format to represent a Biosystem
mathematical model. Used by over 250 tools
(SBML.org), it remains lacking in many aspects in
order to provide the appropriate model in the right
context. The reliability of a model depends
considerably on the context related to the model
design. The development of semantic annotation of
biological elements allows systems biologists to
connect design context (domain ontology) to a
model.
Semantic in biological modeling
There are several organizations (EBI [2],
NCBI [3]) maintaining databases ( Biomodels [2],
Protein, Gene, etc.) and/or ontologies (gene
ontology [4]) in order to manage biological
components (e.g., reaction, species, etc.) in a
standard way. They try to categorize the already
defined components and identify relationships
among them. Each database assigns unique id to
each element and keeps tracking relevant details
(e.g. properties, description) with these ids.
Furthermore, we can find several web applications
(e.g., KEGG Mapper) that provide services to map
the same components from different places [5].
Some of them provide web services, especially
RESTful services, that could be used by software
tool developers (web services for GO terms and
annotation provided by EBML-EBI) [6]. A single
component can be annotated by multiple databases
and or ontologies. The SBML defines annotationtag to annotate biological components, it has
resources with the details of database and id for
each annotation [7]. E.g. the reaction MTHFR,
[5,10-methylene-tetrahydrofolate] + [NADPH] →
[5-methyl-tetrahydrofolate] in BIOMD0000000018
has the annotations "urn:miriam:ec-code:1.5.1.20",
"urn:miriam:kegg.reaction:R01224". This reaction
has Enzyme id 1.5.1.20 and KEGG reaction id is
R01224.
SBML reliability evaluation in existing tools
(Online SBML Validator)
The model reliability checking should ensure
their correctness on both syntax and semantic
(meaning). The Online SBML validator introduced
by SBML.org provides the services to test syntax
and internal consistency of an SBML model. This
system checks the following aspects of a model
[1]:
Consistency of measurement units associated
with quantities (SBML L2V4 rules 105nn)
Correctness and consistency of identifiers used
for model entities (SBML L2V4 rules 103nn)
Syntax of MathML mathematical expressions
(SBML L2V4 rules 102nn)
Validity of SBO identifiers (if any) used in the
model (SBML L2V4 rules 107nn)
Perform static analysis of whether the model is
over determined
Perform additional checks for recommended
good modeling practices
Perform all other general SBML consistency
checks (SBML L2V4 rules 2nnnn; highly
recommended)
However, this system does not consider the
entire annotation information to evaluate the
meaning of models. In order to analyze the
semantics and syntax of models, we have designed
a tool that extends the web services provided by
the online SBML validator
2. METHOD
Principle
The reliability level of a model is calculated
based on its validity of its syntax and semantics.
Correctness of models on syntax is examined with
the usage of web services provided by the online
SBML validator. Semantic strength is measured by
the annotated URL id of each model’s component.
Design and algorithms for SBMLChecker
SBMLChecker does two way analysis, one for
semantic strength and another for syntax
correctness (Figure 5.), and generates reports R1,
R2 respectively.
Figure 1. Global mechanism for model reliability evaluation
Figure 2. Algorithm for semantic analysis
The semantic analyzer takes each kind of
component separately and identifies all ontologies
and databases that are used to annotate it. For
example, if any species is annotated with KEGG
id, KEGG will be used to check the annotation of
every remaining species. Then the percentage of
KEGG annotation will be calculated. Species are
considered to be more consistent/reliable if the
percentage is high. In this way, percentage of every
possible annotated ontologies/databases will be
calculated. The maximum percentage will decide
the best consistency level of the model element
(e.g. species) in the resource (e.g. KEGG,
MIRIAM Register)
Reliability score estimation
The consistency for the components ( ) of
kind k over
ontologies and/or databases,
))
where is an ith ontology or database.
Finally, cumulative consistency is calculated
by taking the average consistencies of each kind of
component. Consistency of model m,
∑
∑
where the number of components,
.
In addition to the consistency report, an error
report is generated by combining the online SBML
validator’s error report with our own semantic
check error report. Based on the quality checking,
it will suggest to provide a valid model for any
relevant applications such as model comparison
and integration, but it can be skipped if they want.
Implementation of SBMLChecker
We used the IDE NetBeans (7.3) to fulfill
everything related to coding, and the JSBML
library (jsbml-0.8-with-depenedencies) was used to
manipulate SBML files [8]. The JDK 1.7 java
library were used for this development [9].
Furthermore, the library to handle excel file:
apache poi, and any other relevant libraries were
included.
The JSBML is a flexible and entirely javabased library for working with SBML. This library
supports all SBML Levels and Versions through
Level 3 Version 1, and it maintains the highest
possible degree of compatibility with the popular
library libSBML [10]. JSBML also supports
modules that can facilitate the development of
plugins for end user applications, as well as ease
migration from a libSBMLbased backend.
reaction 44%. The model’s reliability can be
improved by annotating it.
Validation
The model BIOMD0000000018 is examined
for reliability by SBMLChecker. According to the
results, it is syntactically valid and earned a
semantic score of 79%. This semantic score comes
from: compartment 100%, species 93%, and
3. APPLICATION
SBMLChecker in a Workflow Management
System (bioextract.org)
Figure 3 SBMLChecker on BioExtract server for the reliability checking of biomodels
A Java program named SBMLChecker.jar is
designed for reliability checking. This can process
SBML files received through command line
parameter argument, and writes a generated report
in excel, text, and xml formats. The SBMLChecker
has been deployed on the HPC (High Performance
Figure 4 SBMLChecker on the web portal
Computing) infrastructure iPlant for availability on
BioExtract server (Figure 3), and has been
integrated on a web portal (Figure 4).
4. CONCLUSION
SBMLChecker provide a novel approach in
SBML model reliability measurement. Using a
combination of the meaning (semantic) and the
syntax, we generate a reliability score that can be
used as indicator to interpret the output result from
a given model in specific context. This approach
also provides a unique identification to each model
element (compound, reaction, and compartment) in
order to facilitate any Systems Biology operation
such
as
merging,
splitting,
simulation.
Implemented in Java and connected to the model
database BIOMODELS using Restful API,
SBMLChecker is available online for small models
and available on Bioextract.org for workflow
design and big models.
Funding: This work was made possible by SDINBRE Grant #P20RR016479-09 from the
National Center for Research Resources (NCRR),
a component of the National Institutes of Health
(NIH). Its contents are solely the responsibility of
the authors and do not necessarily represent the
official views of NCRR or NIH.
[5]
REFERENCES
[6]
[1]
[2]
[3]
[4]
View publication stats
SBML.org, “SBML Online Validator.” [Online].
Available:
http://sbml.org/Facilities/Validator/.
[Accessed: 10-Apr-2014].
EBML-EBI, “BioModels Database,” 2014. [Online].
Available:
http://www.ebi.ac.uk/biomodels-main/.
[Accessed: 10-Apr-2014].
U. S. N. L. of Medicine, “NCBI.” [Online].
Available: http://www.ncbi.nlm.nih.gov/. [Accessed:
10-Mar-2014].
J. A. Blake, M. Dolan, H. Drabkin, D. P. Hill, N. Li,
D. Sitnikov, S. Bridges, S. Burgess, T. Buza, F.
McCarthy, D. Peddinti, L. Pillai, S. Carbon, H.
Dietze, A. Ireland, S. E. Lewis, C. J. Mungall, P.
Gaudet, R. L. Chrisholm, P. Fey, W. A. Kibbe, S.
Basu, D. A. Siegele, B. K. McIntosh, D. P. Renfro, A.
E. Zweifel, J. C. Hu, N. H. Brown, S. Tweedie, Y.
Alam-Faruque, R. Apweiler, A. Auchinchloss, K.
Axelsen, B. Bely, M.-C. Blatter, C. Bonilla, L.
Bouguerleret, E. Boutet, L. Breuza, A. Bridge, W. M.
[7]
[8]
[9]
[10]
Chan, G. Chavali, E. Coudert, E. Dimmer, A.
Estreicher, L. Famiglietti, M. Feuermann, A. Gos, N.
Gruaz-Gumowski, R. Hieta, C. Hinz, C. Hulo, R.
Huntley, J. James, F. Jungo, G. Keller, K. Laiho, D.
Legge, P. Lemercier, D. Lieberherr, M. Magrane, M.
J. Martin, P. Masson, P. Mutowo-Muellenet, C.
O’Donovan, I. Pedruzzi, K. Pichler, D. Poggioli, P.
Porras Millán, S. Poux, C. Rivoire, B. Roechert, T.
Sawford, M. Schneider, A. Stutz, S. Sundaram, M.
Tognolli, I. Xenarios, R. Foulgar, J. Lomax, P.
Roncaglia, V. K. Khodiyar, R. C. Lovering, P. J.
Talmud, M. Chibucos, M. G. Giglio, H.-Y. Chang, S.
Hunter, C. McAnulla, A. Mitchell, A. Sangrador, R.
Stephan, M. A. Harris, S. G. Oliver, K. Rutherford,
V. Wood, J. Bahler, A. Lock, P. J. Kersey, D. M.
McDowall, D. M. Staines, M. Dwinell, M.
Shimoyama, S. Laulederkind, T. Hayman, S.-J.
Wang, V. Petri, T. Lowry, P. D’Eustachio, L.
Matthews, R. Balakrishnan, G. Binkley, J. M. Cherry,
M. C. Costanzo, S. S. Dwight, S. R. Engel, D. G.
Fisk, B. C. Hitz, E. L. Hong, K. Karra, S. R.
Miyasato, R. S. Nash, J. Park, M. S. Skrzypek, S.
Weng, E. D. Wong, T. Z. Berardini, E. Huala, H. Mi,
P. D. Thomas, J. Chan, R. Kishore, P. Sternberg, K.
Van Auken, D. Howe, and M. Westerfield, “Gene
Ontology annotations and resources.,” Nucleic Acids
Res., vol. 41, no. Database issue, pp. D530–5, Jan.
2013.
M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M.
Tanabe, “KEGG for integration and interpretation of
large-scale molecular data sets.,” Nucleic Acids Res.,
vol. 40, no. Database issue, pp. D109–14, Jan. 2012.
EBML-EBI, “QuickGO.” [Online]. Available:
http://www.ebi.ac.uk/QuickGO/. [Accessed: 03-Oct2013].
M. Hucka, L. Smith, D. Wilkinson, F. Bergmann, S.
Hoops, S. Keating, S. Sahle, and J. Schaff, “The
Systems Biology Markup Language (SBML):
Language Specification for Level 3 Version 1 Core,”
Nat. Preced., Oct. 2010.
A. Dräger, N. Rodriguez, M. Dumousseau, A. Dörr,
C. Wrzodek, N. Le Novère, A. Zell, and M. Hucka,
“JSBML: a flexible Java library for working with
SBML.,” Bioinformatics, vol. 27, no. 15, pp. 2167–8,
Aug. 2011.
O. Corporation, “JavaTM Platform, Standard Edition 7
Development
Kit.”
[Online].
Available:
http://www.oracle.com/technetwork/java/javase/jdk7-readme-429198.html. [Accessed: 01-Jun-2013].
B. J. Bornstein, S. M. Keating, A. Jouraku, and M.
Hucka, “LibSBML: an API library for SBML.,”
Bioinformatics, vol. 24, no. 6, pp. 880–1, Mar. 2008.
Mathialakan Thavappiragasam, Carol M. Lushbough, Etienne Z. Gnimpieba
Computer Science Department, University of South Dakota, 414 E. Clark St. Vermillion, SD 57069, USA,
{Mathialakan.Thavappi; Carol.Lushbough; Etienne.Gnimpieba}@usd.edu
ABSTRACT
In Systems Biology model design, reliability
evaluation constitutes a requirements challenge. In
order to apply the models on a given process or on
work for in silico study, a systems biologist needs
to be ensured of the models quality. The key
problem remains the relation between the model
and the biologist question. Several algorithms was
designed to validate models but they only check
correctness of syntax (e.g. Online SBML
validator). These algorithms do not consider
semantic annotation of a model defining biological
context of the model. In our approach we have
measured the model reliability using a combination
of meaning (semantic) and syntax. This approach
allows researcher to identify a model that really fits
his needs and application domain. It also provides
unique identification to each model element
(compound, reaction, and compartment) in order to
facilitate any Systems Biology operation such as
merging, splitting, and simulation. It is
implemented in Java and connected to the model
database BIOMODELS using Restful API, our
algorithm implementation called SBMLChecker is
available
online
at
http://jacksons.usd.edu/SBMLC/. The command
line version has been deployed on BioExtract
server, at bioextract.org that it to be integrated in
automatic sharable scientific workflow.
Keywords: semantic, syntax, annotated URL id,
SBML, biological model
1. INTRODUCTION
System Biology Markup Language (SBML) is the
common format to represent a Biosystem
mathematical model. Used by over 250 tools
(SBML.org), it remains lacking in many aspects in
order to provide the appropriate model in the right
context. The reliability of a model depends
considerably on the context related to the model
design. The development of semantic annotation of
biological elements allows systems biologists to
connect design context (domain ontology) to a
model.
Semantic in biological modeling
There are several organizations (EBI [2],
NCBI [3]) maintaining databases ( Biomodels [2],
Protein, Gene, etc.) and/or ontologies (gene
ontology [4]) in order to manage biological
components (e.g., reaction, species, etc.) in a
standard way. They try to categorize the already
defined components and identify relationships
among them. Each database assigns unique id to
each element and keeps tracking relevant details
(e.g. properties, description) with these ids.
Furthermore, we can find several web applications
(e.g., KEGG Mapper) that provide services to map
the same components from different places [5].
Some of them provide web services, especially
RESTful services, that could be used by software
tool developers (web services for GO terms and
annotation provided by EBML-EBI) [6]. A single
component can be annotated by multiple databases
and or ontologies. The SBML defines annotationtag to annotate biological components, it has
resources with the details of database and id for
each annotation [7]. E.g. the reaction MTHFR,
[5,10-methylene-tetrahydrofolate] + [NADPH] →
[5-methyl-tetrahydrofolate] in BIOMD0000000018
has the annotations "urn:miriam:ec-code:1.5.1.20",
"urn:miriam:kegg.reaction:R01224". This reaction
has Enzyme id 1.5.1.20 and KEGG reaction id is
R01224.
SBML reliability evaluation in existing tools
(Online SBML Validator)
The model reliability checking should ensure
their correctness on both syntax and semantic
(meaning). The Online SBML validator introduced
by SBML.org provides the services to test syntax
and internal consistency of an SBML model. This
system checks the following aspects of a model
[1]:
Consistency of measurement units associated
with quantities (SBML L2V4 rules 105nn)
Correctness and consistency of identifiers used
for model entities (SBML L2V4 rules 103nn)
Syntax of MathML mathematical expressions
(SBML L2V4 rules 102nn)
Validity of SBO identifiers (if any) used in the
model (SBML L2V4 rules 107nn)
Perform static analysis of whether the model is
over determined
Perform additional checks for recommended
good modeling practices
Perform all other general SBML consistency
checks (SBML L2V4 rules 2nnnn; highly
recommended)
However, this system does not consider the
entire annotation information to evaluate the
meaning of models. In order to analyze the
semantics and syntax of models, we have designed
a tool that extends the web services provided by
the online SBML validator
2. METHOD
Principle
The reliability level of a model is calculated
based on its validity of its syntax and semantics.
Correctness of models on syntax is examined with
the usage of web services provided by the online
SBML validator. Semantic strength is measured by
the annotated URL id of each model’s component.
Design and algorithms for SBMLChecker
SBMLChecker does two way analysis, one for
semantic strength and another for syntax
correctness (Figure 5.), and generates reports R1,
R2 respectively.
Figure 1. Global mechanism for model reliability evaluation
Figure 2. Algorithm for semantic analysis
The semantic analyzer takes each kind of
component separately and identifies all ontologies
and databases that are used to annotate it. For
example, if any species is annotated with KEGG
id, KEGG will be used to check the annotation of
every remaining species. Then the percentage of
KEGG annotation will be calculated. Species are
considered to be more consistent/reliable if the
percentage is high. In this way, percentage of every
possible annotated ontologies/databases will be
calculated. The maximum percentage will decide
the best consistency level of the model element
(e.g. species) in the resource (e.g. KEGG,
MIRIAM Register)
Reliability score estimation
The consistency for the components ( ) of
kind k over
ontologies and/or databases,
))
where is an ith ontology or database.
Finally, cumulative consistency is calculated
by taking the average consistencies of each kind of
component. Consistency of model m,
∑
∑
where the number of components,
.
In addition to the consistency report, an error
report is generated by combining the online SBML
validator’s error report with our own semantic
check error report. Based on the quality checking,
it will suggest to provide a valid model for any
relevant applications such as model comparison
and integration, but it can be skipped if they want.
Implementation of SBMLChecker
We used the IDE NetBeans (7.3) to fulfill
everything related to coding, and the JSBML
library (jsbml-0.8-with-depenedencies) was used to
manipulate SBML files [8]. The JDK 1.7 java
library were used for this development [9].
Furthermore, the library to handle excel file:
apache poi, and any other relevant libraries were
included.
The JSBML is a flexible and entirely javabased library for working with SBML. This library
supports all SBML Levels and Versions through
Level 3 Version 1, and it maintains the highest
possible degree of compatibility with the popular
library libSBML [10]. JSBML also supports
modules that can facilitate the development of
plugins for end user applications, as well as ease
migration from a libSBMLbased backend.
reaction 44%. The model’s reliability can be
improved by annotating it.
Validation
The model BIOMD0000000018 is examined
for reliability by SBMLChecker. According to the
results, it is syntactically valid and earned a
semantic score of 79%. This semantic score comes
from: compartment 100%, species 93%, and
3. APPLICATION
SBMLChecker in a Workflow Management
System (bioextract.org)
Figure 3 SBMLChecker on BioExtract server for the reliability checking of biomodels
A Java program named SBMLChecker.jar is
designed for reliability checking. This can process
SBML files received through command line
parameter argument, and writes a generated report
in excel, text, and xml formats. The SBMLChecker
has been deployed on the HPC (High Performance
Figure 4 SBMLChecker on the web portal
Computing) infrastructure iPlant for availability on
BioExtract server (Figure 3), and has been
integrated on a web portal (Figure 4).
4. CONCLUSION
SBMLChecker provide a novel approach in
SBML model reliability measurement. Using a
combination of the meaning (semantic) and the
syntax, we generate a reliability score that can be
used as indicator to interpret the output result from
a given model in specific context. This approach
also provides a unique identification to each model
element (compound, reaction, and compartment) in
order to facilitate any Systems Biology operation
such
as
merging,
splitting,
simulation.
Implemented in Java and connected to the model
database BIOMODELS using Restful API,
SBMLChecker is available online for small models
and available on Bioextract.org for workflow
design and big models.
Funding: This work was made possible by SDINBRE Grant #P20RR016479-09 from the
National Center for Research Resources (NCRR),
a component of the National Institutes of Health
(NIH). Its contents are solely the responsibility of
the authors and do not necessarily represent the
official views of NCRR or NIH.
[5]
REFERENCES
[6]
[1]
[2]
[3]
[4]
View publication stats
SBML.org, “SBML Online Validator.” [Online].
Available:
http://sbml.org/Facilities/Validator/.
[Accessed: 10-Apr-2014].
EBML-EBI, “BioModels Database,” 2014. [Online].
Available:
http://www.ebi.ac.uk/biomodels-main/.
[Accessed: 10-Apr-2014].
U. S. N. L. of Medicine, “NCBI.” [Online].
Available: http://www.ncbi.nlm.nih.gov/. [Accessed:
10-Mar-2014].
J. A. Blake, M. Dolan, H. Drabkin, D. P. Hill, N. Li,
D. Sitnikov, S. Bridges, S. Burgess, T. Buza, F.
McCarthy, D. Peddinti, L. Pillai, S. Carbon, H.
Dietze, A. Ireland, S. E. Lewis, C. J. Mungall, P.
Gaudet, R. L. Chrisholm, P. Fey, W. A. Kibbe, S.
Basu, D. A. Siegele, B. K. McIntosh, D. P. Renfro, A.
E. Zweifel, J. C. Hu, N. H. Brown, S. Tweedie, Y.
Alam-Faruque, R. Apweiler, A. Auchinchloss, K.
Axelsen, B. Bely, M.-C. Blatter, C. Bonilla, L.
Bouguerleret, E. Boutet, L. Breuza, A. Bridge, W. M.
[7]
[8]
[9]
[10]
Chan, G. Chavali, E. Coudert, E. Dimmer, A.
Estreicher, L. Famiglietti, M. Feuermann, A. Gos, N.
Gruaz-Gumowski, R. Hieta, C. Hinz, C. Hulo, R.
Huntley, J. James, F. Jungo, G. Keller, K. Laiho, D.
Legge, P. Lemercier, D. Lieberherr, M. Magrane, M.
J. Martin, P. Masson, P. Mutowo-Muellenet, C.
O’Donovan, I. Pedruzzi, K. Pichler, D. Poggioli, P.
Porras Millán, S. Poux, C. Rivoire, B. Roechert, T.
Sawford, M. Schneider, A. Stutz, S. Sundaram, M.
Tognolli, I. Xenarios, R. Foulgar, J. Lomax, P.
Roncaglia, V. K. Khodiyar, R. C. Lovering, P. J.
Talmud, M. Chibucos, M. G. Giglio, H.-Y. Chang, S.
Hunter, C. McAnulla, A. Mitchell, A. Sangrador, R.
Stephan, M. A. Harris, S. G. Oliver, K. Rutherford,
V. Wood, J. Bahler, A. Lock, P. J. Kersey, D. M.
McDowall, D. M. Staines, M. Dwinell, M.
Shimoyama, S. Laulederkind, T. Hayman, S.-J.
Wang, V. Petri, T. Lowry, P. D’Eustachio, L.
Matthews, R. Balakrishnan, G. Binkley, J. M. Cherry,
M. C. Costanzo, S. S. Dwight, S. R. Engel, D. G.
Fisk, B. C. Hitz, E. L. Hong, K. Karra, S. R.
Miyasato, R. S. Nash, J. Park, M. S. Skrzypek, S.
Weng, E. D. Wong, T. Z. Berardini, E. Huala, H. Mi,
P. D. Thomas, J. Chan, R. Kishore, P. Sternberg, K.
Van Auken, D. Howe, and M. Westerfield, “Gene
Ontology annotations and resources.,” Nucleic Acids
Res., vol. 41, no. Database issue, pp. D530–5, Jan.
2013.
M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M.
Tanabe, “KEGG for integration and interpretation of
large-scale molecular data sets.,” Nucleic Acids Res.,
vol. 40, no. Database issue, pp. D109–14, Jan. 2012.
EBML-EBI, “QuickGO.” [Online]. Available:
http://www.ebi.ac.uk/QuickGO/. [Accessed: 03-Oct2013].
M. Hucka, L. Smith, D. Wilkinson, F. Bergmann, S.
Hoops, S. Keating, S. Sahle, and J. Schaff, “The
Systems Biology Markup Language (SBML):
Language Specification for Level 3 Version 1 Core,”
Nat. Preced., Oct. 2010.
A. Dräger, N. Rodriguez, M. Dumousseau, A. Dörr,
C. Wrzodek, N. Le Novère, A. Zell, and M. Hucka,
“JSBML: a flexible Java library for working with
SBML.,” Bioinformatics, vol. 27, no. 15, pp. 2167–8,
Aug. 2011.
O. Corporation, “JavaTM Platform, Standard Edition 7
Development
Kit.”
[Online].
Available:
http://www.oracle.com/technetwork/java/javase/jdk7-readme-429198.html. [Accessed: 01-Jun-2013].
B. J. Bornstein, S. M. Keating, A. Jouraku, and M.
Hucka, “LibSBML: an API library for SBML.,”
Bioinformatics, vol. 24, no. 6, pp. 880–1, Mar. 2008.