ProMoT: modular modeling for systems biology (original) (raw)
Journal Article
,
1Systems Biology Group, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany and 2Genedata AG, 4016 Basel, Switzerland
*To whom correspondence should be addressed.
Search for other works by this author on:
,
1Systems Biology Group, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany and 2Genedata AG, 4016 Basel, Switzerland
Search for other works by this author on:
,
1Systems Biology Group, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany and 2Genedata AG, 4016 Basel, Switzerland
Search for other works by this author on:
,
1Systems Biology Group, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany and 2Genedata AG, 4016 Basel, Switzerland
Search for other works by this author on:
1Systems Biology Group, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany and 2Genedata AG, 4016 Basel, Switzerland
Search for other works by this author on:
Received:
02 October 2008
Revision received:
18 December 2008
Accepted:
12 January 2009
Published:
15 January 2009
Cite
Sebastian Mirschel, Katrin Steinmetz, Michael Rempel, Martin Ginkel, Ernst Dieter Gilles, ProMoT: modular modeling for systems biology, Bioinformatics, Volume 25, Issue 5, March 2009, Pages 687–689, https://doi.org/10.1093/bioinformatics/btp029
Close
Navbar Search Filter Mobile Enter search term Search
Abstract
Summary: The modeling tool ProMoT facilitates the efficient and comprehensible setup and editing of modular models coupled with customizable visual representations. Since its last major publication in 2003, ProMoT has gained new functionality in particular support of logical models, efficient editing, visual exploration, model validation and support for SBML.
Availability: ProMoT is an open source project and freely available at http://www.mpi-magdeburg.mpg.de/projects/promot/.
Contact: mirschel@mpi-magdeburg.mpg.de; mirschel@mpi-magdeburg.mpg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
1 INTRODUCTION
Research in systems biology requires efficient and easy-to-use software tools for modeling and visualization. This gains even more importance as size and complexity of models continue to increase. A suitable way to efficiently handle such models is the decomposition into smaller, more manageable parts resulting in modular models. Such models are characterized by a tree of modules which provides a hierarchical structure for the content. The modular description is based on network theory which was adapted for application to model biological systems (Kremling et al., 2000). Network theory allows to specify elements (e.g. species) and coupling elements (e.g. reactions). Modular networks additionally consist of modules and interfaces between modules. Modules, interface elements, variables and equations are called modeling entities and constitute the modular modeling concept. ProMoT implements the modular modeling concept (Ginkel et al., 2003). Modeling entities can be extended by the user. Additionally, libraries containing modeling entities for specific application areas, such as biochemical reaction networks, logic-based signaling networks or chemical engineering are provided. Although different other modeling tools such as CellDesigner (Funahashi et al., 2008), JDesigner (Sauro et al., 2003) and Edinburgh Pathway Editor (Sorokin et al., 2006) also facilitate visual editing of models, they do not provide a modular approach for model setup.
ProMoT is implemented in Common Lisp and Java. It runs on Windows and Linux. Further system requirements, installation instructions, several tutorials and up-to-date versions can be found at http://www.mpi-magdeburg.mpg.de/projects/promot/.
2 DESCRIPTION
ProMoT supports models based on DAE (differential algebraic equation) systems and models based on a logical (Boolean) modeling formalism (Klamt et al., 2006; Saez-Rodriguez et al., 2006). Modeling in ProMoT is subdivided into several steps which are associated to different components. In the following the functionality of the ProMoT Browser, the ProMoT Visual Editor and the ProMoT Visual Explorer are introduced.
The ProMoT Browser facilitates loading and saving of models defined in ProMoT's own MDL format. It displays all loaded modeling entities (Fig. 1A), allows to copy or delete them and to create new ones. All other GUI components (e.g. for graphical editing and exploring) can be started from the ProMoT Browser. Models defined in SBML (Hucka et al., 2003) or CELLNETANALYZER (Klamt et al., 2007) format can be imported and exported. Furthermore, ProMoT models can be exported to the simulation environments Matlab (Fig. 1C), DIVA or Diana (Krasnyk et al., 2007). Export in formats which do not support modularity preserves the complete contents and discards all information about the original partition in modules. Prior to model export ProMoT validates and optimizes the model. Documentation for models can be generated in LaTeX or HTML format. For detailed information about different aspects of the export see Supplementary Material.
Fig. 1.
GUI components of ProMoT and associated modeling steps for two different modeling types—the dynamic model of the Epidermal Growth Factor Receptor (EGFR) by Saez-Rodriguez et al. (2005) (left) and the logical, signaling EGFR model taken from Samaga et al. (2009) The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data, submitted (right). Arrows indicate a feasible sequence of modeling steps. The ProMoT Browser shows loaded modeling entities (A). Models can be edited using the ProMoT Visual Editor (B, D). While editing a background syntax checking marks structural modeling errors (D). Models can be exported to different simulation environments (C) or analysis tools. Subsequently the analysis results from CELLNETANALYZER can be visualized on top of the network structure (E).
The ProMoT Visual Editor aims to setup and edit the model holistically. Hence, the topological structure of a module based on a graph metaphor as well as behavioral properties (variables and equations) should be graphically editable. Modeling entities can be placed and connected by links (Fig. 1B and D). Afterwards their variables can be altered using a variable editor. In previous versions, all properties could only be edited in the MDL code of the model. For models (e.g. imported SBML models without an initial layout) different layout algorithms are available which take advantage of ProMoT's inherent modularity. To provide model validation while editing, a background syntax check validates the model against structural modeling errors (see Supplementary Material and Fig. 1D). This is particularly elaborated for the logical modeling formalism. Additionally, editing shortcuts reduce the amount of user interactions and the probability to introduce errors in the modeling process.
The ProMoT Visual Explorer provides basically two functionalities—exploring and visualizing a modular model. The former uses context sensitive zooming through the different levels of modules and helps to get a quick view of edits or to search for specific components in context of the whole model. The latter allows to tailor visualizations specifically to users needs. The goal is twofold: first, it aims to reduce visual complexity by presenting only a relevant subset of the model. This results in a less cluttered and more descriptive visualization. Second, it provides adequate visual representations for the logical modeling formalisms supported by ProMoT. Among others it visually aids the user in specific modeling tasks, e.g. the visual interpretation of analysis results (Fig. 1E). The ProMoT Visual Explorer is also used to export the model representation into common bitmap, vector and graph formats.
3 CONCLUSIONS
ProMoT, a tool for efficient and comprehensible setup and editing of modular models is presented. A sophisticated graphical user interface assists the modeler in setup and management of modules and additionally includes a component for graphical zooming and flexible visual presentations. The described software is well integrated with external tools (e.g. CellNetAnalyzer, DIVA, Diana, Matlab) for model analysis and simulation. Moreover, ProMoT can be used as a command line tool which is helpful to start from external tools such as SYCAMORE (Weidemann et al., 2008). ProMoT is suitable for different application areas, e.g. dynamic models (Bettenbrock et al., 2006), logical models (Samaga et al., 2009, The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data, submitted) and synthetic biology (Marchisio et al., 2008).
ACKNOWLEDGEMENTS
The authors thank R. Samaga and J. Saez-Rodriguez who provided models. The authors are also thankful to anonymous reviewers for constructive comments.
Funding: German Ministry of Research and Education [HepatoSys grant 0313077 (to S.M. and M.R.), ForSys grant 0313922 (to K.S.)].
Conflict of Interest: None declared.
REFERENCES
et al.
A quantitative approach to catabolite repression in Escherichia coli
,
J. Biol. Chem.
,
2006
, vol.
281
(pg.
2578
-
2584
)
et al.
CellDesigner 3.5: a versatile modeling tool for biochemical networks
,
Proc. IEEE
,
2008
, vol.
96
(pg.
1254
-
1265
)
et al.
Modular modeling of cellular systems with ProMoT/Diva
,
Bioinformatics
,
2003
, vol.
19
(pg.
1169
-
1176
)
et al.
The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models
,
Bioinformatics
,
2003
, vol.
19
(pg.
524
-
531
)
et al.
A methodology for the structural and functional analysis of signaling and regulatory networks
,
BMC Bioinformatics
,
2006
, vol.
7
doi: 10.1186/1471-2105-7-56
et al.
Structural and functional analysis of cellular networks with CellNetAnalyzer
,
BMC Sys. Biol.
,
2007
, vol.
1
doi:10.1186/1752-0509-1-2
et al.
The ProMoT/Diana simulation environment
,
Proceeding of the 16th European Symposium on Computer Aided Process Engineering.
,
2006
Garmisch-Partenkirchen
Elsevier
(pg.
445
-
450
)
et al.
The organization of metabolic reaction networks: a signal-oriented approach to cellular models
,
Metab. Eng.
,
2000
, vol.
2
(pg.
190
-
200
)
Computational design of synthetic gene circuits with composable parts
,
Bioinformatics
,
2008
, vol.
24
(pg.
1903
-
1910
)
et al.
Dissecting the puzzle of life: modularization of signal transduction networks
,
Comput. Chem. Eng.
,
2005
, vol.
29
(pg.
619
-
629
)
et al.
Visual setup of logical models of signaling and regulatory networks with ProMoT
,
BMC BioInformatics
,
2006
, vol.
7
doi: 10.1186/1471-2105-7-506
et al.
Next generation simulation tools: the systems biology workbench and BioSPICE integration
,
OMICS
,
2003
, vol.
7
(pg.
355
-
372
)
et al.
The pathway editor: a tool for managing complex biological networks
,
IBM J. Res. Dev.
,
2006
, vol.
50
(pg.
561
-
573
)
et al.
SYCAMORE - a systems biology computational analysis and modeling research environment
,
Bioinformatics
,
2008
, vol.
24
(pg.
1463
-
1464
)
Author notes
Associate Editor: Alfonso Valencia
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Supplementary data
Citations
Views
Altmetric
Metrics
Total Views 1,785
1,439 Pageviews
346 PDF Downloads
Since 12/1/2016
Month: | Total Views: |
---|---|
December 2016 | 2 |
January 2017 | 1 |
February 2017 | 5 |
March 2017 | 2 |
May 2017 | 6 |
June 2017 | 7 |
July 2017 | 3 |
August 2017 | 9 |
October 2017 | 6 |
November 2017 | 4 |
December 2017 | 22 |
January 2018 | 23 |
February 2018 | 28 |
March 2018 | 18 |
April 2018 | 27 |
May 2018 | 15 |
June 2018 | 24 |
July 2018 | 22 |
August 2018 | 31 |
September 2018 | 25 |
October 2018 | 30 |
November 2018 | 25 |
December 2018 | 26 |
January 2019 | 22 |
February 2019 | 34 |
March 2019 | 32 |
April 2019 | 40 |
May 2019 | 45 |
June 2019 | 19 |
July 2019 | 40 |
August 2019 | 50 |
September 2019 | 39 |
October 2019 | 21 |
November 2019 | 28 |
December 2019 | 30 |
January 2020 | 19 |
February 2020 | 10 |
March 2020 | 29 |
April 2020 | 13 |
May 2020 | 14 |
June 2020 | 26 |
July 2020 | 21 |
August 2020 | 9 |
September 2020 | 24 |
October 2020 | 18 |
November 2020 | 9 |
December 2020 | 9 |
January 2021 | 3 |
February 2021 | 13 |
March 2021 | 15 |
April 2021 | 14 |
May 2021 | 4 |
June 2021 | 3 |
July 2021 | 12 |
August 2021 | 5 |
September 2021 | 16 |
October 2021 | 42 |
November 2021 | 19 |
December 2021 | 21 |
January 2022 | 26 |
February 2022 | 24 |
March 2022 | 21 |
April 2022 | 18 |
May 2022 | 15 |
June 2022 | 14 |
July 2022 | 30 |
August 2022 | 34 |
September 2022 | 57 |
October 2022 | 10 |
November 2022 | 29 |
December 2022 | 13 |
January 2023 | 21 |
February 2023 | 16 |
March 2023 | 42 |
April 2023 | 24 |
May 2023 | 15 |
June 2023 | 22 |
July 2023 | 10 |
August 2023 | 10 |
September 2023 | 5 |
October 2023 | 10 |
November 2023 | 19 |
December 2023 | 16 |
January 2024 | 18 |
February 2024 | 10 |
March 2024 | 18 |
April 2024 | 14 |
May 2024 | 19 |
June 2024 | 11 |
July 2024 | 19 |
August 2024 | 26 |
September 2024 | 14 |
October 2024 | 6 |
Citations
57 Web of Science
×
Email alerts
Citing articles via
More from Oxford Academic