Martin Ginkel

The systems biology markup language (SBML) : a medium for representation and exchange of biochemical network models

MOTIVATION Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY The specification of SBML Level 1 is freely available from http://www.sbml.org/

FluxAnalyzer: exploring structure, pathways, and flux distributions in metabolic networks on interactive flux maps

MOTIVATION The analysis of structure, pathways and flux distributions in metabolic networks has become an important approach for understanding the functionality of metabolic systems. The need of a user-friendly platform for stoichiometric modeling of metabolic networks in silico is evident. RESULTS The FluxAnalyzer is a package for MATLAB and facilitates integrated pathway and flux analysis for metabolic networks within a graphical user interface. Arbitrary metabolic network models can be composed by instances of four types of network elements. The abstract network model is linked with network graphics leading to interactive flux maps which allow for user input and display of calculation results within a network visualization. Therein, a large and powerful collection of tools and algorithms can be applied interactively including metabolic flux analysis, flux optimization, detection of topological features and pathway analysis by elementary flux modes or extreme pathways. The FluxAnalyzer has been applied and tested for complex networks with more than 500,000 elementary modes. Some aspects of the combinatorial complexity of pathway analysis in metabolic networks are discussed. AVAILABILITY Upon request from the corresponding author. Free for academic users (license agreement). Special contracts are available for industrial corporations. SUPPLEMENTARY INFORMATION http://www.mpi-magdeburg.mpg.de/projects/fluxanalyzer.

Dynamics of the direct methanol fuel cell (DMFC) : experiments and model-based analysis

Abstract A laboratory-scale liquid-feed direct methanol fuel cell (DMFC) was operated with different methanol feeding strategies. A proton exchange membrane (PEM) was used as the elecytrolyte. The cell voltage response to dynamic feeding of methanol revealed that a significant voltage increase can be obtained from dynamic changes in methanol feed concentration. The observed fuel cell behaviour was analysed with a mathematical model which consists of anode mass balances, charge balances of both electrodes and electrode kinetic expressions. Anode kinetics were derived from a four-step reaction mechanism with several intermediates bound to the catalyst surface. The model also accounts for the undesired cross-over of methanol, through the PEM, towards the cathode catalyst layer. First, the model is applied to predict steady-state current–voltage characteristics. Then, the cell voltage response to dynamic changes of methanol feed concentration is simulated. The simulated results are in full agreement to experimental observations. It turns out that methanol cross-over can be reduced by periodically pulsed methanol feeding.

Modular modeling of cellular systems with ProMoT/Diva

MOTIVATION Need for software to setup and analyze complex mathematical models for cellular systems in a modular way, that also integrates the experimental environment of the cells. RESULTS A computer framework is described which allows the building of modularly structured models using an abstract, modular and general modeling methodology. With this methodology, reusable modeling entities are introduced which lead to the development of a modeling library within the modeling tool ProMot. The simulation environment Diva is used for numerical analysis and parameter identification of the models. The simulation environment provides a number of tools and algorithms to simulate and analyze complex biochemical networks. The described tools are the first steps towards an integrated computer-based modeling, simulation and visualization environment Availability: Available on request to the authors. The software itself is free for scientific purposes but requires commercial libraries. SUPPLEMENTARY INFORMATION http://www.mpi-magdeburg.mpg.de/projects/promot

ProMoT : Modular Modeling for Systems Biology

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.

PROMOT: A Modeling Tool for Chemical Processes

The novel process modeling tool PROMOT supports the object-oriented modeling of chemical processes for the simulation environment DIVA. In PROMOT, differential-algebraic process models can be built by aggregating structural and behavioral modeling entities that represent the topological structure or the dynamic and steady-state behavior, respectively, of the investigated chemical processes. Process models and their modeling entities may be defined either in an object-oriented modeling language or with a graphical user interface. This paper discusses the modeling concept, the modeling language, the knowledge representation aspects, and the implementation of PROMOT.

Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules

MOTIVATION The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks de.ned as chemical systems. Such a decomposition would be very useful as most quantitative models are de.ned using the latter, more detailed formalism. RESULTS Here, we introduce a novel method to decompose biochemical networks into modules so that the bidirectional (retroactive) couplings among the modules are minimized. Our approach adapts a method to detect community structures, and applies it to the so-called retroactivity matrix that characterizes the couplings of the network. Only the structure of the network, e.g. in SBML format, is required. Furthermore, the modularized models can be loaded into ProMoT, a modeling tool which supports modular modeling. This allows visualization of the models, exploiting their modularity and easy generation of models of one or several modules for further analysis. The method is applied to several relevant cases, including an entangled model of the EGF-induced MAPK cascade and a comprehensive model of EGF signaling, demonstrating its ability to uncover meaningful modules. Our approach can thus help to analyze large networks, especially when little a priori knowledge on the structure of the network is available. AVAILABILITY The decomposition algorithms implemented in MATLAB (Mathworks, Inc.) are freely available upon request. ProMoT is freely available at http://www.mpi-magdeburg.mpg.de/projects/promot. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A model library for membrane reactors implemented in the process modelling tool ProMoT

Abstract A model library for spatially distributed membrane reactor models is presented. The structure of the library is guided by a general modelling concept for the systematic formulation of chemical engineering process models. Therefore, the building blocks in the library can also be used to model related processes like fixed bed reactors or adsorption columns. The library is implemented in the process modelling tool (P ro M o T), whose key features are presented. The use of the library is illustrated by the example of the selective oxidation of hydrocarbons in two types of membrane reactors.

Visual setup of logical models of signaling and regulatory networks with ProMoT

BackgroundThe analysis of biochemical networks using a logical (Boolean) description is an important approach in Systems Biology. Recently, new methods have been proposed to analyze large signaling and regulatory networks using this formalism. Even though there is a large number of tools to set up models describing biological networks using a biochemical (kinetic) formalism, however, they do not support logical models.ResultsHerein we present a flexible framework for setting up large logical models in a visual manner with the software tool ProMoT. An easily extendible library, ProMoTs inherent modularity and object-oriented concept as well as adaptive visualization techniques provide a versatile environment. Both the graphical and the textual description of the logical model can be exported to different formats.ConclusionNew features of ProMoT facilitate an efficient set-up of large Boolean models of biochemical interaction networks. The modeling environment is flexible; it can easily be adapted to specific requirements, and new extensions can be introduced. ProMoT is freely available from http://www.mpi-magdeburg.mpg.de/projects/promot/.

The ProMoT/Diana Simulation Environment

Abstract We introduce the object-oriented modeling tool ProMoT and the simulation environment Diana suitable for numerical analysis of problems in chemical engineering and systems biology. The key aspects of this environment are flexible structured models, and efficient modular numerical kernel, and the use of the scripting language Python as a powerful command line interface. The implementation is based on CAPE-OPEN interfaces to allow a modular software design and easy extensions of the system. The contribution discusses the design and implementation rationale of the simulation environment.