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#### Two different approaches for modeling biochemical reaction networks with ProMoT

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##### Citation

Mirschel, S., Steinmetz, K., & Kremling, A. (2009). Two different approaches for
modeling biochemical reaction networks with ProMoT. In I. Troch, & F. Breitenecker (*Proceedings / MATHMOD 09: 6th Vienna Conference on Mathematical Modelling* (pp. 2322-2330).

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-9311-6

##### Abstract

In recent years modeling of reaction systems tend to be more complex. Especially in systems biology researchers are faced with large-scale and highly interconnected networks that have to be mapped on
various types of mathematical models. Handling of such systems requires efficient and flexible computing tools
as well as a powerful modeling concept to describe the system under investigation. The modeling tool ProMoT provides an unique framework for setup, editing and visualization of systems which are relevant in chemical engineering and systems biology.
ProMoT is based on a modular modeling concept which is well suited to describe modular structures of technical
and biological systems. This concept is based on network theory that provides elementary modeling entities like
compounds or single reactions. The elementary modeling entities can be aggregated to higher structural elements
like chemical apparatus or overall pathways in systems biology. The modular modeling concept was originally applied to a quantitative modeling approach and also recently extended to include a qualitative modeling approach, frequently used in systems biology.
Setting up and managing models can be done using either a textual or diagrammatic user interface in ProMoT.
In order to analyze or simulate models, ProMoT is tightly coupled with several simulation and analysis tools in
order to get a closed environment which results in an optimized modeling workflow. ProMoT is also linked with
many other tools via SBML, the standard exchange format in the area of systems biology.
Modeling based on DAE systems. The quantitative modeling approach in ProMoT has its origin in chemical
engineering and is also capable to describe cellular processes in systems biology such as intracellular metabolic or
signaling processes. Elementary modeling entities are substance storages and substance transformers, representing single compounds and biochemical reactions, respectively. The resulting equations that allow to simulate the system are based on mass balances. However, in many cases, model reduction can be performed that allow to
reduce the number of dynamic state variables by setting up algebraic relationships between the compounds. For
instance the processes of signal transduction are in general much faster than the processes of protein synthesis
and therefore its dynamics can be neglected. The resulting differential and algebraic equations (DAE systems) are
provided automatically by ProMoT.
Models that are set up with ProMoT finally can be exported to various simulation engines such as Matlab,
DIVA or its successor Diana. Furthermore, a different set of kinetic parameters can be reloaded in ProMoT, e. g.
after parameter estimation with DIVA.
Modeling based on a logical modeling formalism. Besides the quantitative approach ProMoT supports also a
logical (Boolean) modeling formalism which results in a qualitative description of the system. This approach
is well suited to describe systems with a large number of components and considerably less information about the
dynamics. In most cases there is a lack of kinetic parameters. Moreover, experimental data may be available only
at discrete values (a protein is present or not or if protein A is active then also protein B is active). In this case,
the approach of logical modeling is more appealing.
Substances, represented through compounds can be connected with logical gates like AND, OR and NOT to build
up the logical network. Editing can be done in the ProMoT Visual Editor where predefined library elements
can be placed using drag&drop functionality. Afterwards the elements can be connected by links. In order to
analyze these logical models, ProMoT provides the export of the models to CellNetAnalyzer in the form
of logical equations coupled with a graphical representation. The analysis results of CellNetAnalyzer can be
re-imported in ProMoT and visualized in an intuitive way.
The approach is applied to a number of projects dealing with signal transduction networks (i. e. Epidermal Growth
Factor Receptor, T-Cell Receptor Signaling). For a convenient and user-friendly setup of logical models, ProMoT
provides a library containing the basic components, short-cuts for efficient and less error-prone work, validation for
structural modeling errors while editing and special visual representations which are used to represent the logical
model. This is of great value in order to interpret and discuss specific results in interdisciplinary teams.