Biology Reference
In-Depth Information
Wall chart representation
All analyzed biochemical reactions are collected by the Boehringer company [6]. Moreover, the
KEGG information system represents the static representation of this biochemical data [7]. Based on
that data our model allows the dynamic representation of biochemical pathways. In this chapter we
will discuss the representation of the glycolysis using our Petrinet model. This biochemical network
is a subset of the Boehringer pathway chart. The glycolysis is an important biochemical process
which allows the metabolic production of energy. Most of the biochemical reactions of glycolysis are
biochemical reactions, which are controlled by positive (metabolites) and negative components (ADP,
Insulin). However, the Petrinet modeling of biochemical processes makes regulation components visible.
In our Petrinet representation the inhibitor process of P-enol-pyruvate can be shown directly. Glycolysis
is a complex example which consists of eight reactions (transitions); different metabolites are connected.
Our Petrinet representation describes the biochemical process in direction of the glycolysis. The effects
of the enzymes are shown by bold arcs. The positive and negative influence of substances will be shown,
using bi-directional interrupt arcs.
CONCLUSION
An important task of Molecular Bioinformatics is to develop information systems for the simulation
of biochemical networks. Therefore, models have to be defined which are able to simulate biochemical
networks based on the static data representation ci7. A lot of different models are presented [2,8], but
we are still looking for a useful formalization which will solve this task. Petrinets belong to the class
of discrete models, which also allow quantitative analysis. Quantitative and qualitative simulations are
important in order to understand the molecular behavior of biochemical reactions.
Moreover, kinetic
effects can be studied directly using this method.
The first Petrinet approach for the simulation of metabolic pathways was presented by Reddy
et al.
1993 [9]. This approach is based on the condition event net and discusses qualitative aspects. Moreover,
positive and negative components are not included, and the dynamic behavior of biochemical reactions is
not represented. Using our approach, the modeling of metabolic networks is possible. This formalization
can be used, for example, for the dynamic representation of the Boehringer pathway chart [6], which
differs between two domains: the genetic pathways and the domain of metabolic reactions. The advantage
of our approach is:
-
the graphical representation is a model which corresponds to biochemical reactions,
-
the components of our model are substances (places) and reactions (transitions),
-
the relations between substances will be characterized by directed arcs.
Our qualitative model permits a biochemical reaction to consume and produce concentrations. This
biochemical behavior is similar to the pre- and post-conditions of Petrinets, and our definition of self-
modification permits the representation of kinetic effects. Moreover, consuming substances depend on
the actual concentration of substances, and the kinetic behavior can be discussed in detail using functions
as specific arrow weights. Quantitative changes can also be modeled by the modification of the structure
of the Petrinet which allows the discussion of the influence of specific substances. Moreover, the rates
of the places can be modified. By means of modifying the actual arrows, new reactions can be defined.
Our formalization is a parallel and discrete model which allows the quantitative simulation of metabolic
processes. In the research field of biotechnology and molecular medicine the quantitative simulation
