Biology Reference
In-Depth Information
Biochemical pathways can be interpreted as complex graphs. Each node represents a metabolite
and each edge represents a biochemical reaction which is catalyzed by specific molecular structures.
Therefore, the application of the theory of Petrinets seems to be useful. The Petrinet application of
biochemical reactions was introduced by Reddy
et al.
1993 [9]. In this paper it is shown that Petrinets
can easily simulate qualitative biochemical reactions. The problem of this presentation [9] is that gene
regulation processes cannot be simulated. It is not possible to simulate the kinetic effect. The first gap
could be closed, showing that different classes of conditions can be interpreted as genes, proteines, or
enzymes [10]. Using this formalization, cell communication and gene regulation can easily be simulated.
Moreover, the simulation of kinetic effects and the feedback control of biochemical reactions is very
important. In this paper we present the extension of our formalization [10], which allows the quantitative
modeling of regulatory biochemical networks.
METABOLIC ENGINEERING
Metabolic engineering is the improvement of cellular activities by manipulating the enzymatic transport
and the regulatory functions of the cell with the use of DNA recombination technology [4]. The
opportunity to introduce heterologous genes and regulatory elements distinguishes metabolic engineering
from traditional genetic approaches to improve the strain. Metabolic engineering includes manipulation
of protein processing pathways as well as of pathways involving smaller metabolites. At present,
metabolic engineering is more a collection of examples than a codified science. The main features of
metabolic engineering can be subdivided into two parts: the theoretical and the practical part. The
synthesis and creating of new products or new reactants and the synthesis of hybrid metabolic networks
belong to the practical part. In this presentation, the theoretical part of metabolic engineering will be
discussed. Biochemical data has to be stored by using integrative database systems. Moreover, specific
analysis algorithms have to be implemented. The main task is to develop and implement interactive
simulation environments, which allow the quantitative discussion of metabolic processes. Therefore,
integrative simulation environments must be implemented, which allow the simulation of gene regulation,
biosynthesis, and cell communication.
The recombination of phenotypes and features in organisms can be carried out by using methods
of DNA recombination. In the first step specific genes, which represent the desirable phenotype (for
example body length), have to be isolated and integrated into a specific genome (e.g. Ti-plasmid). Gene
transfer into the organism will be realized by infection of the vector molecule. This recombinant process
can produce the corresponding phenotype of this gene. A well known example is the so-called 'super
mouse' [11], which contains the growth gene of the rat. By expressing this gene, the body length of the
'super mouse' is double the length of a mouse. A popular research field is to identify genetic defects,
which will produce metabolic diseases. To repair such defects by using methods of biotechnology is the
main task of human genetics. The first step is to identify and modify defect genes. The main problem
is the regulation of genetic activity. This is the reason that cellular control mechanisms are analyzed in
the field of molecular genetics and biotechnology. Metabolism represents a highly connected system of
biochemical reactions, gene regulation mechanisms, and cell communication processes. Therefore, the
main task is to develop new models and simulation shells which will allow to modify complex metabolic
processes.
