Biology Reference
In-Depth Information
data and knowledge of the structure and function of molecular systems is still rudimentary. Furthermore,
the experimental data available in molecular databases have a high error rate, while biological knowledge
has a high rate of uncertainty. Therefore, only modelling and simulation and methods of artificial
intelligence will suffice to discuss arising important questions. Such formal description can be used to
specify a simulation environment. Therefore, modelling and simulation can be interpreted as the basic
step for implementing virtual worlds that allow virtual experiments.
As already mentioned more than 500 database and information systems are available, which represent
molecular knowledge today. Furthermore, a lot of analysis tools and simulation environments are
available. That means that basic components of the electronical infrastructure for the implementation of
a virtual cell are present. The concepts and tools which are available in the literature and the Internet are
based on specific questions, such as the gene regulation process phenomena, or the biochemical process
control. To solve current questions, we have to implement integrative tools (Integrative Bioinformatics)
which can be used finally to implement a virtual cell. If we take a look at the Internet, we can see
that only online representations of cellular illustrations, taken directly from topics, are available today
(
http://www.life.uiuc.edu/plantbio/cell/
)
. One of the first implementations is the E-Cell project of M.
Tomita (
www.e-cell.org
). Many new virtual cell projects are following the E-Cell project. Regarding the
different methods for modelling and simulation of metabolic pathways we can divide these tools into two
classes. The classical methods are members of the so called analytical class. All these tools are based on
the theory of differential equations and try to realize the exact molecular simulation. The main argument
against this class is that we do not have the dynamic molecular data. This was the main argument for a
lot of different scientists coming from different research areas to develop discrete models. These models
are based on the theory of formal languages, automata, objects, rules, expert systems etc. However, a
few of these models are also hybrid models. Until now it is not clear which kind of model will be the best
to help to implement the virtual cell. Exactly 10 years ago M. Mavrovouniotis and colleagues presented
the first paper using Petri nets for this important application [Reddy
et al
., 1993]. In this paper they
used only simple case condition systems for simulation of simple biochemical processes. During the last
decade a lot of deeper papers were published using this method of simulation of metabolic networks.
The advantages of this method are:
1. Deep theory and results are available,
2. Powerful simulation shells are available and
3. Petri nets can combine both classes in a simple way.
Point three is very important because using higher Petri nets we are able to place differential equations
to the arcs of the model. That means that we are able to expand a discrete to an analytical model at any
time.
REFERENCE
•
Reddy, V. N., Mavrovouniotis, M. L. and Liebman, M. N. (1993). Petri net representations in metabolic pathways. Proc.
Int. Conf. Intell. Syst. Mol. Biol.
1
, 328-336.
