Biology Reference
In-Depth Information
of visualization is the consideration of multi-dimensional data annotations in a way suitable for the
information discovery process.
The mentioned data sources provide an established basis for the modeling and characterization of
a biomedical system. The data integration is a powerful feature of VANESA that provides many
possibilities. Furthermore, graph comparison and graph theory functions support the user in a better
understanding of biological circumstances. Highlighting and comparison functions point out important
facts in a set of different models. In order to make the graphical representation and the analysis on the
networks more legible, graph layout transformations and animation algorithms are considered as well.
Another important feature of VANESA is that information is visualized in a clear and understandable
manner to meet the purposes of underlying research activities. With an intuitive graphical user interface,
the user is enabled to record research results and thoughts in the form of a digital network model. The
user is not limited to any kind of biological model; moreover it is possible to create an individual system
that meets the requirements of each research activity.
The reconstructed quorum sensing network is constructed using 11 different life science data sources
(UniProt, KEGG, OMIM, GO, ENZYME, BRENDA, PDB, MINT, SCOP, EMBL-Bank, and Pub-Chem)
and experimental information. The mentioned data sources provide an established basis for the modeling
and the characterization of the underlying biological system.
Due to the availability of information from these data sources and the use of VANESA we were able to
construct and extend the proposed quorum sensing network (Fig. 5). The network creation was started
with the proposed quorum sensing system represented in Fig. 1. The network was created step by step,
adding all relevant biological elements. In reference to the research activities the following elements
were added to the network: enzymes, proteins, receptors, transcription factors, sRNAs, small molecules
and pathway maps.
As a whole, the mentioned elements characterize the quorum sensing system and form the fundamentals
of the network. During the development of the network all elements were put into relation with each
other to demonstrate the involved biological processes. The biological elements are either connected
through activation, dissociation, reaction or binding/association edges. The type of the edges is derived
from database information and experimental data.
After creating the network, the integrated biological databases were automatically checked for useful
information. Especially the databases KEGG and BRENDA were queried for information that might
be related to the network and its elements. Thanks to the BRENDA and KEGG database information it
was possible to extend the network with further biological elements and to compare it to other related
organisms. Additionally, the elements within the system were linked to database information. In the last
step we transformed the model into the language of Petri nets to simulate cell-to-cell communication
structures.
In order to model a qualitative quorum sensing Petri net, the quorum sensing network modeled by
VANESA was transformed into the Petri net language. For this purpose, an additional export function
for the software application Cell Illustrator (
http://cellillustrator.com
)
was implemented in VANESA. By
making use of the CSML export file format version 1.9 it is possible to transform the quorum sensing
network model constructed in VANESA into the Petri net language.
RESULTS
In the first term a first draft of the quorum sensing model was taken into account. Figure 5 shows the
modeled quorum sensing network which has been used for the Petri net representation. Exporting the
