Biology Reference
In-Depth Information
well to the facts described in the literature. The purpose of this paper was to describe method to
construct biological pathways with the HFPN. Therefore we introduced the well-known glycolytic
pathway controlled by the lac operon gene regulatory mechanism.
Although this paper deals with a known biological phenomenon, with GON, we succeeded in dis-
covering one unknown biological phenomenon in multicellular systems [Matsuno
et al.,
2003a]. We
analyzed the mechanism of Notch-dependent boundary formation in the
Drosophila
large intestine, by
experimental manipulation of Delta expression and computational modeling and simulation by GON.
Boundary formation representing the situation in the normal large intestine was shown in the simulation.
By manipulating Delta expression in the large intestine, a few types of disorder in boundary cell differ-
entiation were observed, and similar abnormal patterns were generated by the simulation. Simulation
results suggest that parameter values representing the strength of cell-autonomous suppression of Notch
signaling by Delta are essential for generating two different modes of patterning: lateral inhibition and
boundary formation, which could explain how a common gene regulatory network results in two different
patterning modes
in vivo
.
We should emphasize that, essentially, any type of differential equations can be modeled with HFPN.
This means that GON has the potential to simulate biological pathway models for other biosimulation
tools such as E-Cell and Gepasi. More concretely, if the users have a skill of programming, they can
develop original functions as the Java class files, which can be called from the transitions or the arcs of
HFPN. This means that, any function of E-Cell or Gepasi can be included as Java class file in GON.
With GON, we have succeeded in modeling many kinds of biological pathways [
http://www.Genomic
Object.Net/
]
. However, at the same time, we recognized that the current notion of HFPN is still
insufficient to model more sophisticated biological pathways, including more complex information such
as localization, cell interaction, etc.
This is one of the reasons to motivate us to create a “hybrid functional Petri net with extension
(HFPNe)” by enhancing the concept of HFPN [Nagasaki
et al.,
2003]. The HFPNe allows more “types”
for places (integer, real, boolean, string, vector) with which complex information can be handled. In
other words, the definition of these types allow us to treat other existing Petri nets as subsets of the
HFPNe. For example, a traditional Petri net (with only discrete elements) is treated as the HFPNe using
only “integer type”. Furthermore, HFPNe can define a hybrid system of continuous and discrete events
together with a hierarchization of objects for an intuitive creation of complex objects. Genomic Object
Net is developed based on the notion of an HFPNe. The biological pathway models constructed with the
HFPNe will be reported in the near future.
ACKNOWLEDGEMENTS
This work was partially supported by the Grand-in-Aid for Scientific Research on Priority Areas
“Genome Information Science” from the Ministry of Education, Culture, Sports, Science and Technology
in Japan.
REFERENCES
•
Alberts, B., Bray, D., Lewis, J., Raff, M,. Roberts, K. and Watson J. (1994). The Molecular Biology of the Cell, Third
Edition. Garland Publishing, Inc, New York.
•
Alla, H. and David, R. (1998). Continuous and hybrid petri nets. Journal of Circuits, Systems, and Computers
8
, 159-188.
