Preface - kmutt

KMUTT Annual Research Abstracts 2005
begins with the fixation of CO 2 from atmosphere
to the Calvin cycle. Passing through a series of
reactions, triose phosphate from Calvin cycle is
converted to sucrose which is transported to sink
cells and is eventually formed the amylose and
amylopectin (starch constituents). After
validating the model with data from a number of
literatures, the results show that the structured
model is a good representative of studied
system. The result of triose phosphate (DHAP
and GAP) elevation due to lessening the aldolase
activity is an illustration of validation.
Furthermore, the representative model will be
used to gain more understanding of starch
production process such as the effect of CO 2
uptake on qualitative and quantitative aspects of
starch biosynthesis.
IC-322 AN APPLIED PROCESS CONTROL
CONCEPT TO GENE MOTIF NETWORK
MODELING FOR PREDICTING THE GENE
EXPRESSION PROFILES
Saowalak Kalapanulak,
Natee Tang-amornsuksan, Asawin Meechai,
Supapon Cheevadhanarak,
Sakarindr Bhumiratana
Bioinfo 2005 (the 2005 International Joint
Conference of InCoB, AASBi and KSBI),
September 22-24, 2005, BEXCO, Busan, Korea
In this work, we applied the transfer
function commonly used in process control
theory to model and predict the expression of
genes within a given regulatory motif of
Saccharomyces cerevisiae. To demonstrate this
technique, the cell cycle microarray data of S.
cerevisiae at alpha-factor arrest condition
obtained from literatures were used. The transfer
function model of a feed-forward motif was
created using two methods; i.e, the modified
Gene Kim’s and the Control Station Software
methods. The former required to express the
array data as mathematical equations before
formulating the transfer function, whereas the
latter used the array data as input into the
software to obtain the function. In comparison,
the modified Gene Kim’s method provided a
better transfer function model. This model can
predict the expression data of CUP9 and
YJR100C genes of fkh1, fkh2 mutant S.
cerevisiae strains within 20% error. This model
can predict not only the expression of the target
genes but also the expression of the regulator
genes. Moreover, identification of the most
189
effective regulator genes in each network motif
is possible.
IC-323 PROBING STARCH BIOSYNTHESIS
ENZYME ISOFORMS BY VISUALIZATION
OF CONSERVED SECONDARY STRUCTURE
PATTERNS
Tayvich Vorapreeda, Weerayuth Kittichotirat,
Asawin Meechai, Sakarindr Bhumiratana,
Supapon Cheevadhanarak
Bioinfo 2005 (the 2005 International Joint
Conference of InCoB, AASBi and KSBI),
September 22-24, 2005, BEXCO, Busan, Korea
Starch is an indispensable raw material
in many industries. The utilization of starch
mostly depends on its physical and chemical
properties, which differ according to plant
origins and their varieties, and the manufacturers
often find them unsuitable for their industrial
requirement. These problems are often handled
by exposing the native form of starch to physical
or chemical modification processes in ordered to
tailor them appropriately according to the
industrial needs. However, both physical and
chemical modifications are cost intensive and
generate high chemical waste. Moreover, large
portion of starches are likely to be destroyed
during the process. Thus, a major challenge
would be to predict the effect of genetic changes
on the functional properties of starch to produce
designer starches that suit specific uses. The
enzymes in starch biosynthesis pathway
generally have a variety of isoforms that have
specific roles, which are believed to affect starch
properties, but their precise roles have not been
identified. Understanding of relationships
between enzyme isofoms in starch biosynthesis
processes and properties of starch granule is
therefore one of the main goals of plant
molecular biologists. Because of the difficulties
in obtaining experimental evidences of each
enzyme isoform, as they are usually instable,
hard to purify to homogeneity and exist in low
abundance, classifying them into families based
on the presence of shared features is one of the
common strategies for functional analysis.
Additionally since structures are more conserved
than sequences in evolution and with the
evidence that secondary structure agreement
between predicted secondary structures can be
used to identify distantly related protein
sequences, we present a novel computational
method for reclassifying starch enzyme isoforms
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