Microbial biotechnology

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Feb 12, 2013 (4 years and 6 months ago)

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Current and Future in Pathway Research

국제

워크숍

개최
(e
-
Pathway)



?

행사명


Current and Future in Pathway Research

?

행사일시


2012.07.06 (

⤠ 9㨰0⁁M⁾ 6㨰0⁐M

?

행사장소


KISTI
본원

대강당

?

행사내용

○ Pathway
연구

분야

전문가

초청

강연



연구

교류

?

행사필요성

○ IT
-
BT
분야

학제간

융합

연구






성과

확산을

위한

글로벌

협력체제

구축




세계적으로

활발하게

추진되고

있는

패스웨이

구축
/
생성
/
활용

연구의

국내

활성화

계기를

마련




분야에

있어서의

국제적

연구개발

협력

네트워크

구축



?

행사

프로그램

Time

Title

Name & Affiliation

9:30
-
9:50

Opening

Won
-
Kyung Sung

(KISTI)

10:00
-
10:40

Linking pathways to literature: PathText

So
phia Ananiadou (NaCTeM,
Manchester Univ.)

10:40
-
11:20

Information in New Drug Discovery research

Sung
-
eun Yoo


(Choongnam Univ.)

11:20
-
12:00

The MetaCyc Family of Pathway/Genome Databases
and the Pathway Tools Software

Ingrid Keseler (BioCyc, SRI)

1
2
:
0
0
-
1
3
:
3
0

Lunch


13:30
-
14:10

e
-
Pathway: A Platform for the Autonomous
Generation and I ntegration of Biological Pathway

Sung
-
Pil Choi (KISTI)

14:
1
0
-
14:
5
0


Kousaku Okubo (DBCLS)

14:
5
0
-
15:
3
0

Systems Metabolic Engineering

Sang Yup Lee (KAIST)

1
5
:
3
0
-
15:
5
0

Cof
fee Break


15:50
-
16:30

KEGG: current status and its applications

Goto (KEGG, Kyoto Univ.)

16:30
-
17:10

Reactome


Linking pathways, networks and disease.

Robin Haw (Reactome)

17:10
-
17:30

Discussion & Closing

Won
-
Kyung Sung

(KISTI)













초청

연사

소개



발표

주제



Systems
Metabolic Engineering

Sang Yup Lee

Department of Chemical and Biomolecular Engineering

BioProcess Engineering Research Center and Bioinformatics Research Center

Center for Systems and Synthetic Biotechnology, Institute for the BioCent
ury

Korea Advanced Institute of Science and Technology (KAIST)

Daejeon 305
-
701, Korea (
leesy@kaist.ac.kr
)



T
here ha
s

recently
been much interest in developing sustainable system for the production of chemicals,
fu
els, and materials from renewable resources.
As

m
icroorganisms

isolated from nature
are oft
en
inefficient in performing
our

desired task
s
,
metabolic engineering
is

employed

for the improvement of
microbial performance.
In this lecture, metabolic engineerin
g based on quantitative pathway analysis will
be presented with accompanying examples of producing chemicals, fuels and materials
.
Focus will be
given on general strategies for systems metabolic
engineering

of microorganisms for
successful

bioprocess devel
opment.
[
Our

work
has been

supported by the
Technology Development Program to
Solve Climate Changes from the Ministry of Education, Science and Technology
.]


Sang Yup Lee

Sang Yup Lee

received B.S. in Chem. E. from Seoul National University in 1986, and Ph
.D. in Chem. E.
from Northwestern University in 1991. Currently, he
is Distinguished Professor
and
Dean of College of
Life Science and Bioengineering

at KAIST. He is also the

Director of Center for Systems and Synthetic
Biotechnology, Director of BioProces
s Engineering Research Center, and Director of Bioinformatics
Research Center. He has published more than
400

journal papers,
and numerous patents.

He
received
many

awards, including the National Order of Merit,
Citation Classic Award,

Elmer Gaden Award
, a
nd
Merck Metabolic Engineering Award. He is currently Fellow of
AAAS
, American Academy of
Microbiology,
Society for Industrial Microbiology and Biotechnology,
Korean Academy of Science and
Technology,

and National Academy of Engineering of Korea. He is als
o Foreign Associate of National
Academy of Engineering USA, Editor
-
in
-
Chief of Biotechnology Journal, and editor and board member
of many journals.
His research interests are

metabolic engineering,

systems

and

synthetic biology, and
industrial biotechnolog
y
.

Reactome


Linking pathways, networks and disease.


Robin Haw

Ontario Institute for Cancer Research, Informatics and
Bio

Computing, Toronto, ON, Canada



The Reactome Knowledgebase of human biological pathways and processes is
a curated and peer
-
revie
wed knowledgebase available online as an open access
resource that can be freely used and distributed by all members of the
biological research community. Geneticists, genomics and proteomics
researchers, clinicians, molecular biologists, bioinformaticians

and systems biologists use Reactome to
interpret high
-
throughput experimental datasets, to develop novel algorithms for data mining and
visualization, and to build predictive models of normal and abnormal pathways. The Reactome curation
system draws upon
the expertise of independent researchers who author precise machine
-
readable
descriptions of human pathways under the guidance of a team of curators. Pathway modules are
extensively checked to ensure factual accuracy and compliance with the data model, and

a system of
evidence tracking ensures that all assertions are backed by the primary literature. Recent extensions of our
data model accommodate the annotation of disease processes, allowing us to represent the altered
biological behavior of mutant variant
s frequently found in cancer, and to describe the mode of action and
specificity of anti
-
cancer therapeutics. Reactome pathways currently cover a quarter of the translated
portion of the genome, and are available on our web site for browsing, downloading,
and manipulation by
in
-
house and third party online analysis tools. To increase protein coverage and associated annotations,
we have extended our protein coverage by offering a network of “functional interactions” (FIs) predicted
by a conservative machine
-
learning approach, that add an additional 25% of the translated genome, for a
combined coverage of approximately 50%. We offer several analytical tools built upon the Reactome FI
network and have begun to demonstrate the network’s usefulness for the analys
is of genome
-
scale
datasets in human disease research.


Database URL:
http://www.reactome.org

Contact email: robin.haw@oicr.on.ca


Information in New Drug Discovery research


Sung
-
eun Yoo

Department of New Drug Disc
overy

Graduate School of Drug discovery & Development

Chungnam National University




Drug discovery is the process by which drugs are discovered or designed.


At the beginning of early modern drug discovery era, numerous drugs have been
discovered from tr
aditional natural products or by serendipitous way. However over the
years as our understanding of diseases has increased at the molecular and
pathophysiological level, we now attempt to design the molecules in a logical way
based on these informations.

Th
is logical process of drug discovery demands joint efforts between numerous
scientific and technological disciplines classified generally as chemistry and biology. In
order the new drug discovery process to be effective, communication and exchange of
exper
imental information between these scientific disciplines become critical and
crucial.

In this talk, I will describe the general process of new drug discovery and emphasize the
importance of exchanging informations between various scientific disciplines and

particularly how the information from one discipline is translated into the other
disciplines.

KEGG: current status and its applications


Susumu Goto


Starting from 1995 with four core databases,
PATHWAY, GENES, ENZYME and COMPOUND,
KEGG has now 17 data
bases classified into three
categories: Systems information, Genomic
information and Chemical information. Despite the
main objective of the KEGG system is to connect
the genomic and chemical information through the
systems information such as PATHWAY, BRI
TE, MODULE, the targets of KEGG are
expanding to new research fields and general public including medical information for
diseases and drugs. We also have to adopt to the new technologies. Recently
metagenomics data from next generation sequencers have be
en accumulated and we
have included some of the data in KEGG for the interpretation of the human gut
microbiomes.


Although manual curation is still a basis for the creation of reference pathways,
modules, and functional hierarchies, many automatic process
es have been incorporated
into the functional annotation for complete genomes and metagenomes due to the
exponential growth of genome sequences. In addition, tools for annotating genomes,
predicting new pathways, and predicting genes for missing enzymes an
d functions have
been developed as an application of KEGG and available on the web.

The MetaCyc Family of Pathway/Genome Databases and the Pathway
Tools Software



Ingrid Keseler

Senior Scientific Database Curator (EcoCyc) and Principal
Investigator, Bsu
bCyc project


Comprehensive knowledge of metabolic pathways is required
in a variety of biomedical and biotechnology applications.


The
MetaCyc family of Pathway/Genome Databases (PGDBs) describes
the genomes and metabolic pathways of more than 1,700 organ
isms
with sequenced genomes.


These databases share a common schema and ontologies, facilitating
interoperation and comparative analysis.


Many are highly curated, including PGDBs for
E. coli
, yeast,
mouse, and
Arabidopsis
.


PGDBs in the MetaCyc family wer
e derived computationally from
MetaCyc.


MetaCyc now contains more than 1,800 experimentally elucidated metabolic pathways found
in more than 2,300 organisms.


The MetaCyc data were curated from 35,000 publications.


Pathway Tools, the software used to bu
ild, update and publish the MetaCyc family of PGDBs,
contains a large suite of algorithms for manipulating biological networks and genome data. In particular,
it includes inference modules for inferring the metabolic pathways of an organism, and for predi
cting
genes encoding enzymes that might fill missing reactions in the predicted pathways.


Recent additions to
Pathway Tools include (a) the ability to generate steady
-
state metabolic flux models from PGDBs that
enable prediction of the essential genes of
an organism, and of its growth under different nutrient
conditions; (b) a fast, accurate algorithm for prediction of reaction atom mappings; (c) tools for storage
and analysis of organism growth data within PGDBs, such as Phenotype Microarray data.

Linking pathways to literature: PathText



Sophia Ananiadou


full professor in Computer Science, School of Computer Science,
University of Manchester and Director of the National Centre for
Text Mining.


PathText is a text mining based system linking mode
ls encoded in
SBML with evidence from literature. The strengths of PathText
include advanced search based on NaCTeM's text mining services,
Facta+, KLEIO, MEDIE. These services include event extraction tools (EventMine), faceted search based
on named entit
y recognition, disambiguation components and normalisation. In addition, our one
-
stop
collaborative text processing workflow platform (Argo) includes annotation tools that facilitate curation
of pathways. Issues on efficient querying and ranking from model
s will be addressed.