APCTP	■ Introduction · Greetings · Milestones · Organization Chart · Board Members · Staff Members · Former Board Members · Annual Reports · APCTP Logo · Contact us ■ Members · Member Countries · Partnership Institutions ■ Media · APCTP in Media · AAPPS Bulletin ■ Visitor Information · Plan Your Visit · Directions · Tips for Visitors · Facilities Guide · Map ■ Sponsors

Activities	■ Scientific Activities · Scientific Programs · Seminars & Lectures · ICTP Affiliated Center (ICAC) · Scientific Calendar · Call for Programs · Past Scientific Programs · Past Seminars & Lectures · Payment ■ Awards and Visitors · Benjamin Lee Professorship · Visitors · Visitor Information · Visitor Application ■ Outreach Programs · Web Journal <Crossroads> · APCTP Books · Events · Science at Pohang · Past Events

Research	■ Junior Research Groups ■ Young Scientist Training Program ■ Senior Advisory Group ■ APCTP-POSTECH Fellows ■ Publications

Asia Pacific Network	■ Academic Activities in the AP Region · Seminars at IBS: Theoretical Physics of Complex Systems · Center For QUANTUM SPACETIME · Association of Asia Pacific Physical Societies · FIST - Asia Network on HEP · ANPhA - Asian Nuclear Physics Association · APCTP Japan Committee · ICTP Asian Network on Condensed Matter and Complex Systems

Seminars & Lectures

* TITLE

Reconstructing Gene Regulatory Networks from Time-Series Microarray Data

* SPEAKERS

Name	Affiliation	E-mail
Prof. Sai-Ping Lee	Institute of Physics, Academia Sinica

* HOST(Applicant)

Name	Affiliation	E-mail
-

* DATE / TIME

2005-06-25, 11:00 - 12:00

* PLACE

Seminar Room #512, APCTP Headquarters

* ABSTRACT

A gene regulatory network depicts which genes turn on which and at what
moment. Knowledge of such gene networks is key to an understanding of
the biological process. We propose here to use a statistical method for
the reconstruction of gene regulatory networks based on Bayesian networks
from microarray data. We describe a nonlinear model for the rate of gene
transcription in which levels of gene expression are continuous. The
reconstruction becomes an optimization problem where optimization
algorithms are employed to search for optimal solutions. We apply the
methodology to reconstruct the regulatory network of 41 yeast cell cycle
genes from a real microarray data set. The result obtained is promising:
more than 70\% (31 out of 43 arcs) of the reconstructed regulations are
consistent with experimental findings.

Map

How to get to APCTP

Scientific Activities Calendar

Scientific programs

Modal title

Modal title