Professor Chuang, Li-Yeh
E-mail:[email protected]
Education:
PhD. Department of Biochemistry, North Dakota State University
Courses Offered:
Organic chemistry, Organic chemistry experiment, Biochemistry,
Bioinformatics, General chemistry, Science technology English
Specialty:
Biochemistry, Natural products, Bioinformatics
Academic Interests:
(A) Investigation of antibacterial properties and mechanisms on the natural
products against multi-drug resistant clinical bacterium
Due to the past 20 to 30 years, a large number of antibiotics were misapplied in
Taiwan, and no efficiently manage antibiotics used for fishing and livestock farming.
It made many common pathogens produce antibiotic resistant and caused currently
medical treatment in a difficult position. The infections caused by antibiotic resistant
strains not only increasing the mortality rate and hiding an outbreak infection that
critically threaten people’s health. Therefore, in addition to strictly manage control the
use of antibiotics, vigorously develop new antiseptic substances or other therapy
modes are urgent issues. Currently, even many researches are focused on the
antibiotic gene studies, there are not many literatures discusses the relation between
antibiotic susceptibility with the antibiotic genes. Therefore, we will type the clinical
antibiotic resistant isolates according to their antibiotic susceptibility and compare the
relation between their antibiotic genes and species. We plan to develop an easy
detection method that will provide the antibiotic resistant information for clinical
diagnosis. In addition, we will purify and identify structure as well as chemical
properties of the antibacterial substances from Chinese medicines that were evidenced
present significant antibacterial activities in our previous studies. For China five
thousands years’ history, traditional Chinese herb medicines have been used for a long
time. Comparison with other antimicrobial substances, the antibacterial components
of Chinese medicine obviously have fewer side effects, lower toxicity and higher
stability advantage. Therefore, the natural products, obtained from Chinese medicine
against the major drug-resistant strains, have the potential to become natural
antibiotics. Moreover, we also apply molecular biology techniques (PCR, PFGE) to
analyze the variation of gene and determine the protein activity of β-lactamase by
using SDS-PAGE electrophoresis and gel renaturation analysis. Also, by using
scanning electron microscope to determine the antibacterial affect on the cell
morphology, so that to confer the possible antibacterial mechanism. The results of this
research will have significant value in the therapy and prevention of antibiotic
resistant bacterium in the hospital and can provide the information of possible
antibacterial mechanisms of herbal nature antibiotics on science research.
Fig. 1. Experiment flowchart of the investigation of antibacterial properties and
mechanisms on the natural products against multi-drug resistant clinical bacterium
(B) SNP interaction algorithm, system development and application of disease
prediction and pharmacogenomics
Single nucleotide polymorphisms ( SNPs ) are the most common type of DNA
sequence variation in the human genome. Many researchers have verified that the
SNPs might contain the important information for some diseases and cancer.
Therefore, SNP related studies have become very popular subjects recently, such as the
investigation of the relationship between SNPs and human diseases, and the heredity
epidemiology of some complex diseases. Regarding to the SNP interaction, we can
computationally and statistically improve the efficiency for the analysis of the
correlation between SNPs and diseases. In the disease prediction studies, SNPs can be
applied through machine learning technique to design novel algorithms for disease
prediction and feature selection. Regarding to the drug information, the DrugBank
database provides a source for drug information and notation. It contains
comprehensive information of drug target and drug reaction. Although the DrugBank
database also contains SNP information, the data related to the SNPs is not quit
completed. The database only provides the data from NCBI SNP IDs for some specific
drug targets, and lacks a framework for the analysis of these SNPs. It is thus limited on
the personal medicine studies for the medical researchers. Therefore, we focus on the
SNP interaction to design novel algorithms and to investigate the related application in
this project. The proposed study will be carried out in three years. In the first year, we
will continue our previous study on the algorithm improvement for SNP interaction
and built a SNP database by integration of the SNP-related databases and two
previously published systems, genotyping system and SNP interaction system. In the
second year, Taguchi method with the algorithms of particle swarm optimization and
support vector machine will be implemented on the disease prediction andfeature
selection. Finally, data from the databases of DrugBank, STRING, NCBI dbSNP,
HapMap tagSNP, SNP500Cancer and TaqMan, and the systems of SNP-RFLP
restriction enzymes, PCR-CTPP and PCR-RFLP primer design will be integrated to
develop an efficient and complete system for drug SNP analysis. The proposed system
is based on the SNP interaction and the correlation between SNPs and diseases or
drugs. This system will be able to apply on the fields of disease prediction, SNP
feature
selection
and
drug
therapy
or
development.
Fig. 2. System structure and flowchart for our proposed algorithms for SNP interaction,
tagging SNP, simulating genotype data, and software construction.