The Information Systems Laboratory (ISL) at BIOTEC was formed to conduct research and development in information technology to establish and enhance the biological information infrastructure that

• provides the systematic collection, preservation and distribution of bioresources and
• facilitates the search-and-discovery of exploitable bioresources. 

Bioresource Data Management
Our first project, MIMS (Microbial Information Management System), has been started in 2000 to setup an in-house database system for managing the taxonomic, ecological, bibliographic and graphic information of the microbial culture collection at BIOTEC. More than 10,000 strains of microbes now are available in the MIMS database. To maximize the future utilization of microbial resources in Thailand, we have also involved in the establishment of the national information network on culturable microbes by providing the software for collection management to all members of Thailand Network of Culture Collections (TNCC). In regional data network, the ISL staff has participated in a working group to develop and standardize the networking data among 12 Asian countries members of the Asian Consortium for the Conservation and Sustainable Use of Microbial Resources (ACM).

In addition, we are building other bio-resources databases, developing the web interface for researchers to find as much bioresources information from our central databases as possible and providing more informatics tools written by our specialists for data analysis, comparison and visualization.
 
Bioinformatics and Chemoinformatics
The ISL also focuses on machine learning and data mining techniques applied to Bioinformatics and Chemoinformatics, in collaboration with various research groups in Thailand. In particular, researchers at ISL are working on the development of methods and tools in following topics:

• The prediction of protein subcellular localization,
• The prediction of sigma-factor binding site,
• Detection of Leucine-Rich Repeat Region in proteins, and
• Bioactivity Prediction using maximal frequent molecular fragments.