Research Article Open Access

A Feasibility Study of Challenges and Opportunities in Computational Biology: A Malaysian Perspective

R. J.A. Richard and N. Sriraam


The term computational biology refers to the knowledge derived from a computer analysis of biological data that includes identification of genes in DNA sequence of different organisms, prediction of structural and functional mechanism of proteins, feature extraction and classification of genomics and proteomics. Computational biology is a rapidly developing branch of science and is highly interdisciplinary, using techniques and Concepts from informatics, mathematics, chemistry, physics, statistics and biochemistry. This field has risen in parallel with the developments of automated high throughput methods of biochemistry and biological discovery that yield a variety of forms of experimental data, such as DNA& RNA sequences, gene expressions patterns and chemical structures. The field’s rapid growth is spurred by the vast potential for new understanding that can lead to new technological treatments, new agro-crops cultivation and new pharmaceutical drug discovery. In the recent years, most Bioengineering disciplines are started adopting the information technology oriented curriculum due to its high performance computing, data interoperability, web-based platform compatibility and secured a suitable job opportunity. This study discusses the challenges to set up an interdisciplinary oriented curriculum by merging life sciences and information technology at a university level. It also provides the career opportunities for different life science disciplines like drug development, microbial genome applications, biotechnology, forensic and analysis of microbes.

American Journal of Applied Sciences
Volume 2 No. 9, 2005, 1296-1300


Submitted On: 12 July 2005 Published On: 30 September 2005

How to Cite: Richard, R. J. & Sriraam, N. (2005). A Feasibility Study of Challenges and Opportunities in Computational Biology: A Malaysian Perspective . American Journal of Applied Sciences, 2(9), 1296-1300.

  • 3 Citations



  • Computational biology
  • genomics
  • proteomics
  • interdisciplinary oriented curriculum