›› 2010, Vol. 25 ›› Issue (1): 1-2.

• Special Issue on Computational Challenges from Modern Molecular Biology •     Next Articles



  • Online:2010-01-05 Published:2010-01-05
  • About author:
    Ying Xu is the Regents-Georgia Research Alliance Eminent Scholar Chair and Professor in the Department of Biochemistry and Molecular Biology and the Institute of Bioinformatics, the University of Georgia. He received his Ph.D. degree in theoretical computer science from the University of Colorado at Boulder in 1991. He was a visiting assistant worked for Oak Ridge National Laboratory from 1993 to 2003, where he was a senior staff scientist and a group leader. His current research interests include (a) computational and systems biology relevant to human cancer and early detection, (b) microbial genomes and encoded pathways, and (c) plant genomes and plant cell walls. He has published over 200 research articles and four books covering different areas of bioinformatics and systems biology.
    Ming Li is a Canada research chair in bioinformatics and a University Professor at the University of Waterloo. He is a fellow of Royal Society of Canada, ACM, and IEEE. He is a recipient of E.W.R. Steacie Fellowship Award in 1996, and the 2001 Killam Fellowship. Together with Paul Vitanyi he has pioneered the applications of Kolmogorov complexity and co-authored the book ``An introduction to Kolmogorov complexity and its applications''. His research interests recently include protein structure determination and the Internet search engine.
    Tao Jiang received the B.S. degree in computer science and techno-logy from the University of Science and Technology of China, Hefei, in July 1984 and Ph.D. degree in computer science from University of Minnesota in Nov. 1988. He was a faculty member at McMaster University, Hamilton, Ontario, Canada during Jan. 1989$sim$July 2001 and is now professor of computer science and engineering at University of California -- Riverside (UCR). He is also a member of the UCR Institute for Integrative Genome Biology, a member of the Center for Plant Cell Biology, a principal scientist at Shanghai Center for Bioinformation Technology, and Changjiang Visiting Professor at Tsinghua University. Tao Jiang's recent research interest includes combinatorial algorithms, computational molecular biology, bioinformatics, and computational aspects of information retrieval. He is a fellow of ACM and of AAAS. More information about his work can be found at http://www1.cs.ucr.edu/$^sim$jiang.

Bioinformatics is considered as one of the fastest growing fields in science today, thanks to the rapidly expanding and advancing capabilities in biological data collection from cellular organisms using high-throughput measurement technologies. These data reflect different aspects of living organisms such as the existence, structure, functionality and functional states of biological molecules and assemblies under designed experimental conditions. The enormous amount of information hidden in these data allows computational scientists to start to elucidate the internal structures and control mechanisms of biological systems at various levels such as cell, tissue, organ, organism and eco-system in a systematic manner, and even possibly to derive the organizational and operating principles of such systems. Scientists have begun to draw comparisons between the relationship of physics and mathematics and that of biology and computational science, and believe that the future of biology could be taught, like physics, ``as a set of basic systems $cs$ duplicated and adapted to a very wide range of cellular and organismal functions, following basic evolutionary principles constrained by the Earth's geological history'' (T. F. Smith, The challenges facing genomic informatics. Current Topics in Computational Molecular Biology, T. Jiang, Y. Xu and M. Q. Zhang (eds.), pp.3-8, MIT Press, Cambridge, Massachusetts (2002).). It is clearly exciting to possibly play a role in helping to transform biological science from a pure experimental science to a science like physics. Yet the gap between where we are now and where we want to be is enormously large! It is generally believed that computational scientists can and should play essential roles in bridging this gap by offering new techniques, frameworks and possibly theories for solving a variety of computational challenges arising in modern biology.
In this special issue, we have invited 11 teams of leading researchers working at the forefront of bioinformatics and computational biology, plus three additional articles contributed by the three editors, to share their visions about the computational challenges that we are facing in different areas of modern biology. It is not our intention to provide a comprehensive coverage of computational challenges across all areas of bioinformatics and computational biology; instead, we hope to provide some samples of the challenging issues with the 14 articles. Our ultimate goal is to attract more computational scientists to look at and to study these computational problems and beyond to help to shape the future of a new branch of computational science in which biological problems, instead of the traditional physics-oriented/-inspired problems, will be the focus.
The 14 articles cover the following areas of bioinformatics and computational biology:
1. new generation sequencing techniques and computational challenges arising from the associated problems such as genome assembly (Schwartz and Waterman);
2. evolutionary studies of gene orders in ancestral genomes using a phylogenomic approach (Sankoff {it et al.});
3. elucidation and understanding of epigenomic modifications at a genome scale, discussed from two different perspectives by Zhang & Smith and by Liang, respectively;
4. orthologous gene mapping across multiple genomes, a fundamental technique in comparative genomics (Jiang);
5. elucidation of microbial genomic structures and associated computational challenges (Xu);
6. computational challenges from the emerging field of metagenomics (Wooley and Ye);
7. domain-based functional studies of proteins and associated computational challenges (Rendon {it et al.});
8. opportunities and challenges in developing a new generation of accurate prediction techniques for protein tertiary structures (Li);
9. challenges from large-scale proteomic studies through mass spectrometry data analyses (Ma);
10. computational challenges in elucidation of protein interactomic data (Wong and Liu);
11. challenges in reconstruction of gene networks based on time-course microarray gene expression data (Yamaguchi, Imoto and Miyano);
12. study of dynamics of complex biochemical systems using chemical master equations and associated challenges (Liang and Qian); and
13. computational challenges from biological text mining, a technique that will play increasingly more important roles as scientists start to mine the published literature in a similar way to that people have been mining organized data in databases (Dai {it et al.}).
A large number of open questions and challenges in these areas are discussed. It is our hope that these articles will get many computational scientists excited about the expanding field of bioinformatics, and decide to study some of the open problems discussed in this special issue.
We would like to take this opportunity to thank all the authors who have taken time from their busy schedules to write for this special issue. We would also like to thank the editorial office of JCST, particularly Ms. Xiaoxian Wan, for encouraging us to edit this special issue. Throughout this project, Ms. Joan Yantko and Dr. Fenglou Mao of the Computational Systems Biology Lab at the University of Georgia, have both provided timely help in coordinating with the authors and setting up an internal website to facilitate communication between the editors and the authors as well as among the authors. We thank their help.

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