Bioinfomatics and computational systems biology research is fundamental to our understanding of complex biological systems, impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences. In this special section, six papers in their significantly extended versions were selected from the papers presented at the IEEE Conference on Bioinformatics and Biomedicine (BIBM), 2010. These papers present recent research works in bioinformatics and computational systems biology to make use of large-scale biological data or high throughput data to understand the mechanism of biological systems and to show their usefulness in diagnosis and drug design for complex diseases.
Gene regulatory networks, protein interaction networks, and signal transduction cascades or pathways are all complex systems with many interacting components. The reconstruction of structure and mechanisms of interactions among components in networks from available experimental data is one of the most challenging tasks of systems biology.
Yinyin Yuan, Christina Curtis, Carlos Caldas, and Florian Markowetz proposed an integrative approach to learn a sparse interaction network of DNA copy number regions with their downstream transcriptional targets in breast cancer. The DNA-RNA interaction network helps to distinguish copy-number driven expression alterations and yields a quantitative copy-number dependency score, which distinguishes cis- versus trans-effects.
Li-Zhi Liu, Fang-Xiang Wu, and W.J. Zhang studied the S-system, which consists of a group of nonlinear ordinary differential equations being an effective model to characterize molecular biological systems and analyze the system dynamics. They proposed a novel algorithm to estimate parameters without the knowledge of system structure.
Shuichi Kawano, Teppei Shimamura, Atsushi Niida, Seiya Imoto, Rui Yamaguchi, Masao Nagasaki, Ryo Yoshida, Cristin Print, and Satoru Miyano proposed a statistical method for uncovering gene pathways that characterize cancer heterogeneity. Their method is based on the sparse probabilistic principal component analysis, and pathway activity logistic regression models.
Gene expression models play a key role in understanding the mechanisms of gene regulation whose aspects are grade and switch-like responses. Haseong Kim and Erol Gelenbe proposed a stochastic gene expression model describing the switch-like behaviors of a gene by employing hill functions to the conventional Gillespie algorithm to explain switch-like behaviors of gene responses and oscillatory expressions, which are consistent with the published experimental study.
The development of new sequencing techniques and meta-genomics has dramatically changed the way of genomics data acquiring and analyzing. Xin Chen, Xiaohua Hu, Tze Y. Lim, Xiajiong Shen, E.K. Park, and Gail L. Rosen studied a method that enables both the homology-based approach and the composition-based approach to evaluate the functional core (i.e., microbial core and gene core, correspondingly) and identify major functionality groups by generative topic modeling, which is able to extract useful information from unlabeled data.
Predicting ligand binding residues and functional sites in protein sequences is one of the important bioinformatics problems. Alvaro J. González, Li Liao, and Cathy H. Wu studied that the residual and sites tend to be not only conserved but also exhibit strong correlation due to the selection pressure during evolution in order to maintain the required structure and/or function. They proposed a graph theoretic clustering and kernel-based canonical correlation analysis (kCCA) to identify binding and functional sites in protein sequences as the residues that exhibit strong correlation between the residues of evolutionary characterization at the sites and the structure-based functional classification of the proteins in the context of a functional family.
The guest editors would like to thank all of the authors for their high-quality work they contributed to this special section and all of the anonymous reviewers for their great efforts and expert comments in evaluating the manuscripts.
Michael K. Ng
L. Chen is with the Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai, China.
M. Ng is with the Department of Mathematics and Centre for Mathematical Imaging and Vision, Hong Kong Baptist University, Kowloon Tong, Hong Kong. E-mail: firstname.lastname@example.org.
For information on obtaining reprints of this article, please send e-mail to: email@example.com.
The work of Luonan Chen is supported by NSFC under No. 91029301 and No. 61134013, and also partially supported by Aihara Project of the FIRST program from JSPS initiated by CSTP. The work of Michael K. Ng is supported in part by HKRGC grants and Hong Kong Baptist University FRG grants.
received the ME and PhD degrees in electrical engineering from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. Beginning in 1997, he was an associate professor at the Osaka Sangyo University, Osaka, Japan, and then a full professor. Since 2010, he has been a professor and executive director at the Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. He was the founding director of the Institute of Systems Biology, Shanghai University. He serves as chair of the Technical Committee on Systems Biology of the IEEE SMC Society, and as the founding president of the Computational Systems Biology Society of ORS China. He serves as an editor or editorial board member for major systems biology related journals, e.g., BMC Systems Biology
, IEEE/ACM Transactions on Computational Biology and Bioinformatics
, IET Systems Biology
, Mathematical Biosciences
, Journal of the Royal Society Interface
, Journal of Theoretical Biology
, and the International Journal of Systems and Synthetic Biology
. His fields of interest are systems biology, computational biology, and nonlinear dynamics. In the past five years, he published more than 100 journal papers and two monographs in the area of systems biology.
Michael K. Ng
received the BSc and MPhil degrees in 1990 and 1992, respectively, from the University of Hong Kong, and the PhD degree in 1995 from the Chinese University of Hong Kong. He is a professor in the Department of Mathematics at Hong Kong Baptist University. He was a research fellow in the Computer Sciences Laboratory at the Australian National University from 1995 to 1997, and an assistant/associate professor from 1997 to 2005 at the University of Hong Kong before joining Hong Kong Baptist University. His research interests include bioinformatics, data mining, image processing, scientific computing, and data mining, and he serves on the editorial boards of international journals. More information about him can be found at http://www.math.hkbu.edu.hk/~mng.