Molecules 2010, 15(11), 8177-8192; doi:10.3390/molecules15118177

Analysis of Protein Pathway Networks Using Hybrid Properties

1 College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China 2 Centre for Computational Systems Biology, Fudan University, Shanghai 200433, China 3 Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China 4 Shanghai Center for Bioinformation Technology, Shanghai 200235, China 5 Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China 6 Institute of Systems Biology, Shanghai University, Shanghai 200444, China 7 Gordon Life Science Institute, San Diego, California 92130, USA
* Author to whom correspondence should be addressed.
Received: 5 August 2010; in revised form: 11 November 2010 / Accepted: 12 November 2010 / Published: 12 November 2010
(This article belongs to the Special Issue From Computational Chemistry to Complex Networks)
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Abstract: Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on different organisms, at both the genetic and metabolic levels, has been accumulated and systematically stored in various specific databases, such as KEGG, ENZYME, BRENDA, EcoCyc and MetaCyc. These data have made it feasible to address such an essential problem. In this paper, we have analyzed known regulatory pathways in humans by extracting different (biological and graphic) features from each of the 17,069 protein-formed systems, of which 169 are positive pathways, i.e., known regulatory pathways taken from KEGG; while 16,900 were negative, i.e., not formed as a biologically meaningful pathway. Each of these protein-forming systems was represented by 352 features, of which 88 are graph features and 264 biological features. To analyze these features, the “Minimum Redundancy Maximum Relevance” and the “Incremental Feature Selection” techniques were utilized to select a set of 22 optimal features to query whether a protein-forming system is able to form a biologically meaningful pathway or not. It was found through cross-validation that the overall success rate thus obtained in identifying the positive pathways was 79.88%. It is anticipated that, this novel approach and encouraging result, although preliminary yet, may stimulate extensive investigations into this important topic.
Keywords: protein-forming system; regulatory pathway; minimum redundancy maximum relevance; gene ontology; biological graphic feature

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MDPI and ACS Style

Chen, L.; Huang, T.; Shi, X.-H.; Cai, Y.-D.; Chou, K.-C. Analysis of Protein Pathway Networks Using Hybrid Properties. Molecules 2010, 15, 8177-8192.

AMA Style

Chen L, Huang T, Shi X-H, Cai Y-D, Chou K-C. Analysis of Protein Pathway Networks Using Hybrid Properties. Molecules. 2010; 15(11):8177-8192.

Chicago/Turabian Style

Chen, Lei; Huang, Tao; Shi, Xiao-He; Cai, Yu-Dong; Chou, Kuo-Chen. 2010. "Analysis of Protein Pathway Networks Using Hybrid Properties." Molecules 15, no. 11: 8177-8192.

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