Dynamic bayesian network modeling of cyanobacterial biological processes via gene clustering

Authors:
Nguyen Xuan Vinh;Madhu Chetty;Ross Coppel;Pramod P. Wangikar
Affiliations:
Gippsland School of Information Technology, Monash University, Australia;Gippsland School of Information Technology, Monash University, Australia;Department of Microbiology, Monash University, Australia;Chemical Engineering Department, Indian Institute of Technology, Mumbai, India
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

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Abstract

Cyanobacteria are photosynthetic organisms that are credited with both the creation and replenishment of the oxygen-rich atmosphere, and are also responsible for more than half of the primary production on earth. Despite their crucial evolutionary and environmental roles, the study of these organisms has lagged behind other model organisms. This paper presents preliminary results on our ongoing research to unravel the biological interactions occurring within cyanobacteria. We develop an analysis framework that leverages recently developed bioinformatics and machine learning tools, such as genome-wide sequence matching based annotation, gene ontology analysis, cluster analysis and dynamic Bayesian network. Together, these tools allow us to overcome the lack of knowledge of less well-studied organisms, and reveal interesting relationships among their biological processes. Experiments on the Cyanothece bacterium demonstrate the practicability and usefulness of our approach.