Getting to the edge: network science identifies biological modules in Arabidopsis regulatory and interactions networks
S. MUKHTAR (1) (1) University of Alabama at Birmingham, U.S.A.

Systems biology yields models that analyze various changes in biological systems over time and seeks to uncover the predictable and unpredictable intricacies. Any given genotype has a sophisticated underlying network of macromolecular interactions that give rise to a phenotype. Network biology, a branch of systems biology, translates the complexities of molecular interactions into a biological message. Diverse natural systems possess ubiquitous properties such as scale-free and small-world nature and network biology-based analyses seek to uncover these unique properties. In any eukaryotic cell, thousands of genes and their products orchestrate transcriptional and translational relationships to create diverse networks including regulatory and protein-protein interactions networks. To understand regulatory functions, we are constructing an Arabidopsis dynamic immune regulatory network through our custom computational pipeline: Transcriptional REgulatory Network Dynamics (TREND). Determination of the structure and dynamic behavior of these networks will enable us to understand how the cell responds to diverse environmental cues and performs complex biological functions. In addition, we are also applying network decomposition analyses on large-scale Arabidopsis protein-protein interaction networks. We are integrating phenotypic data into various layers of Arabidopsis networks that will provide novel insights in understanding the properties of essential/non-essential proteins. These data also evidenced that pathogen effectors target non-essential hubs to modulate immune responses. In summary, our large-scale integrative network-based analysis is expected to (a) provide a comprehensive understanding of the relationships between phytopathogens and plants, (b) infer and assess biological functions, (c) understand biological processes and molecular pathways, and (d) predict and prioritize candidates for further investigation.

Abstract Number: P14-422
Session Type: Poster