Integrated network analysis and effective tools in plant systems biology

Abstract

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

Keywords: flux balance analysis; genome-scale metabolic reconstruction; network visualization; pathway analysis; plant metabolism.

Figure 1

An example of the network representation of time-series metabolome data in Arabidopsis using KEGGscape (http://apps.cytoscape.org/apps/keggscape). The datasets were sampled with 4-h resolution under a 16 h/8 h light/dark cycle at 20°C (Espinoza et al., 2010). We used the KEGG pathway map (ath00020), the tricarboxylic acid (TCA) cycle, or the citrate cycle. We queried MetMask (Redestig et al., 2010) for a list of KEGG compound IDs associated with a list of predefined metabolite names and picked up the most pathway-mapped KEGG compound ID for each metabolite. Metabolite names shown in red represent detected compounds in the dataset. The diurnal changes were visualized on bar charts ranging from −0.3 to 0.3 in log-mean values. ZT, Zeitgeber time.