We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functionally distinct subnetworks: (i) a core, consisting of a large number of directed reaction cycles of enzymes for interconversions among intermediate metabolites; (ii) a catabolic module, with a largely layered structure consisting of mostly catabolic enzymes; (iii) an anabolic module with a similar structure consisting of virtually all anabolic genes; and (iv) the three subnetworks cover on average ∼56, ∼31 and ∼13% of a network's nodes across the 17 networks, respectively. Functional analyses suggest: (1) cellular metabolic fluxes generally go from the catabolic module to the core for substantial interconversions, then the flux directions to anabolic module appear to be determined by input nutrient levels as well as a set of precursors needed for macromolecule syntheses; and (2) enzymes in each subnetwork have characteristic ranges of kinetic parameters, suggesting optimized metabolic and regulatory relationships among the three subnetworks.

CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters. Based on the literature of empirical studies and genome/protein databases, it summarizes four types of information: common biological functions (pathways) driving CyanoHABs, customized pathway maps, classification of blooming type based on databases and the genomes of cyanobacteria. A total of 19 pathways are reconstructed, which are involved in the utilization of macronutrients (e.g. carbon, nitrogen, phosphorus and sulfur), micronutrients (e.g. zinc, magnesium, iron, etc.) and other resources (e.g. light and vitamins) and in stress resistance (e.g. lead and copper). These pathways, comprised of both transport and biochemical reactions, are reconstructed with proteins from NCBI and reactions from KEGG and visualized with self-created transport/reaction maps. The pathways are hierarchical and consist of subpathways, protein/enzyme complexes and constituent proteins. New cyanobacterial genomes can be annotated and visualized for these pathways and compared with existing species. This set of genomic functional repertoire is useful in analyzing aquatic metagenomes and metatranscriptomes in CyanoHAB research. Most importantly, it establishes a link between genome and ecology. All these reference proteins, pathways and maps and genomes are free to download at http://www.csbg-jlu.info/CyanoPATH.

1Physiological states-derived metabolic regulation network determines cellular phenotype but 2 is not considered in current constraint-based models (CBMs). Here, we proposed a novel 3 metabolic flux prediction model, Decrem, which integrated comprehensive regulatory 4 contexts-topologically coupled reactions-derived coactivation regulation and global cell 5 state regulators-derived enzyme kinetics-into canonical CBMs to approximate biologically 6 feasible fluxes. A unique advantage of Decrem is it relies only on the concentrations of 7 identified metabolites. When applied to three model organisms, Escherichia coli, 8Saccharomyces cerevisiae and Bacillus subtilis, Decrem demonstrated high accuracy in 9 metabolic flux prediction across various conditions, particularly for incomplete metabolic 10 models. Growth analysis by Decrem on yeast knockout strains revealed specific overflowed 11 pathway for several low growth rate strains, which are consistent with experiments but not 12 identified by previous methods. Additionally, the Pearson correlation coefficients between 13 experimental and predicted growth rates on 1030 E. coli genome-scale deletion strains were 14 increased to 0.743, compared to the 0.127, 0.103 and 0.281, respectively. This method is 15 expected to accurately simulate dynamic metabolic regulation and phenotype prediction for 16 synthetic biology. 17Keywords: genome-scale growth rate analysis / hierarchical metabolic regulation model / 18 linearized michaelis-menten kinetics / metabolic flux prediction / sparse linear basis 19 decomposition 20