Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

BackgroundSilicon plays important biological roles, but the mechanisms of cellular responses to silicon are poorly understood. We report the first analysis of cell cycle arrest and recovery from silicon starvation in the diatom Thalassiosira pseudonana using whole genome microarrays.ResultsThree known responses to silicon were examined, 1) silicified cell wall synthesis, 2) recovery from silicon starvation, and 3) co-regulation with silicon transporter (SIT) genes. In terms of diatom cell wall formation, thus far only cell surface proteins and proteins tightly associated with silica have been characterized. Our analysis has identified new genes potentially involved in silica formation, and other genes potentially involved in signaling, trafficking, protein degradation, glycosylation and transport, which provides a larger-scale picture of the processes involved. During silicon starvation, an overrepresentation of transcription and translation related genes were up-regulated, indicating that T. pseudonana is poised to rapidly recover from silicon starvation and resume cell cycle progression upon silicon replenishment. This is in contrast to other types of limitation, and provides the first molecular data explaining the well-established environmental response of diatoms to grow as blooms and to out-compete other classes of microalgae for growth. Comparison of our data with a previous diatom cell cycle analysis indicates that assignment of the cell cycle specific stage of particular cyclins and cyclin dependent kinases should be re-evaluated. Finally, genes co-varying in expression with the SITs enabled identification of a new class of diatom-specific proteins containing a unique domain, and a putative silicon efflux protein.ConclusionsAnalysis of the T. pseudonana microarray data has provided a wealth of new genes to investigate previously uncharacterized cellular phenomenon related to silicon metabolism, silicon’s interaction with cellular components, and environmental responses to silicon.

The PurR transcription factor plays a critical role in transcriptional regulation of purine metabolism in enterobacteria. Here, we elucidate the role of PurR under exogenous adenine stimulation at the genome-scale using high-resolution chromatin immunoprecipitation (ChIP)–chip and gene expression data obtained under in vivo conditions. Analysis of microarray data revealed that adenine stimulation led to changes in transcript level of about 10% of Escherichia coli genes, including the purine biosynthesis pathway. The E. coli strain lacking the purR gene showed that a total of 56 genes are affected by the deletion. From the ChIP–chip analysis, we determined that over 73% of genes directly regulated by PurR were enriched in the biosynthesis, utilization and transport of purine and pyrimidine nucleotides, and 20% of them were functionally unknown. Compared to the functional diversity of the regulon of the other general transcription factors in E. coli, the functions and size of the PurR regulon are limited.

Although metabolic networks have been reconstructed on a genome-scale, the corresponding reconstruction and integration of governing transcriptional regulatory networks has not been fully achieved. Here we reconstruct such an integrated network for amino acid metabolism in Escherichia coli. Analysis of ChIP-chip and gene expression data for the transcription factors ArgR, Lrp, and TrpR showed that ∼82% of the genes they regulate are directly involved in amino acid metabolism. Further analysis shows that 19/20 amino acid biosynthetic pathways are either directly or indirectly controlled by these three transcription factors. Classifying the regulated genes into three functional categories of transport, biosynthesis, and metabolism leads to elucidation of regulatory motifs constituting the integrated network's basic building blocks. The regulatory logic of these motifs was determined based on the relationships between transcription factor binding and changes in transcript levels in response to exogenous amino acids. Remarkably, the resulting logic shows how amino acids are differentiated as signaling and nutrient molecules, and thus revealing the overarching regulatory principles of this stimulon.

Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r2 (p<1e-6) correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. Finally, we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network.