Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High-throughput annotation and metabolic modeling of these genomes is now a reality. The highthroughput reconstruction and analysis of genome-scale transcriptional regulatory networks represent the next frontier in microbial bioinformatics. The fruition of this next frontier will depend on the integration of numerous data sources relating to mechanisms, components and behavior

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... the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here, we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data and binding site locations and we explore how these data are being used for the reconstruction of new regulatory networks. From template network-based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. We also explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model. Keywords: genome-scale metabolic (GSM) model; transcriptional regulatory network (TRN); de novo reverse engineering; integrated metabolic and regulatory models Jose¤ P. . She conducts her research in the fields of metabolic engineering and systems biology. Christopher S. Henry is an assistant computational scientist with appointments at Argonne National Laboratory, the University of Chicago and Northwestern University. He is an expert in the reconstruction, optimization and analysis of genome-scale metabolic models.