Identifying global regulators in transcriptional regulatory networks in bacteria

Impact of global transcriptional regulation by ArcA, ArcB, Cra, Crp, Cya, Fnr, and Mlc on glucose catabolism in Escherichia coli

Even though transcriptional regulation plays a key role in establishing the metabolic network, the extent to which it actually controls the in vivo distribution of metabolic fluxes through different pathways is essentially unknown. Based on metabolism-wide quantification of intracellular fluxes, we systematically elucidated the relevance of global transcriptional regulation by ArcA, ArcB, Cra, Crp, Cya, Fnr, and Mlc for aerobic glucose catabolism in batch cultures of Escherichia coli. Knockouts of ArcB, Cra, Fnr, and Mlc were phenotypically silent, while deletion of the catabolite repression regulators Crp and Cya resulted in a pronounced slow-growth phenotype but had only a nonspecific effect on the actual flux distribution. Knockout of ArcA-dependent redox regulation, however, increased the aerobic tricarboxylic acid (TCA) cycle activity by over 60%. Like aerobic conditions, anaerobic derepression of TCA cycle enzymes in an ArcA mutant significantly increased the in vivo TCA flux when nitrate was present as an electron acceptor. The in vivo and in vitro data demonstrate that ArcA-dependent transcriptional regulation directly or indirectly controls TCA cycle flux in both aerobic and anaerobic glucose batch cultures of E. coli. This control goes well beyond the previously known ArcA-dependent regulation of the TCA cycle during microaerobiosis.

Modular analysis of the transcriptional regulatory network of E. coli

The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules.

Oligonucleotide microarray analysis of the salA regulon controlling phytotoxin production by Pseudomonas syringae pv. syringae

The salA gene is a key regulatory element for syringomycin production by Pseudomonas syringae pv. syringae and encodes a member of the LuxR regulatory protein family. Previous studies revealed that salA, a member of the GacS/GacA signal transduction system, was required for bacterial virulence, syringomycin production, and expression of the syrB1 synthetase gene. To define the SalA regulon, the spotted oligonucleotide microarray was constructed using gene-specific 70-mer oligonucleotides of all open reading frames (ORFs) predicted in the syringomycin (syr) and syringopeptin (syp) gene clusters along with representative genes important to bacterial virulence, growth, and survival. The microarray containing 95 oligos was used to analyze transcriptional changes in a salA mutant (B301DSL07) and its wild-type strain, B301D. Expression of 16 genes was significantly higher (> twofold) in B301D than in the salA mutant; the maximum change in expression was 15-fold for some toxin biosynthesis genes. Except for the sylD synthetase gene for syringolin production, all ORFs controlled by SalA were located in the syr-syp genomic island and were associated with biosynthesis, secretion, and regulation of syringomycin and syringopeptin. The positive regulatory effect of SalA on transcription of sypA, syrB1, syrC, and sylD was verified by reporter fusions or real-time polymerase chain reaction analysis. None of the genes or ORFs was significantly down-regulated by the salA gene. These results demonstrated that a subgenomic oligonucleotide microarray is a powerful tool for defining the SalA regulon and its relationship to other genes important to plant pathogenesis.

Alternative sigma factor interactions inSalmonella: σEand σHpromote antioxidant defences by enhancing σSlevels

Hierarchical interactions between alternative sigma factors control sequential gene expression in Gram-positive bacteria, whereas alternative sigma factors in Gram-negative bacteria are generally regarded to direct expression of discrete gene subsets. In Salmonella enterica serovar Typhimurium (S. Typhimurium), sigma(E) responds to extracytoplasmic stress, whereas sigma(H) responds to heat shock and sigma(S) is induced during nutrient limitation. Deficiency of sigma(E), sigma(H) or sigma(S) increases S. Typhimurium susceptibility to oxidative stress, but an analysis of double and triple mutants suggested that antioxidant actions of sigma(E) and sigma(H) might be dependent on sigma(S). Transcriptional profiling of mutant Salmonella lacking sigma(E) revealed reduced expression of genes dependent on sigma(H) and sigma(S) in addition to sigma(E). Further investigation demonstrated that sigma(E) augments sigma(S) levels during stationary phase via enhanced expression of sigma(H) and the RNA-binding protein Hfq, leading to increased expression of sigma(S)-dependent genes and enhanced resistance to oxidative stress. Maximal expression of the sigma(S)-regulated gene katE required sigma(E) in Salmonella-infected macrophages as well as stationary-phase cultures. Interactions between alternative sigma factors permit the integration of diverse stress signals to produce coordinated genetic responses.

Promoters in the environment: transcriptional regulation in its natural context

Transcriptional activation of many bacterial promoters in their natural environment is not a simple on/off decision. The expression of cognate genes is integrated in layers of iterative regulatory networks that ensure the performance not only of the whole cell, but also of the bacterial population, and even the microbial community, in a changing environment. Unlike in vitro systems, where transcription initiation can be recreated with a handful of essential components, in vivo, promoters must process various physicochemical and metabolic signals to determine their output. This helps to achieve optimal bacterial fitness in extremely competitive niches. Promoters therefore merge specific responses to distinct signals with inclusive reactions to more general environmental changes.

Dissecting the PhoP regulatory network of Escherichia coli and Salmonella enterica

Genetic and genomic approaches have been successfully used to assign genes to distinct regulatory networks. However, the present challenge of distinguishing differentially regulated genes within a network is particularly hard because members of a given network tend to have similar regulatory features. We have addressed this challenge by developing a method, termed Gene Promoter Scan, that discriminates coregulated promoters by simultaneously considering both multiple cis promoter features and gene expression. Here, we apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the enteric bacteria Escherichia coli and Salmonella enterica. Our analysis uncovered members of the PhoP regulon and interactions with other regulatory systems that were not discovered in previous approaches. The predictions made by Gene Promoter Scan were experimentally validated to establish that the PhoP protein uses multiple mechanisms to control gene transcription, regulates acid resistance determinants, and is a central element in a highly connected network.

Functional interactions between the carbon and iron utilization regulators, Crp and Fur, in Escherichia coli

In Escherichia coli, the ferric uptake regulator (Fur) controls expression of the iron regulon in response to iron availability while the cyclic AMP receptor protein (Crp) regulates expression of the carbon regulon in response to carbon availability. We here identify genes subject to significant changes in expression level in response to the loss of both Fur and Crp. Many iron transport genes and several carbon metabolic genes are subject to dual control, being repressed by the loss of Crp and activated by the loss of Fur. However, the sodB gene, encoding superoxide dismutase, and the aceBAK operon, encoding the glyoxalate shunt enzymes, show the opposite responses, being activated by the loss of Crp and repressed by the loss of Fur. Several other genes including the sdhA-D, sucA-D, and fumA genes, encoding key constituents of the Krebs cycle, proved to be repressed by the loss of both transcription factors. Finally, the loss of both Crp and Fur activated a heterogeneous group of genes under sigmaS control encoding, for example, the cyclopropane fatty acid synthase, Cfa, the glycogen synthesis protein, GlgS, the 30S ribosomal protein, S22, and the mechanosensitive channel protein, YggB. Many genes appeared to be regulated by the two transcription factors in an apparently additive fashion, but apparent positive or negative cooperativity characterized several putative Crp/Fur interactions. Relevant published data were evaluated, putative Crp and Fur binding sites were identified, and representative results were confirmed by real-time PCR. Molecular explanations for some, but not all, of these effects are provided.

Making connections between novel transcription factors and their DNA motifs

The key components of a transcriptional regulatory network are the connections between trans-acting transcription factors and cis-acting DNA-binding sites. In spite of several decades of intense research, only a fraction of the estimated approximately 300 transcription factors in Escherichia coli have been linked to some of their binding sites in the genome. In this paper, we present a computational method to connect novel transcription factors and DNA motifs in E. coli. Our method uses three types of mutually independent information, two of which are gleaned by comparative analysis of multiple genomes and the third one derived from similarities of transcription-factor-DNA-binding-site interactions. The different types of information are combined to calculate the probability of a given transcription-factor-DNA-motif pair being a true pair. Tested on a study set of transcription factors and their DNA motifs, our method has a prediction accuracy of 59% for the top predictions and 85% for the top three predictions. When applied to 99 novel transcription factors and 70 novel DNA motifs, our method predicted 64 transcription-factor-DNA-motif pairs. Supporting evidence for some of the predicted pairs is presented. Functional annotations are made for 23 novel transcription factors based on the predicted transcription-factor-DNA-motif connections.

TRACTOR_DB: a database of regulatory networks in gamma-proteobacterial genomes

Experimental data on the Escherichia coli transcriptional regulatory system has been used in the past years to predict new regulatory elements (promoters, transcription factors (TFs), TFs' binding sites and operons) within its genome. As more genomes of gamma-proteobacteria are being sequenced, the prediction of these elements in a growing number of organisms has become more feasible, as a step towards the study of how different bacteria respond to environmental changes at the level of transcriptional regulation. In this work, we present TRACTOR_DB (TRAnscription FaCTORs' predicted binding sites in prokaryotic genomes), a relational database that contains computational predictions of new members of 74 regulons in 17 gamma-proteobacterial genomes. For these predictions we used a comparative genomics approach regarding which several proof-of-principle articles for large regulons have been published. TRACTOR_DB may be currently accessed at http://www.bioinfo.cu/Tractor_DB, http://www.tractor.lncc.br/ or at http://www.cifn.unam.mx/Computational_Genomics/tractorDB. Contact Email id is tractor@cifn.unam.mx.

Structure and evolution of transcriptional regulatory networks

The regulatory interactions between transcription factors and their target genes can be conceptualised as a directed graph. At a global level, these regulatory networks display a scale-free topology, indicating the presence of regulatory hubs. At a local level, substructures such as motifs and modules can be discerned in these networks. Despite the general organisational similarity of networks across the phylogenetic spectrum, there are interesting qualitative differences among the network components, such as the transcription factors. Although the DNA-binding domains of the transcription factors encoded by a given organism are drawn from a small set of ancient conserved superfamilies, their relative abundance often shows dramatic variation among different phylogenetic groups. Large portions of these networks appear to have evolved through extensive duplication of transcription factors and targets, often with inheritance of regulatory interactions from the ancestral gene. Interactions are conserved to varying degrees among genomes. Insights from the structure and evolution of these networks can be translated into predictions and used for engineering of the regulatory networks of different organisms.

Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach

BACKGROUND

Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN) of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear.

RESULTS

In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif) in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators.

CONCLUSION

The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E. coli. Analysis of the distribution of feed forward loops and bi-fan motifs in the hierarchical structure suggests that these network motifs are not elementary building blocks of functional modules in the transcriptional regulatory network of E. coli.

An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs

Recent studies of genome-wide transcriptional regulatory network (TRN) revealed several intriguing structural and dynamic features of gene expression at a system level. Unfortunately, the network under study is often far from complete. A critical question is thus how much the network is incomplete and to what extent this would affect the results of analysis. Here we compare the Escherichia coli TRN built by Shen-Orr et al. (Nature Genet., 31, 64-68) with two TRNs reconstructed from RegulonDB and Ecocyc respectively and present an extended E.coli TRN by integrating information from these databases and literature. The scale of the extended TRN is about twice as large as the previous ones. The new network preserves the multi-layer hierarchical structure which we recently reported but has more layers. More global regulators are inferred. While the feed forward loop (FFL) is confirmed to be highly representative in the network, the distribution of the different types of FFLs is different from that based on the incomplete network. In contrast to the notion of motif aggregation and formation of homologous motif clusters, we found that most FFLs interact and form a giant motif cluster. Furthermore, we show that only a small portion of the genes is solely regulated by only one FFL. Many genes are regulated by two or more interacting FFLs or other more complicated network motifs together with transcriptional factors not belonging to any network motifs, thereby forming complex regulatory circuits. Overall, the extended TRN represents a more solid basis for structural and functional analysis of genome-wide gene regulation in E.coli.

Identification of a New Member of the Phage Shock Protein Response in Escherichia coli, the Phage Shock Protein G (PspG)

The phage shock protein operon (pspABCDE) of Escherichia coli is strongly up-regulated in response to overexpression of the filamentous phage secretin protein IV (pIV) and by many other stress conditions including defects in protein export. PspA has an established role in maintenance of the proton-motive force of the cell under stress conditions. Here we present evidence for a new member of the phage shock response in E. coli. Using transcriptional profiling, we show that the synthesis of pIV in E. coli leads to a highly restricted response limited to the up-regulation of the psp operon genes and yjbO. The psp operon and yjbO are also up-regulated in response to pIV in Salmonella enterica serovar Typhimurium. yjbO is a highly conserved gene found exclusively in bacteria that contain a psp operon but is physically unlinked to the psp operon. yjbO encodes a putative inner membrane protein that is co-controlled with the psp operon genes and is predicted to be an effector of the psp response in E. coli. We present evidence that yjbO expression is driven by sigma(54)-RNA polymerase, activated by PspF and integration host factor, and negatively regulated by PspA. PspF specifically regulates only members of the PspF regulon: pspABCDE and yjbO. We found that increased expression of YjbO results in decreased motility of bacteria. Because yjbO is co-conserved and co-regulated with the psp operon and is a member of the phage shock protein F regulon, we propose that yjbO be renamed pspG.

Transcription activation by remodelling of a nucleoprotein assembly: the role of NarL at the FNR-dependent Escherichia coli nir promoter

Expression from the Escherichia coli nir promoter is co-dependent on both the FNR protein (an anaerobically triggered transcription activator) and NarL or NarP proteins (transcription activators triggered by nitrite and nitrate). We found previously that FNR binds to a site centred at position - 41.5 at the nir promoter, but that FNR-dependent activation is repressed by IHF binding to a site centred at position -88 (IHF I) and Fis binding to sites centred at positions -142 (Fis I) and +23 (Fis II). Here, we have studied the binding of purified IHF, Fis and FNR to the nir promoter in vitro. Our results show that the nir promoter contains a second IHF site at position -115 (IHF II) and a third Fis site at position -97 (Fis III), and that IHF, Fis and FNR can bind together to form multiprotein complexes. Surprisingly, IHF binding at the IHF II site increases FNR-dependent activation by decreasing the repression mediated by IHF and Fis binding at the other sites. In previous work, we found that NarL or NarP activates the nir promoter by binding to a site centred at position -69.5 and counteracting the repressive effects of IHF and Fis. We now show that NarL can displace IHF bound at the IHF I site, but IHF is unable to displace bound NarL. We suggest that NarL interferes with IHF binding at the nir promoter by distorting the minor groove at its target site, and we argue that the resulting activation by NarL results from remodelling of the local nucleoprotein structure to facilitate FNR-dependent transcription.

Towards a comprehensive understanding ofBacillus subtiliscell physiology by physiological proteomics

Using Bacillus subtilis as a model system for functional genomics, this review will provide insights how proteomics can be used to bring the virtual life of genes to the real life of proteins. Physiological proteomics will generate a new and broad understanding of cellular physiology because the majority of proteins synthesized in the cell can be visualized. From a physiological point of view two major proteome fractions can be distinguished: proteomes of growing cells and proteomes of nongrowing cells. In the main analytical window almost 50% of the vegetative proteome expressed in growing cells of B. subtilis were identified. This proteomic view of growing cells can be employed for analyzing the regulation of entire metabolic pathways and thus opens the chance for a comprehensive understanding of metabolism and growth processes of bacteria. Proteomics, on the other hand, is also a useful tool for analyzing the adaptational network of nongrowing cells that consists of several partially overlapping regulation groups induced by stress/starvation stimuli. Furthermore, proteomic signatures for environmental stimuli can not only be applied to predict the physiological state of cells, but also offer various industrial applications from fermentation monitoring up to the analysis of the mode of action of drugs. Even if DNA array technologies currently provide a better overview of the gene expression profile than proteome approaches, the latter address biological problems in which they can not be replaced by mRNA profiling procedures. This proteomics of the second generation is a powerful tool for analyzing global control of protein stability, the protein interaction network, protein secretion or post-translational modifications of proteins on the way towards the elucidation of the mystery of life.

Charting gene regulatory networks: strategies, challenges and perspectives

One of the foremost challenges in the post-genomic era will be to chart the gene regulatory networks of cells, including aspects such as genome annotation, identification of cis-regulatory elements and transcription factors, information on protein-DNA and protein-protein interactions, and data mining and integration. Some of these broad sets of data have already been assembled for building networks of gene regulation. Even though these datasets are still far from comprehensive, and the approach faces many important and difficult challenges, some strategies have begun to make connections between disparate regulatory events and to foster new hypotheses. In this article we review several different genomics and proteomics technologies, and present bioinformatics methods for exploring these data in order to make novel discoveries.

Genomic studies with Escherichia coli MelR protein: applications of chromatin immunoprecipitation and microarrays

Escherichia coli MelR protein is a transcription activator that is essential for melibiose-dependent expression of the melAB genes. We have used chromatin immunoprecipitation to study the binding of MelR and RNA polymerase to the melAB promoter in vivo. Our results show that MelR is associated with promoter DNA, both in the absence and presence of the inducer melibiose. In contrast, RNA polymerase is recruited to the melAB promoter only in the presence of inducer. The MelR DK261 positive control mutant binds to the melAB promoter but cannot recruit RNA polymerase. Further analysis of immunoprecipitated DNA, by using an Affymetrix GeneChip array, showed that the melAB promoter is the major, if not the sole, target in E. coli for MelR. This was confirmed by a transcriptomics experiment to analyze RNA in cells either with or without melR.

Genomic analysis of regulatory network dynamics reveals large topological changes

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here--particularly the large-scale topological changes and hub transience--will apply to other biological networks, including complex sub-systems in higher eukaryotes.

Genes regulated by TorR, the trimethylamine oxide response regulator of Shewanella oneidensis

The torECAD operon encoding the trimethylamine oxide (TMAO) respiratory system of Shewanella oneidensis is positively controlled by the TorS/TorR two-component system when TMAO is available. Activation of the tor operon occurs upon binding of the phosphorylated response regulator TorR to a single operator site containing the direct repeat nucleotide sequence TTCATAN4TTCATA. Here we show that the replacement of any nucleotide of one TTCATA hexamer prevented TorR binding in vitro, meaning that TorR specifically interacts with this DNA target. Identical direct repeat sequences were found in the promoter regions of torR and of the new gene torF (SO4694), and they allowed TorR binding to both promoters. Real-time PCR experiments revealed that torR is negatively autoregulated, whereas torF is strongly induced by TorR in response to TMAO. Transcription start site location and footprinting analysis indicate that the operator site at torR overlaps the promoter -10 box, whereas the operator site at torF is centered at -74 bp from the start site, in agreement with the opposite role of TorR in the regulation of the two genes. Since torF and torECAD are positively coregulated by TorR, we propose that the TorF protein plays a role related to TMAO respiration.

The regulation of bacterial transcription initiation

Bacteria use their genetic material with great effectiveness to make the right products in the correct amounts at the appropriate time. Studying bacterial transcription initiation in Escherichia coli has served as a model for understanding transcriptional control throughout all kingdoms of life. Every step in the pathway between gene and function is exploited to exercise this control, but for reasons of economy, it is plain that the key step to regulate is the initiation of RNA-transcript formation.

RegulonDB (version 4.0): transcriptional regulation, operon organization and growth conditions in Escherichia coli K-12

RegulonDB is the primary database of the major international maintained curation of original literature with experimental knowledge about the elements and interactions of the network of transcriptional regulation in Escherichia coli K-12. This includes mechanistic information about operon organization and their decomposition into transcription units (TUs), promoters and their sigma type, binding sites of specific transcriptional regulators (TRs), their organization into 'regulatory phrases', active and inactive conformations of TRs, as well as terminators and ribosome binding sites. The database is complemented with clearly marked computational predictions of TUs, promoters and binding sites of TRs. The current version has been expanded to include information beyond specific mechanisms aimed at gathering different growth conditions and the associated induced and/or repressed genes. RegulonDB is now linked with Swiss-Prot, with microarray databases, and with a suite of programs to analyze and visualize microarray experiments. We provide a summary of the biological knowledge contained in RegulonDB and describe the major changes in the design of the database. RegulonDB can be accessed on the web at the URL: http://www.cifn.unam.mx/Computational_Biology/regulondb/.

Regulatory network of Escherichia coli: consistency between literature knowledge and microarray profiles

The transcriptional network of Escherichia coli may well be the most complete experimentally characterized network of a single cell. A rule-based approach was built to assess the degree of consistency between whole-genome microarray experiments in different experimental conditions and the accumulated knowledge in the literature compiled in RegulonDB, a data base of transcriptional regulation and operon organization in E. coli. We observed a high and statistical significant level of consistency, ranging from 70%-87%. When effector metabolites of regulatory proteins are not considered in the prediction of the active or inactive state of the regulators, consistency falls by up to 40%. Similarly, consistency decreases when rules for multiple regulatory interactions are altered or when "on" and "off" entries were assigned randomly. We modified the initial state of regulators and evaluated the propagation of errors in the network that do not correlate linearly with the connectivity of regulators. We interpret this deviation mainly as a result of the existence of redundant regulatory interactions. Consistency evaluation opens a new space of dialogue between theory and experiment, as the consequences of different assumptions can be evaluated and compared.

Environmental conditions and transcriptional regulation inEscherichia coli: a physiological integrative approach

Bacteria develop a number of devices for sensing, responding, and adapting to different environmental conditions. Understanding within a genomic perspective how the transcriptional machinery of bacteria is modulated, as a response for changing conditions, is a major challenge for biologists. Knowledge of which genes are turned on or turned off under specific conditions is essential for our understanding of cell behavior. In this study we describe how the information pertaining to gene expression and associated growth conditions (even with very little knowledge of the associated regulatory mechanisms) is gathered from the literature and incorporated into RegulonDB, a database on transcriptional regulation and operon organization in E. coli. The link between growth conditions, signal transduction, and transcriptional regulation is modeled in the database in a simple format that highlights biological relevant information. As far as we know, there is no other database that explicitly clarifies the effect of environmental conditions on gene transcription. We discuss how this knowledge constitutes a benchmark that will impact future research aimed at integration of regulatory responses in the cell; for instance, analysis of microarrays, predicting culture behavior in biotechnological processes, and comprehension of dynamics of regulatory networks. This integrated knowledge will contribute to the future goal of modeling the behavior of E. coli as an entire cell. The RegulonDB database can be accessed on the web at the URL: http://www.cifn.unam.mx/Computational_Biology/regulondb/.