Information on Transcriptional Regulation and Signal Transduction of _Escherichia coli_ K-12 Integrated in the Database RegulonDB.

Since its inception, RegulonDB ("http://regulondb.ccg.unam.mx/":http://regulondb.ccg.unam.mx/) has been a database that compiles information about the regulation of transcription initiation of _Escherichia coli_ K-12. However, we are aware that transcriptional regulation is not an isolated process; instead, it is the response to the different environmental conditions that trigger a series of concatenated reactions that end in transcriptional regulation, and it implies an adequate response in terms of induced and repressed gene products. We are working now to include all these new data in RegulonDB. As a consequence, transcriptional regulation in RegulonDB will be part of a unit that initiates with the signal, continues with the signal transduction to the core of regulation to modify expression of the affected set of target genes, and ends with an adequate response. We refer to these units as genetic sensory response units, or Gensor Units.

The inclusion of Gensor Units will bring a dramatic change and expansion of RegulonDB, due to the fact that we will be adding several new types of reactions and interactions. We started to collect data about signal transduction of the sigma factors, the two-component systems, of some transcription factors involved in carbon source utilization, and of genes involved in the synthesis of amino acids. We plan a high-level curation with super-pathways summarizing concatenated sets of reactions linked to those other databases that curate such information, while enabling with RegulonDB a compilation of complete Gensor Units.

In addition, the number of DNA binding sites for some transcription factors has grown considerably, and therefore we decided to review systematically those sites whose lengths ranging from 40 to 60 bp with orientation and consensus sequences that are not easy to identify. The current version of RegulonDB is the beginning of a higher-level curation of gene regulation information, and eventually our database will include all regulatory mechanisms and their regulated genes. 
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Information on Transcriptional Regulation and Signal Transduction of _Escherichia coli_ K-12 Integrated in the Database RegulonDB.

Since its inception, RegulonDB ("http://regulondb.ccg.unam.mx/":http://regulondb.ccg.unam.mx/) has been a database that compiles information about the regulation of transcription initiation of _Escherichia coli_ K-12. However, we are aware that transcriptional regulation is not an isolated process; instead, it is the response to the different environmental conditions that trigger a series of concatenated reactions that end in transcriptional regulation, and it implies an adequate response in terms of induced and repressed gene products. We are working now to include all these new data in RegulonDB. As a consequence, transcriptional regulation in RegulonDB will be part of a unit that initiates with the signal, continues with the signal transduction to the core of regulation to modify expression of the affected set of target genes, and ends with an adequate response. We refer to these units as genetic sensory response units, or geSorgans.

The inclusion of geSorgans will bring a dramatic change and expansion of RegulonDB, due to the fact that we will be adding several new types of reactions and interactions. We started to collect data about signal transduction of the sigma factors, the two-component systems, of some transcription factors involved in carbon source utilization, and of genes involved in the synthesis of amino acids. We plan a high-level curation with super-pathways summarizing concatenated sets of reactions linked to those other databases that curate such information, while enabling with RegulonDB a compilation of complete geSorgans.

In addition, the number of DNA binding sites for some transcription factors has grown considerably, and therefore we decided to review systematically those sites whose lengths ranging from 40 to 60 bp with orientation and consensus sequences that are not easy to identify. The current version of RegulonDB is the beginning of a higher-level curation of gene regulation information, and eventually our database will include all regulatory mechanisms and their regulated genes. 
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El impacto de la Inteligencia Artificial en la mejora de la atención al cliente: Una revisión sistémica

Artificial Intelligence (AI) is gaining more and more importance in the customer service industry, making it possible to automate processes and improve the effectiveness of customer interactions. This systematic review aims to analyze the use of artificial intelligence in customer service, identify the limitations and advantages of the implementation of AI and analyze how they influence customer satisfaction, the collection of literature and selection was used. the guidelines of the PRISMA methodology. As AI has become an increasingly relevant technology in the field of customer service, it is essential to systematically examine its influence in this specific context. It was found in which sectors AI is being used more in customer service and the future perspectives of this technology in customer service will be presented. It was possible to conclude that chatbots are one of the most used applications in customer service to provide quick and effective responses to user queries. However, it is important to consider the benefits and limitations of this technology, as well as the importance of human interaction in customer satisfaction.La Inteligencia Artificial (IA) está ganando una importancia creciente en el sector del servicio al cliente, permitiendo automatizar procesos y mejorar la eficacia de las interacciones con los clientes. Esta revisión sistemática tiene como objetivo explorar de cómo se está utilizando la IA en el servicio de atención al cliente, identificar las limitaciones y ventajas de la implementación de la IA y analizar cómo influyen en la satisfacción del cliente, la recolección de literatura y selección se utilizó las pautas de la metodología PRISMA. A medida que la IA se ha convertido en una tecnología cada vez más relevante en el ámbito del servicio al cliente, es esencial examinar de manera sistemática su influencia en este contexto específico. Se encontró en que sectores se está utilizando más la IA en la atención al cliente y se presentarán las perspectivas futuras de esta tecnología en el servicio al cliente. Se pudo concluir que los chatbots son una de las aplicaciones más utilizadas en la atención al cliente para brindar respuestas rápidas y efectivas a las consultas de los usuarios. Sin embargo, es importante considerar los beneficios y limitaciones de esta tecnología, así como la importancia de la interacción humana en la satisfacción del cliente

The comprehensive updated regulatory network of Escherichia coli K-12

BACKGROUND: Escherichia coli is the model organism for which our knowledge of its regulatory network is the most extensive. Over the last few years, our project has been collecting and curating the literature concerning E. coli transcription initiation and operons, providing in both the RegulonDB and EcoCyc databases the largest electronically encoded network available. A paper published recently by Ma et al. (2004) showed several differences in the versions of the network present in these two databases. Discrepancies have been corrected, annotations from this and other groups (Shen-Orr et al., 2002) have been added, making the RegulonDB and EcoCyc databases the largest comprehensive and constantly curated regulatory network of E. coli K-12. RESULTS: Several groups have been using these curated data as part of their bioinformatics and systems biology projects, in combination with external data obtained from other sources, thus enlarging the dataset initially obtained from either RegulonDB or EcoCyc of the E. coli K12 regulatory network. We kindly obtained from the groups of Uri Alon and Hong-Wu Ma the interactions they have added to enrich their public versions of the E. coli regulatory network. These were used to search for original references and curate them with the same standards we use regularly, adding in several cases the original references (instead of reviews or missing references), as well as adding the corresponding experimental evidence codes. We also corrected all discrepancies in the two databases available as explained below. CONCLUSION: One hundred and fifty new interactions have been added to our databases as a result of this specific curation effort, in addition to those added as a result of our continuous curation work. RegulonDB gene names are now based on those of EcoCyc to avoid confusion due to gene names and synonyms, and the public releases of RegulonDB and EcoCyc are henceforth synchronized to avoid confusion due to different versions. Public flat files are available providing direct access to the regulatory network interactions thus avoiding errors due to differences in database modelling and representation. The regulatory network available in RegulonDB and EcoCyc is the most comprehensive and regularly updated electronically-encoded regulatory network of E. coli K-12

EcoCyc: fusing model organism databases with systems biology.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc

RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions

RegulonDB is the internationally recognized reference database of Escherichia coli K-12 offering curated knowledge of the regulatory network and operon organization. It is currently the largest electronically-encoded database of the regulatory network of any free-living organism. We present here the recently launched RegulonDB version 5.0 radically different in content, interface design and capabilities. Continuous curation of original scientific literature provides the evidence behind every single object and feature. This knowledge is complemented with comprehensive computational predictions across the complete genome. Literature-based and predicted data are clearly distinguished in the database. Starting with this version, RegulonDB public releases are synchronized with those of EcoCyc since our curation supports both databases. The complex biology of regulation is simplified in a navigation scheme based on three major streams: genes, operons and regulons. Regulatory knowledge is directly available in every navigation step. Displays combine graphic and textual information and are organized allowing different levels of detail and biological context. This knowledge is the backbone of an integrated system for the graphic display of the network, graphic and tabular microarray comparisons with curated and predicted objects, as well as predictions across bacterial genomes, and predicted networks of functionally related gene products. Access RegulonDB at

Multidimensional annotation of the Escherichia coli K-12 genome

The annotation of the Escherichia coli K-12 genome in the EcoCyc database is one of the most accurate, complete and multidimensional genome annotations. Of the 4460 E. coli genes, EcoCyc assigns biochemical functions to 76%, and 66% of all genes had their functions determined experimentally. EcoCyc assigns E. coli genes to Gene Ontology and to MultiFun. Seventy-five percent of gene products contain reviews authored by the EcoCyc project that summarize the experimental literature about the gene product. EcoCyc information was derived from 15 000 publications. The database contains extensive descriptions of E. coli cellular networks, describing its metabolic, transport and transcriptional regulatory processes. A comparison to genome annotations for other model organisms shows that the E. coli genome contains the most experimentally determined gene functions in both relative and absolute terms: 2941 (66%) for E. coli, 2319 (37%) for Saccharomyces cerevisiae, 1816 (5%) for Arabidopsis thaliana, 1456 (4%) for Mus musculus and 614 (4%) for Drosophila melanogaster. Database queries to EcoCyc survey the global properties of E. coli cellular networks and illuminate the extent of information gaps for E. coli, such as dead-end metabolites. EcoCyc provides a genome browser with novel properties, and a novel interactive display of transcriptional regulatory networks

A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0

Abstract Background Our understanding of the regulation of gene expression has benefited from the availability of high-throughput technologies that interrogate the whole genome for the binding of specific transcription factors and gene expression profiles. In the case of widely used model organisms, such as Escherichia coli K-12, the new knowledge gained from these approaches needs to be integrated with the legacy of accumulated knowledge from genetic and molecular biology experiments conducted in the pre-genomic era in order to attain the deepest level of understanding possible based on the available data. Results In this paper, we describe an expansion of RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization in E. coli K-12, to include the genome-wide dataset collections from 32 ChIP and 19 gSELEX publications, in addition to around 60 genome-wide expression profiles relevant to the functional significance of these datasets and used in their curation. Three essential features for the integration of this information coming from different methodological approaches are: first, a controlled vocabulary within an ontology for precisely defining growth conditions; second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated transcription factor binding sites without such support; and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration needed to manage and access such wealth of knowledge. Conclusions This version 10.0 of RegulonDB is a first step toward what should become the unifying access point for current and future knowledge on gene regulation in E. coli K-12. Furthermore, this model platform and associated methodologies and criteria can be emulated for gathering knowledge on other microbial organisms