% This file was created with JabRef 2.6. % Encoding: Cp1252 @BOOK{Barabasi2002, title = {Linked: The New Science of Networks}, publisher = {Perseus Books Group}, year = {2002}, author = {Albert-L\'{a}szl\'{o} Barab\'{a}si}, pages = {256}, edition = {First}, month = may, isbn = {0738206679}, owner = {jfreyre}, timestamp = {2008.07.09} } @ARTICLE{Freyre-Gonzalez2008, author = {Julio Augusto Freyre-Gonz\'{a}lez and Jos\'{e} Antonio Alonso-Pav\'{o}n and Luis Gerardo Treviño-Quintanilla and Julio Collado-Vides}, title = {Functional architecture of \textit{Escherichia coli}: new insights provided by a natural decomposition approach.}, journal = {Genome Biol}, year = {2008}, volume = {9}, pages = {R154}, number = {10}, month = {Oct}, abstract = {ABSTRACT: BACKGROUND: Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However, these approaches are not natural, as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here, we unravel the transcriptional regulatory network of Escherichia coli by separating it into its key elements, thus revealing its natural organization. We also present a mathematical criterion, based on the topological features of the transcriptional regulatory network, to classify the network elements into one of two possible classes: hierarchical or modular genes. RESULTS: We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode transcription factors responsible for coordinating module responses based on general interest signals. Hierarchical elements correlate highly with the previously studied global regulators, suggesting that this could be the first mathematical method to identify global regulators. We identified a new element in transcriptional regulatory networks never described before: intermodular genes. These are structural genes which integrate, at the promoter level, signals coming from different modules, and therefore from different physiological responses. Using the concept of pleiotropy, we have reconstructed the hierarchy of the network and discuss the role of feedforward motifs in shaping the hierarchical backbone of the transcriptional regulatory network. CONCLUSIONS: This study sheds new light on the design principles underpinning the organization of transcriptional regulatory networks, showing a novel nonpyramidal architecture comprised of independent modules globally governed by hierarchical transcription factors, whose responses are integrated by intermodular genes.}, doi = {10.1186/gb-2008-9-10-r154}, owner = {jfreyre}, pii = {gb-2008-9-10-r154}, pmid = {18954463}, timestamp = {2008.10.29}, url = {http://dx.doi.org/10.1186/gb-2008-9-10-r154} } @MISC{Freyre-Gonzalez2005, author = {Julio A. Freyre-Gonz\'{a}lez and Jos\'{e} A. Alonso-Pav\'{o}n and Daniel V\'{a}zquez-Hernandez and Mario Sandoval-Calderon and Mariana Matus-Garc\'{\i}a and Ortega-del Vecchyo, Diego and Julio Collado-Vides}, title = {Modular and hierarchical organization of the transcriptional regulatory network of \textit{Escherichia coli} K-12}, howpublished = {5th International Workshop on Bioinformatics and Systems Biology, Poster Session, Berlín, Alemania}, month = {August}, year = {2005}, abstract = {There are strong arguments that support the idea of modular organization in the cell [3]. A module is defined as a group of correlated elements that cooperate in a specific cellular function [3,1]. In genetic networks, these modules are integrated by transcription factors (TFs) and genes that act coordinately when specific stimuli are present. In biological networks there exist global TFs that interact with several elements of many modules. This makes difficult or impossible to classify those TFs into a single module. Consequently, we may classify the network’s elements into two groups: elements that belong to modules (genes and local TFs, which will hereafter be called modular elements), and elements that coordinate such modules in a hierarchical fashion (global TFs and sigma factors, which will hereafter be called control elements). This suggests that a methodology that will allow for the classification of the network’s genes in one of the aforementioned groups is required. Recently, topological analyses have suggested the existence of hierarchical modularity in the transcriptional regulatory network (TRN) of E. coli [2,6,5]. Nevertheless, these studies have neglected the importance of classifying genes in modular and control elements, as well as the existence of feedback circuits among them. Such feedback circuits could be interpreted as a mechanism by which control elements retrieve information about the status of genes in modules and, based on this feedback signal, generate decisions about the fate of the cell. Assuming these hypotheses, in our laboratory, we are working on an algorithm to propose a hierarchical structure of the TRN: 1. Using data from RegulonDB [7,4] we will decompose the network, through the analysis of the node degree and clustering coefficient distribution, into the aforementioned groups and temporally remove the control elements. 2. Using Monica Riley’s gene functional assignations [8] we will analyze the modules to determine whether they are physiologically correlated or not. 3. Finally, we will add the removed control elements to infer the hierarchical structure of the TRN. On this poster we will show the results obtained from applying this methodology to the TRN of E. coli.}, owner = {jfreyre}, timestamp = {2008.11.09} } @ARTICLE{Gottesman1984, author = {S. Gottesman}, title = {Bacterial regulation: global regulatory networks}, journal = {Annu Rev Genet}, year = {1984}, volume = {18}, pages = {415--441}, doi = {10.1146/annurev.ge.18.120184.002215}, keywords = {Aerobiosis; Anaerobiosis; Bacterial Proteins; Base Sequence; Cyclic AMP; DNA Repair; Energy Metabolism; \textit{Escherichia coli}; Gene Expression Regulation; Glucose; Heat-Shock Proteins; Nitrogen; Operon; Phosphate; Receptors, Cyclic AMP; Repressor Proteins; s}, owner = {jfreyre}, pmid = {6099091}, timestamp = {2008.02.05}, url = {http://dx.doi.org/10.1146/annurev.ge.18.120184.002215} } @ARTICLE{Griffith2002, author = {Kevin L Griffith and Ishita M Shah and Todd E Myers and Michael C O'Neill and Richard E Wolf}, title = {Evidence for ``pre-recruitment'' as a new mechanism of transcription activation in \textit{Escherichia coli}: the large excess of SoxS binding sites per cell relative to the number of SoxS molecules per cell}, journal = {Biochem Biophys Res Commun}, year = {2002}, volume = {291}, pages = {979--986}, number = {4}, month = {Mar}, abstract = {In response to the oxidative stress imposed by redox-cycling compounds like paraquat, \textit{Escherichia coli} induces the synthesis of SoxS, which then activates the transcription of approximately 100 genes. The DNA binding site for SoxS-dependent transcription activation, the "soxbox," is highly degenerate, suggesting that the genome contains a large number of SoxS binding sites. To estimate the number of soxboxes in the cell, we searched the \textit{E. coli} genome for SoxS binding sites using as query sequence the previously determined optimal SoxS binding sequence. We found approximately 12,500 sequences that match the optimal binding sequence under the conditions of our search; this agrees with our previous estimate, based on information theory, that a random sequence the size of the \textit{E. coli} genome contains approximately 13,000 soxboxes. Thus, fast-growing cells with 4-6 genomes per cell have approximately 65,000 soxboxes. This large number of potential SoxS binding sites per cell raises the interesting question of how SoxS distinguishes between the functional soxboxes located within the promoters of target genes and the plethora of equivalent but nonfunctional binding sites scattered throughout the chromosome. To address this question, we treated cells with paraquat and used Western blot analysis to determine the kinetics of SoxS accumulation per cell; we also determined the kinetics of SoxS-activated gene expression. The abundance of SoxS reached a maximum of 2,500 molecules per cell 20 min after induction and gradually declined to approximately 500 molecules per cell over the next 1.5 h. Given that activation of target gene expression began almost immediately and given the large disparity between the number of SoxS molecules per cell, 2,500, and the number of SoxS binding sites per cell, 65,000, we infer that SoxS is not likely to activate transcription by the usual "recruitment" pathway, as this mechanism would require a number of SoxS molecules similar to the number of soxboxes. Instead, we propose that SoxS first interacts in solution with RNA polymerase and then the binary complex scans the chromosome for promoters that contain a soxbox properly positioned and oriented for transcription activation. We name this new pathway "pre-recruitment."}, doi = {10.1006/bbrc.2002.6559}, institution = {Department of Biological Sciences, University of Maryland Baltimore County, Baltimore, Maryland 21250, USA.}, keywords = {Bacterial Proteins; Binding Sites; Blotting, Western; Cell Division; DNA-Binding Proteins; \textit{Escherichia coli}; \textit{Escherichia coli} Proteins; Gene Expression Regulation, Bacterial; Genome, Bacterial; Kinetics; Numerical Analysis, Computer-Assisted; Oxidative Stress; Paraquat; Protein Transport; Trans-Activation (Genetics); Trans-Activators; Transcription Factors}, owner = {jfreyre}, pii = {S0006291X02965599}, pmid = {11866462}, timestamp = {2008.02.05}, url = {http://dx.doi.org/10.1006/bbrc.2002.6559} } @ARTICLE{Hartwell1999, author = {L. H. Hartwell and J. J. Hopfield and S. Leibler and A. W. Murray}, title = {From molecular to modular cell biology}, journal = {Nature}, year = {1999}, volume = {402}, pages = {C47--C52}, number = {6761 Suppl}, month = {Dec}, abstract = {Cellular functions, such as signal transmission, are carried out by 'modules' made up of many species of interacting molecules. Understanding how modules work has depended on combining phenomenological analysis with molecular studies. General principles that govern the structure and behaviour of modules may be discovered with help from synthetic sciences such as engineering and computer science, from stronger interactions between experiment and theory in cell biology, and from an appreciation of evolutionary constraints.}, doi = {10.1038/35011540}, institution = {Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA.}, keywords = {Action Potentials; Evolution; Forecasting; Models, Biological; Molecular Biology}, owner = {jfreyre}, pmid = {10591225}, timestamp = {2008.02.05}, url = {http://dx.doi.org/10.1038/35011540} } @ARTICLE{Keseler2005, author = {Ingrid M Keseler and Julio Collado-Vides and Socorro Gama-Castro and John Ingraham and Suzanne Paley and Ian T Paulsen and Martín Peralta-Gil and Peter D Karp}, title = {EcoCyc: a comprehensive database resource for \textit{Escherichia coli}}, journal = {Nucleic Acids Res}, year = {2005}, volume = {33}, pages = {D334--D337}, number = {Database issue}, month = {Jan}, abstract = {The EcoCyc database (http://EcoCyc.org/) is a comprehensive source of information on the biology of the prototypical model organism \textit{Escherichia coli} K12. The mission for EcoCyc is to contain both computable descriptions of, and detailed comments describing, all genes, proteins, pathways and molecular interactions in E.coli. Through ongoing manual curation, extensive information such as summary comments, regulatory information, literature citations and evidence types has been extracted from 8862 publications and added to Version 8.5 of the EcoCyc database. The EcoCyc database can be accessed through a World Wide Web interface, while the downloadable Pathway Tools software and data files enable computational exploration of the data and provide enhanced querying capabilities that web interfaces cannot support. For example, EcoCyc contains carefully curated information that can be used as training sets for bioinformatics prediction of entities such as promoters, operons, genetic networks, transcription factor binding sites, metabolic pathways, functionally related genes, protein complexes and protein-ligand interactions.}, doi = {10.1093/nar/gki108}, institution = {SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA.}, keywords = {Computational Biology; Databases, Genetic; \textit{Escherichia coli} K12; \textit{Escherichia coli} Proteins; Gene Expression Regulation, Bacterial; Genome, Bacterial; Genomics; Software; User-Computer Interface}, owner = {jfreyre}, pii = {33/suppl_1/D334}, pmid = {15608210}, timestamp = {2008.02.05}, url = {http://dx.doi.org/10.1093/nar/gki108} } @ARTICLE{Leskovec2008, author = {Jure Leskovec and Eric Horvitz}, title = {Planetary-scale views on an instant-messaging network}, year = {2008}, month = mar, abstract = {We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by ``six degrees of separation'' and find that the average path length among Messenger users is 6.6. We also find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender.}, eprint = {arXiv:0803.0939v1 [physics.soc-ph]}, keywords = {Physics - Physics and Society}, owner = {jfreyre}, timestamp = {2008.07.01}, url = {http://arxiv.org/abs/0803.0939} } @BOOK{Lipschutz1986, title = {Estructura de Datos}, publisher = {Mcgraw-Hill}, year = {1986}, author = {Seymour Lipschutz}, pages = {352}, series = {Serie Schaum}, month = dec, isbn = {0070380015}, owner = {jfreyre}, timestamp = {2008.07.09} } @INCOLLECTION{Marconi1967, author = {Guglielmo Marconi}, title = {Wireless Telegraphic Communication}, booktitle = {Physics 1901--1921}, publisher = {Elsevier Publishing Company}, year = {1967}, series = {Nobel Lectures}, pages = {196--222}, address = {Amsterdam}, owner = {jfreyre}, timestamp = {2008.06.30} } @INCOLLECTION{Neidhardt1996, author = {Neidhardt, F. C. and Savageau, M.}, title = {Regulation beyond the operon}, booktitle = {Escherichia coli and Salmonella: Cellular and Molecular Biology}, publisher = {American Society for Microbiology}, year = {1996}, editor = {Neidhardt, F. C.}, pages = {1310--1324}, address = {Washington D.C.}, edition = {Second}, owner = {jfreyre}, timestamp = {2008.04.18} } @BOOK{Nelson2000, title = {Lehninger Principles of Biochemistry}, publisher = {W. H. Freeman}, year = {2000}, author = {David L. Nelson and Michael M. Cox}, pages = {1200}, edition = {Third}, month = feb, isbn = {1572599316}, owner = {jfreyre}, timestamp = {2008.07.25} }