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@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}
}