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.
Program of Computational Genomics, Centro de Investigación sobre Fijación de Nitrógeno, Univercidad Nacional Autónoma de México (CIFN-UNAM), Morelos 62100, México.
Rosa María
RM
Gutiérrez-Ríos
David A
DA
Rosenblueth
José Antonio
JA
Loza
Araceli M
AM
Huerta
Jeremy D
JD
Glasner
Fred R
FR
Blattner
Julio
J
Collado-Vides
D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics*; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics*; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D009876:Operon / Q000235:genetics; D011237:Predictive Value of Tests; D018085:Regulon / Q000235:genetics; D012107:Research Design