Robust reconstruction of gene expression profiles from reporter gene data using linear inversion.
Bioinformatics
; 31(12): i71-9, 2015 Jun 15.
Article
en En
| MEDLINE
| ID: mdl-26072511
ABSTRACT
MOTIVATION Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest. RESULTS:
We propose a general approach based on regularized linear inversion to solve a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles. We evaluate the validity of the approach using in silico simulation studies, and observe that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data. We apply the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with Escherichia coli. The methods are capable of reliably reconstructing time-course profiles of growth rate, promoter activity and protein concentration from weak and noisy signals at low population volumes. Moreover, they capture critical features of those profiles, notably rapid changes in gene expression during growth transitions. AVAILABILITY AND IMPLEMENTATION The methods described in this article are made available as a Python package (LGPL license) and also accessible through a web interface. For more information, see https//team.inria.fr/ibis/wellinverter.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Regulación Bacteriana de la Expresión Génica
/
Genes Reporteros
/
Perfilación de la Expresión Génica
/
Proteínas de Escherichia coli
/
Escherichia coli
/
Genes Bacterianos
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2015
Tipo del documento:
Article