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Gene Regulation and Systems Biology

Synopsis: An open access, peer reviewed electronic journal that covers regulation of genes and proteins they encode and the broader field of systems biology.


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About this journal

ISSN: 1177-6250


Aims and scope:

Gene Regulation and Systems Biology is concerned with the regulation of genes and the proteins they encode and the relationship between gene regulation and the wider field of systems biology.

The regulation of genes and the proteins they encode is at the core of systems biology. Understanding the role of gene regulation in the context of the entire system as it relates to disease processes will aid therapeutic development. Therefore, combining our knowledge of gene expression and promoter control, improving gene and protein networks, and determining the role of signal transduction will enhance our ability to treat complex diseases.

Systems biology is concerned with the integration of different levels of information to understand how complex biological systems function. By studying the relationships and interactions between various parts of a biological system (including gene and protein networks involved in cell signaling) it may be possible to create an understandable model of the whole system. Mathematical, analytic, and particularly computer simulation and heuristics are used as research methods.

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Submissions, excluding editorials, letters to the editor and dedications, will be peer reviewed by two reviewers.  Reviewers are required to provide fair, balanced and constructive reports.  

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Image Analysis and Data Normalization Procedures are Crucial for Microarray Analyses

Authors: Ali Kpatcha Kadanga, Christine Leroux, Muriel Bonnet, Stéphanie Chauvet, Bruno Meunier, Isabelle Cassar-Malek and Jean-François Hocquette
Publication Date: 17 Mar 2008
Gene Regulation and Systems Biology 2008:2 107-112

Ali Kpatcha Kadanga, Christine Leroux, Muriel Bonnet, Stéphanie Chauvet, Bruno Meunier, Isabelle Cassar-Malek and Jean-François Hocquette

INRA, UR1213, Unité de Recherches sur les Herbivores, Centre de Recherches de Clermont- Ferrand/Theix, F-63122 Saint Genès-Champanelle, France.

Abstract

This study was conducted with the aim of optimizing the experimental design of array experiments. We compared two image analysis and normalization procedures prior to data analysis using two experimental designs. For this, RNA samples from Charolais steers Longissimus thoracis muscle and subcutaneous adipose tissues were labeled and hybridized to a bovine 8,400 oligochip either in triplicate or in a dye-swap design. Image analysis and normalization were processed by either GenePix/MadScan or ImaGene/GeneSight. Statistical data analysis was then run using either the SAM method or a Student’s t-test using a multiple test correction run on R 2.1 software. Our results show that image analysis and normalization procedure had an impact whereas the statistical methods much less influenced the outcome of differentially expressed genes. Image analysis and data normalization are thus an important aspect of microarray experiments, having a potentially significant impact on downstream analyses such as the identification of differentially expressed genes. This study provides indications on the choice of raw data preprocessing in microarray technology.



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