Bioinformatics Advance Access originally published online on September 3, 2007
Bioinformatics 2007 23(20):2747-2753; doi:10.1093/bioinformatics/btm397
An error model for protein quantification
1Freiburg Center for Data Analysis and Modeling FDM, Eckerstrasse 1, 2Department for Physics, University of Freiburg, Hermann-Herder-Strasse 3, D-79104 Freiburg and 3Department of Medicine II, University Hospital of Freiburg, Hugstetter Strasse 55, D-79106 Freiburg, Germany
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Quantitative experimental data is the critical bottleneck in the modeling of dynamic cellular processes in systems biology. Here, we present statistical approaches improving reproducibility of protein quantification by immunoprecipitation and immunoblotting.
Results: Based on a large data set with more than 3600 data points, we unravel that the main sources of biological variability and experimental noise are multiplicative and log-normally distributed. Therefore, we suggest a log-transformation of the data to obtain additive normally distributed noise. After this transformation, common statistical procedures can be applied to analyze the data.
An error model is introduced to account for technical as well as biological variability. Elimination of these systematic errors decrease variability of measurements and allow for a more precise estimation of underlying dynamics of protein concentrations in cellular signaling.
The proposed error model is relevant for simulation studies, parameter estimation and model selection, basic tools of systems biology.
Availability: Matlab and R code is available from the authors on request. The data can be downloaded from our website www.fdm.uni-freiburg.de/
ckreutz/data.
Contact: ckreutz{at}fdm.uni-freiburg.de
The authors wish it to be known that, in this opinion, the first two authors should be regarded as joint First Authors.
Associate Editor: Burkhard Rost
Received on May 7, 2007; revised on July 11, 2007; accepted on August 2, 2007
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