tailieunhanh - Báo cáo khoa học: Computational processing and error reduction strategies for standardized quantitative data in biological networks

High-quality quantitative data generated under standardized conditions is critical for understanding dynamic cellular processes. We report strategies for error reduction, and algorithms for automated data processing and for establishing the widely used techniques of immunoprecipitation and immu-noblotting as highly precise methods for the quantification of protein levels and modifications. | iFEBS Journal Computational processing and error reduction strategies for standardized quantitative data in biological networks Marcel Schilling1 Thomas Maiwald2 z Sebastian Bohl1 Markus Kollmann2 Clemens Kreutz2 Jens Timmer2 and Ursula Klingmuller1 1 German Cancer Research Center Heidelberg Germany 2 Freiburg Center for Data Analysis and Modeling University of Freiburg Germany Keywords data processing error reduction normalization quantitative immunoblotting signaling pathways Correspondence U. Klingmuller German Cancer Research Center Im Neuenheimer Feld 280 69120 Heidelberg Germany Fax 49 6221 424488 Tel 49 6221 424481 E-mail Authors who contributed equally to the work presented in this article. Received 8 September 2005 revised 25 October 2005 accepted 27 October 2005 doi High-quality quantitative data generated under standardized conditions is critical for understanding dynamic cellular processes. We report strategies for error reduction and algorithms for automated data processing and for establishing the widely used techniques of immunoprecipitation and immunoblotting as highly precise methods for the quantification of protein levels and modifications. To determine the stoichiometry of cellular components and to ensure comparability of experiments relative signals are converted to absolute values. A major source for errors in blotting techniques are inhomogeneities of the gel and the transfer procedure leading to correlated errors. These correlations are prevented by randomized gel loading which significantly reduces standard deviations. Further error reduction is achieved by using housekeeping proteins as normalizers or by adding purified proteins in immunoprecipitations as calibrators in combination with criteria-based normalization. Additionally we developed a computational tool for automated normalization validation and integration of data derived from multiple immunoblots. In this way large sets of