Journal
IFAC PAPERSONLINE
Volume 49, Issue 26, Pages 56-62Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ifacol.2016.12.103
Keywords
Estimators; Likelihood function; Maximum likelihood principle; Monte Carlo simulation; Ratio distribution
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Funding
- German Federal Ministry of Education and Research (BMBE) [FKZ0316186A]
- German Research Foundation (DFG) [EXC 310/2]
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This study elaborates on the normalization of data from Western blot experiments and its impact on parameter estimation. Western blot data have to be preprocessed appropriately in order to enable comparison across different replicates. This includes a two step normalization procedure, in which the raw signals are normalized to a loading control and additionally to a reference condition. If the signals themselves are normally distributed, the normalized data are described by ratios of normal distributions, which have some peculiarities that can complicate further analysis such as parameter estimation for biochemical network reconstruction. Here we shortly recapitulate some properties of these ratio distributions and conditions for various approximations that facilitate further analysis. We illustrate results on a case study in which Western blot data used to infer the fold change further a knockdown experiment. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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