Quantitative data generation for systems biology: the impact of randomisation, calibrators and normalisers.

Abstract

Systems biology is an approach to the analysis and prediction of the dynamic behaviour of biological networks through mathematical modelling based on experimental data. The current lack of reliable quantitative data, especially in the field of signal transduction, means that new methodologies in data acquisition and processing are needed. Here, we present methods to advance the established techniques of immunoprecipitation and immunoblotting to more accurate and quantitative procedures. We propose randomisation of sample loading to disrupt lane correlations and the use of normalisers and calibrators for data correction. To predict the impact of each method on improving the data quality we used simulations. These studies showed that randomisation reduces the standard deviation of a smoothed signal by 55% +/- 10%, independently from most experimental settings. Normalisation with appropriate endogenous or external proteins further reduces the deviation from the true values. As the improvement strongly depends on the quality of the normaliser measurement, a criteria-based normalisation procedure was developed. Our approach was experimentally verified by application of the proposed methods to time course data obtained by the immunoblotting technique. This analysis showed that the procedure is robust and can significantly improve the quality of experimental data.