(314f) Mammalian Protein Expression Noise: General Scaling Principles and Implications for Knock-Down Experiments | AIChE

(314f) Mammalian Protein Expression Noise: General Scaling Principles and Implications for Knock-Down Experiments


Birtwistle, M. R. - Presenter, University College Dublin
Kholodenko, B. N. - Presenter, University College Dublin
Kolch, W. - Presenter, University College Dublin

The abundance of a particular protein varies both over time within a single mammalian cell and between cells of a genetically identical population [1-5]. Although this variability can be large [5], general principles of how this noise behaves in mammalian cells are not well understood. Here we present theoretical results supported by experimental evidence that cell-to-cell variability of several different mammalian proteins under a wide range of conditions is well described by a gamma distribution model, as recently found for E. coli [6]. Given this model, we predict and then verify experimentally that at least three-fold changes in expression are needed to separate control and perturbed populations cleanly in knock-down experiments. The model prescribes that when protein levels are manipulated by altering mRNA stability or through a tet-regulated transcription system, then the standard deviation of protein expression varies linearly with mean protein expression, but the coefficient of variation is constant. This prediction is verified experimentally in three different cell systems. Overall, our work presents a simple theoretical model that describes cell-to-cell variability in mammalian protein expression for different proteins in several cell lines under a wide range of conditions, and moreover provides a justified “three-fold change” heuristic for judging the efficacy of knock-down experiments.


1.         Raser, J.M., and O'Shea, E.K. (2005). Noise in gene expression: origins, consequences, and control. Science 309, 2010-2013.

2.         Raj, A., Peskin, C.S., Tranchina, D., Vargas, D.Y., and Tyagi, S. (2006). Stochastic mRNA synthesis in mammalian cells. PLoS Biol 4, e309.

3.         Spencer, S.L., Gaudet, S., Albeck, J.G., Burke, J.M., and Sorger, P.K. (2009). Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459, 428-432.

4.         Chang, H.H., Hemberg, M., Barahona, M., Ingber, D.E., and Huang, S. (2008). Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544-547.

5.         Sigal, A., Milo, R., Cohen, A., Geva-Zatorsky, N., Klein, Y., Liron, Y., Rosenfeld, N., Danon, T., Perzov, N., and Alon, U. (2006). Variability and memory of protein levels in human cells. Nature 444, 643-646.

6.         Taniguchi, Y., Choi, P.J., Li, G.W., Chen, H., Babu, M., Hearn, J., Emili, A., and Xie, X.S. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533-538.