Journal Article

Statistical lower bounds on protein copy number from fluorescence expression images

Lee Zamparo and Theodore J. Perkins

in Bioinformatics

Volume 25, issue 20, pages 2670-2676
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI:

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology


Show Summary Details


Motivation: Fluorescence imaging has become a commonplace for quantitatively measuring mRNA or protein expression in cells and tissues. However, such expression data are usually relative—absolute concentrations or molecular copy numbers are typically not known. While this is satisfactory for many applications, for certain kinds of quantitative network modeling and analysis of expression noise, absolute measures of expression are necessary.

Results: We propose two methods for estimating molecular copy numbers from single uncalibrated expression images of tissues. These methods rely on expression variability between cells, due either to steady-state fluctuations or unequal distribution of molecules during cell division, to make their estimates. We apply these methods to 152 protein fluorescence expression images of Drosophila melanogaster embryos during early development, generating copy number estimates for 14 genes in the segmentation network. We also analyze the effects of noise on our estimators and compare with empirical findings. Finally, we confirm an observation of Bar-Even et al., made in the much different setting of Saccharomyces cerevisiae, that steady-state expression variance tends to scale with mean expression.

Availability: The data are all drawn from FlyEx (explained within), and is available at Data and MATLAB codes for all algorithms described in this article are available at


Journal Article.  5659 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.