Two-Exponential Models of Gene Expression Patterns for Noisy Experimental Data

Journal of computational biology: A journal of computational molecular cell biology

DavidHolloway^5¹  AlexanderSpirov^6,7³  NinaGolyandina^3,4⁴  AlexShlemov^4⁵  TheodoreAlexandrov^1,2⁶ 

[1] Address correspondence to: Dr. Nina Golyandina, St. Petersburg State University, Universitetskaya nab. 7/9, St. Petersburg 199034, Russia^3;Computer Science and CEWIT, SUNY Stony Brook, Stony Brook, New York^6;Mathematics Department, British Columbia Institute of Technology, Burnaby, Canada^5;Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California of San Diego, La Jolla, California^2;St. Petersburg State University, St. Petersburg, Russia^4;Structural and Computational Biology Unit, EMBL, Heidelberg, Germany^1;The Sechenov Institute of Evolutionary Physiology and Biochemistry, St. Petersburg, Russia^7

关键词: bcd mRNA gradient;    Bicoid;    singular spectrum analysis;    spatial pattern;    two-exponential model;   

DOI  :  10.1089/cmb.2017.0063

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Spatial pattern formation of the primary anterior–posterior morphogenetic gradient of the transcription factor Bicoid (Bcd) has been studied experimentally and computationally for many years. Bcd specifies positional information for the downstream segmentation genes, affecting the fly body plan. More recently, a number of researchers have focused on the patterning dynamics of the underlying bcd messenger RNA (mRNA) gradient, which is translated into Bcd protein. New, more accurate techniques for visualizing bcd mRNA need to be combined with quantitative signal extraction techniques to reconstruct the bcd mRNA distribution. Here, we present a robust technique for quantifying gradients with a two-exponential model. This approach (1) has natural, biologically relevant parameters and (2) is invariant to linear transformations of the data arising due to variation in experimental conditions (e.g., microscope settings, nonspecific background signal). This allows us to quantify bcd mRNA gradient variability from embryo to embryo (important for studying the robustness of developmental regulatory networks); sort out atypical gradients; and classify embryos to developmental stage by quantitative gradient parameters.

【 授权许可】

Unknown