A computational statistics approach for estimating the spatial range of morphogen gradients

Author:

Kanodia Jitendra S.1,Kim Yoosik1,Tomer Raju2,Khan Zia3,Chung Kwanghun4,Storey John D.5,Lu Hang4,Keller Philipp J.2,Shvartsman Stanislav Y.1

Affiliation:

1. Department of Chemical and Biological Engineering and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

2. Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, 20147, USA.

3. Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

4. School of Chemical and Biomolecular Engineering and Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta GA 30332, USA.

5. Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

Abstract

A crucial issue in studies of morphogen gradients relates to their range: the distance over which they can act as direct regulators of cell signaling, gene expression and cell differentiation. To address this, we present a straightforward statistical framework that can be used in multiple developmental systems. We illustrate the developed approach by providing a point estimate and confidence interval for the spatial range of the graded distribution of nuclear Dorsal, a transcription factor that controls the dorsoventral pattern of the Drosophila embryo.

Publisher

The Company of Biologists

Subject

Developmental Biology,Molecular Biology

Reference42 articles.

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4. Feature tracking with automatic selection of spatial scales;Bretzner;Computer Vision Image Understanding,1998

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