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

Jitendra S. Kanodia, Yoosik Kim, Raju Tomer, Zia Khan, Kwanghun Chung, John D. Storey, Hang Lu, Philipp J. Keller, Stanislav Yefimovic Shvartsman

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)4867-4874
Number of pages8
JournalDevelopment
Volume138
Issue number22
DOIs
StatePublished - Nov 15 2011

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Developmental Biology

Keywords

  • Computational biology
  • Confidence interval
  • Dorsal gradient
  • Drosophila embryo
  • Morphogen gradient
  • Statistics

Fingerprint Dive into the research topics of 'A computational statistics approach for estimating the spatial range of morphogen gradients'. Together they form a unique fingerprint.

Cite this