A theoretical framework for the regulation of Shh morphogen-controlled gene expression

Development. 2014 Oct;141(20):3868-78. doi: 10.1242/dev.112573.


How morphogen gradients govern the pattern of gene expression in developing tissues is not well understood. Here, we describe a statistical thermodynamic model of gene regulation that combines the activity of a morphogen with the transcriptional network it controls. Using Sonic hedgehog (Shh) patterning of the ventral neural tube as an example, we show that the framework can be used together with the principled parameter selection technique of approximate Bayesian computation to obtain a dynamical model that accurately predicts tissue patterning. The analysis indicates that, for each target gene regulated by Gli, which is the transcriptional effector of Shh signalling, there is a neutral point in the gradient, either side of which altering the Gli binding affinity has opposite effects on gene expression. This explains recent counterintuitive experimental observations. The approach is broadly applicable and provides a unifying framework to explain the temporospatial pattern of morphogen-regulated gene expression.

Keywords: Approximate Bayesian computation; Enhancer; Gene regulation; Gli; Mathematical modelling; Morphogen patterning; Shh; Transcriptional networks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Bayes Theorem
  • Body Patterning
  • Drosophila Proteins / metabolism*
  • Drosophila melanogaster / embryology
  • Gene Expression Profiling
  • Gene Expression Regulation, Developmental*
  • Gene Regulatory Networks
  • Hedgehog Proteins / metabolism*
  • Models, Theoretical
  • Software
  • Thermodynamics


  • Drosophila Proteins
  • Hedgehog Proteins
  • Shh protein, Drosophila