Journal
DEVELOPMENT
Volume 141, Issue 20, Pages 3868-3878Publisher
COMPANY BIOLOGISTS LTD
DOI: 10.1242/dev.112573
Keywords
Approximate Bayesian computation; Enhancer; Gene regulation; Gli; Morphogen patterning; Shh; Mathematical modelling; Transcriptional networks
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Funding
- Medical Research Council [U117560541]
- Wellcome Trust [WT098325MA, WT098326MA]
- Wellcome Trust through a Research Career Development Fellowship [097319/Z/11/Z]
- Wellcome Trust [098325/Z/12/Z] Funding Source: Wellcome Trust
- Wellcome Trust [098325/Z/12/Z] Funding Source: researchfish
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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.
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