Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Cohen M

PROVIDER: S-EPMC4197706 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Cohen Michael M Page Karen M KM Perez-Carrasco Ruben R Barnes Chris P CP Briscoe James J

Development (Cambridge, England) 20141001 20

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 dynamic ...[more]

PMID: 25294939

Similar Datasets

Project description:ObjectivePropose a theoretical framework for retinal biomarkers of Alzheimer's disease (AD).BackgroundThe retina and brain share important biological features that are relevant to AD. Developing retinal biomarkers of AD is a strategic priority but as yet none have been validated for clinical use. Part of the reason may be that fundamental inferential assumptions have been overlooked. Failing to recognize these assumptions will disadvantage biomarker discovery and validation, but incorporating them into analyses could facilitate translation.New theoryThe biological assumption that a disease causes analogous effects in the brain and retina can be expressed within a Bayesian network. This allows inferences about abstract theory and individual events, and provides an opportunity to falsify the foundational hypothesis of retina-brain analogy. Graphical representation of the relationships between variables simplifies comparison between studies and facilitates judgements about whether key assumptions are valid given the current state of knowledge.Major challengesThe framework provides a visual approach to retinal biomarkers and may help to rationalize analysis of future studies. It suggests possible reasons for inconsistent results in existing literature on AD biomarkers.Linkage to other theoriesThe framework can be modified to describe alternative theories of retinal biomarker biology, such as retrograde degeneration resulting from brain disease, and can incorporate confounding factors such as co-existent glaucoma or macular degeneration. Parallels with analogue confirmation theory and surrogate marker validation suggest strengths and weaknesses of the framework that can be anticipated when developing analysis plans.HighlightsRetinal biomarkers hold great promise for Alzheimer's disease (AD), but none are currently used clinically.Assumptions about the cause of retinal and brain changes are often overlooked, and this may disadvantage biomarker discovery and validation.We present a new approach to retinal biomarkers that describes cause and effect graphically in a Bayesian network.We show how this allows a more complete assessment of how well a biomarker might reflect the brain, and how data from right and left eyes can be used to rule out poor biomarker candidates.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets