This project uses quantitative measurements and mathematical modeling to understand how the nematode Caenorhabditis elegans specifies endodermal cell fate. The core of the genetic network governing this cell fate decision consists of a small group of transcription factors whose objective is to activate the expression of an endodermal master regulatory gene. Mutations to this network can have surprising and indeterminate effects. A mutation in the most upstream gene is partially penetrant, resulting in genetically and environmentally identical embryos differing in whether they develop endoderm or not. Although it is known that this mutation reveals a threshold for gene expression in the network, how this threshold is generated is unclear. Moreover, the network contains several pairs of paralogs that appear to be partially redundant, making the network robust against some mutations. The experiments proposed here will apply and test a mathematical modeling framework for transcriptional regulation in developmental genetic networks. The goal is to estimate key parameters of the model from high resolution gene expression measurements on engineered transgenic strains of C. elegans. With these measurements we will be able to model normal endodermal specification in these worms and predict the effects of different perturbations to the network. By iterating between modeling and experimentation, we will refine our understanding of how cell-type specification is controlled during development. As one of the preeminent species for genetic and developmental studies, C. elegans is a natural choice for a model organism for developmental systems biology. Many of its advantages for tractability, genetics, and development carry over to its close relatives, and so the Caenorhabditis genus is fast becoming a model clade for evolutionary systems biology as well.

Public Health Relevance

Making correct cell fate decisions is a crucial part of embryonic development and can have calamitous consequences if it goes wrong. This project uses mathematical modeling and quantitative experimentation to study the gene interactions that ensure that these decisions are made reliably and to understand the developmental outcomes when they fail.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-CB-J (59))
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Hoodbhoy, Tanya
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University of California San Diego
Schools of Arts and Sciences
La Jolla
United States
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Du, Lawrence; Tracy, Sharon; Rifkin, Scott A (2016) Mutagenesis of GATA motifs controlling the endoderm regulator elt-2 reveals distinct dominant and secondary cis-regulatory elements. Dev Biol 412:160-70
Maduro, Morris F; Broitman-Maduro, Gina; Choi, Hailey et al. (2015) MED GATA factors promote robust development of the C. elegans endoderm. Dev Biol 404:66-79
Wu, Allison Chia-Yi; Rifkin, Scott A (2015) Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images. BMC Bioinformatics 16:102