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==2012==
===A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegant development===
BMC Syst Biol. 2012 Jun 26;6(1):77. [Epub ahead of print]
Stigler B, Chamberlin HM.
Abstract
ABSTRACT:
BACKGROUND:
Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments.
RESULTS:
To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9.
CONCLUSIONS:
This work provides a strong example for a mathematical modeling approach that solely uses wild-type data to predict an underlying gene regulatory network. The modeling approach complements traditional methods of data analysis, suggesting non-intuitive network relationships and guiding future experiments.
PMID 22734688


==2010==
==2010==

Revision as of 11:25, 30 June 2012

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Cite this page: Hill, M.A. (2024, March 29) Embryology Worm Development. Retrieved from https://embryology.med.unsw.edu.au/embryology/index.php/Talk:Worm_Development

2012

A regulatory network modeled from wild-type gene expression data guides functional predictions in Caenorhabditis elegant development

BMC Syst Biol. 2012 Jun 26;6(1):77. [Epub ahead of print]

Stigler B, Chamberlin HM. Abstract

ABSTRACT: BACKGROUND: Complex gene regulatory networks underlie many cellular and developmental processes. While a variety of experimental approaches can be used to discover how genes interact, few biological systems have been systematically evaluated to the extent required for an experimental definition of the underlying network. Therefore, the development of computational methods that can use limited experimental data to define and model a gene regulatory network would provide a useful tool to evaluate many important but incompletely understood biological processes. Such methods can assist in extracting all relevant information from data that are available, identify unexpected regulatory relationships and prioritize future experiments. RESULTS: To facilitate the analysis of gene regulatory networks, we have developed a computational modeling pipeline method that complements traditional evaluation of experimental data. For a proof-of-concept example, we have focused on the gene regulatory network in the nematode C. elegans that mediates the developmental choice between mesodermal (muscle) and ectodermal (skin) cell fates in the embryonic C lineage. We have used gene expression data to build two models: a knowledge-driven model based on gene expression changes following gene perturbation experiments, and a data-driven mathematical model derived from time-course gene expression data recovered from wild-type animals. We show that both models can identify a rich set of network gene interactions. Importantly, the mathematical model built only from wild-type data can predict interactions demonstrated by the perturbation experiments better than chance, and better than an existing knowledge-driven model built from the same data set. The mathematical model also provides new biological insight, including a dissection of zygotic from maternal functions of a key transcriptional regulator, PAL-1, and identification of non-redundant activities of the T-box genes tbx-8 and tbx-9. CONCLUSIONS: This work provides a strong example for a mathematical modeling approach that solely uses wild-type data to predict an underlying gene regulatory network. The modeling approach complements traditional methods of data analysis, suggesting non-intuitive network relationships and guiding future experiments.

PMID 22734688

2010

Neurogenesis in the nematode Caenorhabditis elegans

Hobert O. WormBook. 2010 Oct 4:1-24.


Abstract The nervous system represents the most complex tissue of C. elegans both in terms of numbers (302 neurons and 56 glial cells = 37% of the somatic cells in a hermaphrodite) and diversity (118 morphologically distinct neuron classes). The lineage and morphology of each neuron type has been described in detail and neuronal fate markers exists for virtually all neurons in the form of fluorescent reporter genes. The ability to "phenotype" neurons at high resolution combined with the amenability of C. elegans to genetic mutant analysis make the C. elegans nervous system a prime model system to elucidate the nature of the gene regulatory programs that build a nervous system-a central question of developmental neurobiology. Discussing a number of regulatory genes involved in neuronal lineage determination and neuronal differentiation, I will try to carve out in this review a few general principles of neuronal development in C. elegans. These principles may be conserved across phylogeny.

PMID 20891032

http://www.ncbi.nlm.nih.gov/pubmed/20891032

http://www.wormbook.org/chapters/www_specnervsys.2/neurogenesis.html

Spermatogenesis-Specific Features of the Meiotic Program in Caenorhabditis elegans

http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000611


Chromosome axis defects induce a checkpoint-mediated delay and interchromosomal effect on crossing over during Drosophila meiosis

Joyce EF, McKim KS. PLoS Genet. 2010 Aug 12;6(8). pii: e1001059.

PMID 20711363

http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001059

2009

xol-1, the master sex-switch gene in C. elegans, is a transcriptional target of the terminal sex-determining factor TRA-1

Hargitai B, Kutnyánszky V, Blauwkamp TA, Steták A, Csankovszki G, Takács-Vellai K, Vellai T. Development. 2009 Dec;136(23):3881-7. PMID: 19906855

"In the nematode Caenorhabditis elegans, sex is determined by the ratio of X chromosomes to sets of autosomes: XX animals (2X:2A=1.0) develop as hermaphrodites and XO animals (1X:2A=0.5) develop as males. ....Here we identify a consensus TRA-1 binding site in the regulatory region of xol-1, the master switch gene controlling sex determination and dosage compensation. xol-1 is normally expressed in males, where it promotes male development and prevents dosage compensation."
  • Somatic sexual differentiation in Caenorhabditis elegans. Wolff JR, Zarkower D. Curr Top Dev Biol. 2008;83:1-39. PMID: 19118662
  • The primary sex determination signal of Caenorhabditis elegans. Carmi I, Meyer BJ. Genetics. 1999 Jul;152(3):999-1015. PMID: 10388819

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