2008
DOI: 10.1371/journal.pone.0002179
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How to Build Transcriptional Network Models of Mammalian Pattern Formation

Abstract: BackgroundGenetic regulatory networks of sequence specific transcription factors underlie pattern formation in multicellular organisms. Deciphering and representing the mammalian networks is a central problem in development, neurobiology, and regenerative medicine. Transcriptional networks specify intermingled embryonic cell populations during pattern formation in the vertebrate neural tube. Each embryonic population gives rise to a distinct type of adult neuron. The homeodomain transcription factor Lbx1 is ex… Show more

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Cited by 11 publications
(18 citation statements)
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“…Total RNA was prepared from forelimbs and probes prepared from these RNA were applied to nine Mouse Genome 430 2.0 microarrays [63]. The results from all nine arrays were normalized by RMA.…”
Section: Methodsmentioning
confidence: 99%
“…Total RNA was prepared from forelimbs and probes prepared from these RNA were applied to nine Mouse Genome 430 2.0 microarrays [63]. The results from all nine arrays were normalized by RMA.…”
Section: Methodsmentioning
confidence: 99%
“…Numbers based on 27 drugs selected in the primary phenotypic screen. Six drugs (3,8,10,12,18,24) were eliminated from further analysis by a secondary phenotypic screen. induced pluripotent stem cells.…”
Section: Figurementioning
confidence: 99%
“…Scatter plot comparisons of individual arrays showed fewer changes in internal comparisons, between replicate arrays from the same genotype, than in cross comparisons, between individual arrays from different genotypes (data not shown). The number of regulated genes can be estimated by permutation fold-scanning analysis (28,29). This non-parametric method counts probe set comparisons that fall above each -fold cutoff between conditions (cross comparisons) and within conditions (internal comparisons) and uses them to calculate the number of regulated genes as a function of -fold change or false discovery rate (FDR) (29,30).…”
Section: Identification Of T-box Genes Regulated By Pitx2 In Abdomi-mentioning
confidence: 99%
“…The number of regulated genes can be estimated by permutation fold-scanning analysis (28,29). This non-parametric method counts probe set comparisons that fall above each -fold cutoff between conditions (cross comparisons) and within conditions (internal comparisons) and uses them to calculate the number of regulated genes as a function of -fold change or false discovery rate (FDR) (29,30). An FDR of 8% was calculated for WT versus MUT comparisons at a 1.7-fold cutoff.…”
Section: Identification Of T-box Genes Regulated By Pitx2 In Abdomi-mentioning
confidence: 99%
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