A Genomic Regulatory Network for Development
By Eric H. Davidson, J. Rast, P. Oliveri, A. Ransick, C. Calestani, C. Yuh, T. Minokawa, G. Amore, V. Hinman, C. Arenas-Mena and 15 more. 2002
1,610 citations recorded by the source corpus.
Subfields: Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- DaiZhan Cheng
- Hongsheng Qi
- Réka Albert
- Trey Ideker
- Eduardo Sontag
- Madalena Chaves
- Jormay Lim
- Zhiqiang Li
Citations in the corpus
- Robustness and fragility of Boolean models for genetic regulatory networks. (2005, methodology): “We propose a new modeling framework for gene regulatory networks, that combines the intuitive appeal ofa qualitative description of gene states with a high flexibility in incorporating stochasticity in the duration of cellular processes.”
- Inferring cellular networks – a review (2007, background): “[88] test 10 algorithms, including Boolean and Bayesian networks, on a simulation [89] of the genetic network of the sea urchin embryo [90].”
- Highly Conserved Non-Coding Sequences Are Associated with Vertebrate Development (2004, neutral): “The idea that animal development is controlled by cis-regulatory DNA elements (such as enhancers and silencers) is well established and has been elegantly described in invertebrates such as Drosophila and the sea urchin [ 1 ,2,3,4].”
- GATA-3 maintains the differentiation of the luminal cell fate in the mammary gland. (2006, positive): “Efforts to understand luminal cell differentiation have focused on the estrogen receptor (ERa), which is found in asubsetofluminalcellsthatexhibitlowproliferation( Cheng etal.,2004 ).ERaisfoundinonlyhalfofmatureluminalcells and is found in fibroblasts and other stromal cells.”
- The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. (2003, methodology): “To investigate this, we propose and analyze a Boolean model of this network which is based on a binary ON/OFF representation of transcription and protein levels, and in which the interactions are formulated as logical functions.”