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A Gene Network Model for Developing Cell Lineages

Overview of attention for article published in Artificial Life, June 2005
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2 Wikipedia pages

Citations

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32 Dimensions

Readers on

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67 Mendeley
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7 CiteULike
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Title
A Gene Network Model for Developing Cell Lineages
Published in
Artificial Life, June 2005
DOI 10.1162/1064546054407202
Pubmed ID
Authors

Nicholas Geard, Janet Wiles

Abstract

Biological development is a remarkably complex process. A single cell, in an appropriate environment, contains sufficient information to generate a variety of differentiated cell types, whose spatial and temporal dynamics interact to form detailed morphological patterns. While several different physical and chemical processes play an important role in the development of an organism, the locus of control is the cell's gene regulatory network. We designed a dynamic recurrent gene network (DRGN) model and evaluated its ability to control the developmental trajectories of cells during embryogenesis. Three tasks were developed to evaluate the model, inspired by cell lineage specification in C. elegans, describing the variation in gene activity required for early cell diversification, combinatorial control of cell lineages, and cell lineage termination. Three corresponding sets of simulations compared performance on the tasks for different gene network sizes, demonstrating the ability of DRGNs to perform the tasks with minimal external input. The model and task definition represent a new means of linking the fundamental properties of genetic networks with the topology of the cell lineages whose development they control.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Germany 2 3%
Australia 2 3%
United Kingdom 2 3%
Hong Kong 1 1%
South Africa 1 1%
Austria 1 1%
Mexico 1 1%
Hungary 1 1%
Other 0 0%
Unknown 53 79%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 28%
Researcher 15 22%
Student > Master 10 15%
Professor > Associate Professor 6 9%
Other 5 7%
Other 11 16%
Unknown 1 1%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 39%
Computer Science 18 27%
Engineering 9 13%
Biochemistry, Genetics and Molecular Biology 3 4%
Philosophy 2 3%
Other 8 12%
Unknown 1 1%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 May 2023.
All research outputs
#7,452,489
of 22,783,848 outputs
Outputs from Artificial Life
#117
of 340 outputs
Outputs of similar age
#20,300
of 57,223 outputs
Outputs of similar age from Artificial Life
#1
of 2 outputs
Altmetric has tracked 22,783,848 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 340 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 57,223 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them