Learning gene regulatory networks in Arabidopsis Thaliana

author: Chris Needham, University of Leeds
published: Sept. 7, 2007,   recorded: September 2007,   views: 4805


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Gene regulatory networks govern the functional development and biological processes of cells in all organisms. Genes regulate each other as part of a complex system, of which it is vitally important to gain an understanding. For example, discovery of the complete gene regulatory networks in humans would allow the identification of genes which cause disease, and could be used for drug discovery to identify genes interacting with compounds of interest. Similarly in plants knowledge of the gene regulatory networks would allow the development of stress (drought/salt/temperature) resistant crops. Learning large gene regulatory networks with thousands of genes with any certainty from microarray data is extremely challenging. This research aims to build around known networks from the literature on gene regulation, and assesses which other genes are likely to play a regulatory role or be in the same regulatory pathways. The gene regulatory networks are modelled with a Bayesian network. The gene expThe use of large scale public microarray data appears to be a very useful starting point for informing future experiments in order to determine gene regulatory networks.ression levels are quantised and a greedy hill climbing search method is used within a network structure learning algorithm. The inclusion of extra genes with the best explanatory power into the model has been demonstrated to be robust. Large sets of microarray experiments are used in this analysis, specifically 2466 NASC Arabidopsis thaliana microarrays containing gene expression levels of over twenty thousand genes in a number of experimental conditions. Initial investigation of this data is very promising. We have learned gene transcription sub-networks (see Figure 1) regulated by the plant’s circadian clock. The network shown was generated from microarray data without the use of any prior information, and yet the method managed to identify the strong causal relationships between clock components (TOC1, LHY, ELF3, ELF4, CCA1) and to link these to further key regulators of important processes (e.g. ZAT, myb and GATA transcription factors).

See Also:

Download slides icon Download slides: pmnp07_needham_lgrn_01.ppt (1.4 MB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 kullumanali, August 16, 2019 at 8:53 a.m.:

Book your Manali Trip with Kullu Manali Travel Time. We are a leading travel agency in Shimla. We are specialized to offer Tour packages for Kullu Manali. Kullu Manali Travel Time offer you affordable packages for your holiday trip. Here you get multiple packages such as: kullu manali tour packages, shimla manali packages and much more.

Write your own review or comment:

make sure you have javascript enabled or clear this field: