Team:Nevada/Modeling

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=== Acknowledgments ===
=== Acknowledgments ===

Revision as of 19:52, 27 October 2010

UNR Modeling.png

 

Contents

Modeling

Pending.png


Introduction

Plant stress responses are often cascades involving hundreds of genes and gene products. The possible interactions in these cascades are astronomical. Therefore, the 2010 Nevada iGEM team worked with Bioinformatics Professor, Karen Schlauch, to use a computational method that could quickly analyze possible transcriptional regulation pathways, using either microarray data or data from continuous fluorometry experiments. In conjunction with the use of tobacco BY-2 (NT1) cells, this method could allow for even greater time efficiency in identifying important aspects of gene networks in plants. The method was intended to allow for easier identification of promoters useful to the team’s objective of creating remote plant biosensors. The method uses a Boolean network approach to examine the gene network and its possible regulatory system. We first viewed transcripts as “on” when above a threshold value and “off” for lesser values. All Boolean networks that could generate our dataset were generated and evaluated. In this manner, we were able to look at all possible interactions between genes based on the Boolean approach.


The DREB1 Pathway




Boolean Networks




Data

When it became clear that the team would not have sufficient time to perform fluorometry experiments and analyze data by November, it was decided that microarray experiments published to internet databases would have to do. All data was originally obtained from a 24-hour time course microarray experiment performed by Jian-Kang Zhu, et al, and published on the Gene Expression Omnibus database (Zhu, et al. 2005). This allowed for a proof-of-concept to see if the Boolean network would support what was known about the DREB1 pathway from published literature.

Click here to see the initial data set

Because this data consisted of only four time points, all eight genes had similar Boolean values at each time point. Therefore, the Boolean functions for each were essentially the same and numbered in the billions. This provided little data for interpretation. Several methods were used to tease out differences in expression, so that the time courses would be sufficiently different. First, the threshold value for "on" was raised to 2^3.5 rather than 4. Second, The data was interpolated to estimate extra time points within the 24-hour time course. Finally, the number of inputs for each gene was limited to four, as it was deemed unlikely that any gene in this network was receiving input from 5 or more.

Click here to see the interpolated data set and associated Boolean functions




Results


Acknowledgments

The 2010 Nevada iGEM team would like to thank Karen Schlauch for all of her hard work, performing computational analysis, explaining the concepts of Boolean networking, and working with the team to find biological meaning in the Boolean output functions.


References

Zhu, J-K., Lee, B., Henderson, D. (2005) The Arabidopsis Cold-Responsive Transcriptome and Its Regulation by ICE1. Plant Cell. Vol. 17, Issue 11, p3155-3175.


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