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Evaluation of Design Strategies for Time Course Experiments in Genetic Networks: Case Study of the XlnR Regulon in Aspergillus niger

Content Provider	ACM Digital Library
Author	Mach-aigner, Astrid R. De graaff, Leo H. Van straten, Gerrit Van boxtel, Anton J. B. Omony, Jimmy
Abstract	One of the challenges in genetic network reconstruction is finding experimental designs that maximize the information content in a data set. In this paper, the information value of mRNA transcription time course experiments was used to compare experimental designs. The study concerns the dynamic response of genes in the XlnR regulon of Aspergillus niger, with the goal to find the best moment in time to administer an extra pulse of inducing D-xylose. Low and high D-xylose pulses were used to perturb the XlnR regulon. Evaluation of the experimental methods was based on simulation of the regulon. Models that govern the regulation of the target genes in this regulon were used for the simulations. Parameter sensitivity analysis, the Fisher Information Matrix (FIM) and the modified E-criterion were used to assess the design performances. The results show that the best time to give a second D-xylose pulse is when the D-xylose concentration from the first pulse has not yet completely faded away. Due to the presence of a repression effect the strength of the second pulse must be optimized, rather than maximized. The results suggest that the modified E-criterion is a better metric than the sum of integrals of absolute sensitivity for comparing alternative designs.
Starting Page	1316
Ending Page	1325
Page Count	10
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2012.59
Volume Number	9
Issue Number	5
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-09-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sensitivity,Covariance matrix,Data models,Gene expression,Proteins,Bioinformatics,Aspergillus niger.,Experimental design strategies,genetic network,trigger experiments,time course data,parameter estimation,XlnR regulon
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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