Schröder, Adrian and Wrzodek, Clemens and Wollnik, Johannes and Dräger, Andreas and Wanke, Dierk and Berendzen, Kenneth W. and Zell, Andreas

Inferring transcriptional regulators for sets of co-expressed genes by multi-objective evolutionary optimization

IEEE Congress on Evolutionary Computation (CEC 2011), New Orleans, USA, IEEE, 2011


Abstract

Higher organisms are able to respond to continuously changing external conditions by transducing cellular signals into specific regulatory programs, which control gene expression states of thousands of different genes. One of the central problems in understanding gene regulation is to decipher how combinations of transcription factors control sets of coexpressed genes under specific experimental conditions. Existing methods in this field mainly focus on sequence aspects and pattern recognition, e.g., by detecting cis-regulatory modules (CRMs) based on gene expression profiling data. We propose a novel approach by combining experimental data with a priori knowledge of respective experimental conditions. These various sources of evidence are likewise considered using multi-objective evolutionary optimization. In this work, we present three objective functions that are especially designed for stimulus-response experiments and can be used to integrate a priori knowledge into the detection of gene regulatory modules. This method was tested and evaluated on whole-genome microarray measurements of drug-response in human hepatocytes.


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BibTeX

@inproceedings{Schroeder2011a,
  author = {Schr\"oder, Adrian and Wrzodek, Clemens and Wollnik, Johannes and
	Dr\"ager, Andreas and Wanke, Dierk and Berendzen, Kenneth W. and
	Zell, Andreas},
  title = {Inferring transcriptional regulators for sets of co-expressed genes
	by multi-objective evolutionary optimization},
  booktitle = {IEEE Congress on Evolutionary Computation (CEC 2011)},
  year = {2011},
  address = {New Orleans, USA},
  month = jun,
  publisher = {IEEE},
  abstract = {Higher organisms are able to respond to continuously changing external
	conditions by transducing cellular signals into specific regulatory
	programs, which control gene expression states of thousands of different
	genes. One of the central problems in understanding gene regulation
	is to decipher how combinations of transcription factors control
	sets of coexpressed genes under specific experimental conditions.
	Existing methods in this field mainly focus on sequence aspects and
	pattern recognition, e.g., by detecting \emph{cis}-regulatory modules (CRMs)
	based on gene expression profiling data. We propose a novel approach
	by combining experimental data with a priori knowledge of respective
	experimental conditions. These various sources of evidence are likewise
	considered using multi-objective evolutionary optimization. In this
	work, we present three objective functions that are especially designed
	for stimulus-response experiments and can be used to integrate a
	priori knowledge into the detection of gene regulatory modules. This
	method was tested and evaluated on whole-genome microarray measurements
	of drug-response in human hepatocytes.},
  doi = {10.1109/CEC.2011.5949899},
  isbn = {978-1-4244-7833-0},
  pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2011/Schroeder2011cec.pdf},
  url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5949899}
}