Stimulated by the long term goal of identifying gene targets for corn crop improvement, we perturbed a simple plant cell system by the addition of abscisic acid, a well-characterized plant hormone, and measured the gene expression with transcriptional microarrays for 150 minutes, every 10 minutes after treatment. Parameters of biochemistry-based models were inferred for 25 downstream (regulated) genes of interest using a thoroughly studied approximation of the posterior distribution, accounting for model uncertainty through Bayesian model averaging. The four causal gene interactions thereby identified as probable under a first-order difference model present testable hypotheses for future experimentation that could conceivably build knowledge of the complex biological system. The small scale of this result compared to many of the reconstructed networks reported in recent literature reflects a cautious approach to systems biology, but without a priori skepticism. The number of putative genes represented on the microarrays used is on the order of the size of the genome; a modification of the methodology for cases in which the number of measured genes is a small fraction of the genome size is also provided. For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a large enough portion of the genome enables computation of an informative upper bound of how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made. Results: The representation of a sufficiently large portion of genome enables computation of an upper bound on how much confidence one may place in influences between genes on the basis of expression data. Information about which genes encode transcription factors is not necessary but may be incorporated if available. The methodology is generalized to cover cases in which expression measurements are missing for many of the genes that might control the transcription of the genes of interest. The assumption that the gene expression level is roughly proportional to the rate of translation led to better empirical performance than did either the assumption that the gene expression level is roughly proportional to the protein level or the Bayesian model average of both assumptions. Availability: URL points to R code implementing the methods (R Development Core Team 2004). Supplementary information: URL
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Stimulated by the long term goal of identifying gene targets for corn crop improvement, we perturbed a simple plant cell system by the addition of abscisic acid, a well-characterized plant hormone, and measured the gene expression with transcriptional microarrays for 150 minutes, every 10 minutes after treatment. Parameters of biochemistry-based models were inferred for 25 downstream (regulated) genes of interest using a thoroughly studied approximation of the posterior distribution, accounting for model uncertainty through Bayesian model averaging. The four causal gene interactions thereby identified as probable under a first-order difference model present testable hypotheses for future experimentation that could conceivably build knowledge of the complex biological system. The small scale of this result compared to many of the reconstructed networks reported in recent literature reflects a cautious approach to systems biology, but without a priori skepticism. The number of putative genes represented on the microarrays used is on the order of the size of the genome; a modification of the methodology for cases in which the number of measured genes is a small fraction of the genome size is also provided. For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the
Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made. Results: The
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large enough portion of the
sufficiently large portion of
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informative upper bound of
upper bound on
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reasonably place in causal relationships
place in influences
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observed data.
expression data. Information about which genes encode transcription factors is not necessary but may be incorporated if available. The methodology is generalized to cover cases in which expression measurements are missing for many of the genes that might control the transcription of the genes of interest. The assumption that the gene expression level is roughly proportional to the rate of translation led to better empirical performance than did either the assumption that the gene expression level is roughly proportional to the protein level or the Bayesian model average of both assumptions. Availability: URL points to R code implementing the methods (R Development Core Team 2004). Supplementary information: URL