Pub Date : 2007-11-05DOI: 10.1109/BIBE.2007.4375678
Michael L Blinov, Ion I Moraru
Multi-state molecules and multi-component complexes are commonly involved in cellular signaling. Accounting for molecules that have multiple potential states, such as a protein that may be phosphorylated on multiple residues, and molecules that combine to form heterogeneous complexes located among multiple compartments, generates an effect of combinatorial complexity. Models involving relatively few signaling molecules can include thousands of distinct chemical species. Several software tools (StochSim, BioNetGen) are already available to deal with combinatorial complexity. Such tools need information standards if models are to be shared, jointly evaluated and developed. Here we discuss XML conventions that can be adopted for modeling biochemical reaction networks described by user-specified reaction rules. These could form a basis for possible future extensions of the Systems Biology Markup Language (SBML).
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Pub Date : 2007-10-01DOI: 10.1109/BIBE.2007.4375598
Jung Hoon Woo, Tian Zheng, Ju Han Kim
The genetical genomics approach has been used to study the genetic basis of variation in gene expression, where putative transcriptional regulators of genes are identified via genetic quantitative trait mapping. The genetic regulators identified through such efforts can partially account for an individual gene's natural variation. However, genes in a molecular pathway often exhibit coordinated activities, the patterns and levels of which are also regulated. In an effort to understand these complicated mechanisms, we propose a method that searches for the genomic regulators of set-wise co-expression of related genes, based on current genetical genomics data. Using this method, we studied genomic regulators of 233 biological pathways for a BXD RI data set. For 15 pathways, we obtained significant regulatory loci after controlling for the false discovery rate. The results presented in this paper constitute important evidence of the heritability of mRNA co-expression between individuals. We have shown that, by defining new phenotypes using existing genetical genomics data, evidence on regulation of co-expression can be derived.
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