Inference of responsive metabolic pathways from time-series transcriptomic data with consideration of the metabolic network structure.
The inference of responsive metabolic pathways from transcriptomic data remains a problem which needs to be solved. In this work, we make use of time-series transcriptomic data and the inherent structure of a metabolic network to examine the possibility of metabolic pathway inference. We present a method that calculates the state of each network metabolite for the different time points of a transcriptomic dataset. This forms the basis for metabolic pathway enrichment analysis based on time-series gene expression data. Application of the method to yeast transcriptomic datasets revealed metabolic pathways that showed the highest respective response during nitrogen starvation, amino acid starvation and under the influence of heat. Furthermore, key metabolic pathways related to the yeast cell cycle, like the lipid metabolism in the G1 phase, were identified. Therefore, a method for systematic determination of metabolic pathways that showed the highest change under given conditions was introduced. The proposed method allows for the analysis of transcriptomic data closely related to the metabolism of the cell by using the structure of the metabolic network as a framework for analysis, especially by using time-series transcriptomic data.
Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, 21073 Hamburg, Germany. schoepke@tu-harburg.de
Benedikt
B
Schöpke
An-Ping
AP
Zeng
D000465:Algorithms; D002453:Cell Cycle; D016000:Cluster Analysis; D005656:Fungal Proteins / Q000737:chemistry* / Q000378:metabolism; D005658:Fungi / Q000502:physiology; D020869:Gene Expression Profiling; D006801:Humans; D016014:Linear Models; D050356:Lipid Metabolism; D055432:Metabolomics / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D013997:Time Factors; D014158:Transcription, Genetic
D005656:Fungal Proteins