Metabolic network discovery through reverse engineering of metabolome data |
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Authors: | Tunahan Çak?r Margriet M W B Hendriks Johan A Westerhuis Age K Smilde |
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Institution: | (1) Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands;(2) Department of Metabolic and Endocrine Diseases, University Medical Centre Utrecht, Utrecht, The Netherlands;(3) Netherlands Metabolomics Centre, Einsteinweg 55, 2333 CC Leiden, The Netherlands; |
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Abstract: | Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems
biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of
metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data
types based on biological/environmental variability around steady state were analyzed to compare the relative information
content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated
the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions.
We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of
different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing
complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already
informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score
approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions
may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions
of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong
to metabolites connected with weak interaction strength. |
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