Reverse engineering of metabolic networks, a critical assessment.
Inferring metabolic networks from metabolite concentration data is a central topic in systems biology. Mathematical techniques to extract information about the network from data have been proposed in the literature. This paper presents a critical assessment of the feasibility of reverse engineering of metabolic networks, illustrated with a selection of methods. Appropriate data are simulated to study the performance of four representative methods. An overview of sampling and measurement methods currently in use for generating time-resolved metabolomics data is given and contrasted with the needs of the discussed reverse engineering methods. The results of this assessment show that if full inference of a real-world metabolic network is the goal there is a large discrepancy between the requirements of reverse engineering of metabolic networks and contemporary measurement practice. Recommendations for improved time-resolved experimental designs are given.
Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands. D.M.Hendrickx@uva.nl
Diana M
DM
Hendrickx
Margriet M W B
MM
Hendriks
Paul H C
PH
Eilers
Age K
AK
Smilde
Huub C J
HC
Hoefsloot
D000818:Animals; D004926:Escherichia coli / Q000378:metabolism; D005240:Feasibility Studies; D006801:Humans; D055432:Metabolomics; D015003:Yeasts / Q000378:metabolism