Ab initioprediction of metabolic networks using Fourier transform mass spectrometry data.
Fourier transform mass spectrometry has recently been introduced into the field of metabolomics as a technique that enables the mass separation of complex mixtures at very high resolution and with ultra high mass accuracy. Here we show that this enhanced mass accuracy can be exploited to predict large metabolic networksab initio, based only on the observed metabolites without recourse to predictions based on the literature. The resulting networks are highly information-rich and clearly non-random. They can be used to infer the chemical identity of metabolites and to obtain a global picture of the structure of cellular metabolic networks. This represents the first reconstruction of metabolic networks based on unbiased metabolomic data and offers a breakthrough in the systems-wide analysis of cellular metabolism.
Groningen Bioinformatics Centre, University of Groningen, 9751 NN Haren, The Netherlands ; Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow, G12 8QQ UK.
Rainer
R
Breitling
Phenomenome Discoveries, Saskatoon, S7N 4L8 Canada.
Shawn
S
Ritchie
Phenomenome Discoveries, Saskatoon, S7N 4L8 Canada.
Dayan
D
Goodenowe
Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow, G12 8QQ UK.
Mhairi L
ML
Stewart
Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow, G12 8QQ UK.
Michael P
MP
Barrett
Fourier transform mass spectrometry; computational methods; metabolic networks; network reconstruction