Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.
We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.
Nemenman
Ilya
I
CCS-3, MS-B256, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. nemenman@lanl.gov
Escola
G Sean
GS
Hlavacek
William S
WS
Unkefer
Pat J
PJ
Unkefer
Clifford J
CJ
Wall
Michael E
ME
D000465:Algorithms; D000818:Animals; D001698:Biomedical Engineering / Q000379:methods; D001798:Blood Proteins / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004912:Erythrocytes / Q000378:metabolism*; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology; D006801:Humans; D008955:Models, Cardiovascular; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software; D012986:Software Validation
D001798:Blood Proteins; D020543:Proteome