MOTIVATION: Quantitative determination of metabolic fluxes in single tissue biopsies is difficult. We report a novel analysis approach and software package for in vivo flux quantification using stable isotope labeling. RESULTS: We developed a protocol based on brief, timed infusion of (13)C isotope-enriched substrates for the tricarboxylic acid (TCA) cycle followed by quick freezing of tissue biopsies. NMR measurements of tissue extracts were used for flux estimation based on a computational model of carbon transitions between TCA cycle metabolites and related amino acids. To this end, we developed a computational framework in which metabolic systems can be flexibly assembled, simulated and analyzed. Flux parameters were quantified from NMR multiplets by a partial grid search followed by repeated Nelder-Mead optimizations implemented on a computer grid. We implemented a model of the TCA cycle and showed by extensive simulations that the timed infusion protocol reliably quantitates multiple fluxes. Experimental validation of the method was done in vivo on hearts of anesthetized pigs under two different conditions: basal state (n = 7) and cardiac stress caused by infusion of dobutamine (n = 7). About nine tissue samples (40-200 mg dry-weight) were taken per heart. TCA cycle flux was 6.11 +/- 0.28 (SEM) micromol/min x gdw at baseline versus 9.29 +/- 1.03 micromol/min x gdw for dobutamine stress. Oxygen consumption calculated from the TCA cycle flux and from 'gold standard' blood gas-based measurements were close, correlating with r=0.88 (P < 10(-4)). Spatial heterogeneity in metabolic fluxes is detectable amongst the small samples. We propose that our novel isotope snapshot methodology is suitable for flux measurements in biopsies in vivo. AVAILABILITY: Non-profit organizations will, upon request, be granted a non-exclusive license to use the software for internal research and teaching purposes at no charge. A web interface for using the software on our computer grid is available under http://www.ibi.vu.nl/programs/

SUMMARY: CGHnormaliter is a package for normalization of array comparative genomic hybridization (aCGH) data. It uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). CGHnormaliter is integrated in the Bioconductor environment allowing a smooth link to visualization tools and further data analysis. AVAILABILITY AND IMPLEMENTATION: The CGHnormaliter package is implemented in R and under GPL 3.0 license available at Bioconductor: http://www.bioconductor.org CONTACT: [email protected]