Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties of metabolism. Here we extend high-resolution-magic angle spinning (HR-MAS) to enable continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) and provide analysis tools for these data. First, we reproduced a result in human chronic lymphoid leukemia cells by using isotope-edited CIVM-NMR to rapidly and unambiguously demonstrate unidirectional flux in branched-chain amino acid metabolism. We then collected untargeted CIVM-NMR datasets for Neurospora crassa , a classic multicellular model organism, and uncovered dynamics between central carbon metabolism, amino acid metabolism, energy storage molecules, and lipid and cell wall precursors. Virtually no sample preparation was required to yield a dynamic metabolic fingerprint over hours to days at ~4-min temporal resolution with little noise. CIVM-NMR is simple and readily adapted to different types of cells and microorganisms, offering an experimental complement to kinetic models of metabolism for diverse biological systems.

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Using a microfluidics device, fluorescence of a recorder (mCherry or mVenus) gene driven by a clock-controlled gene-2 promoter (ccg-2p) was measured simultaneously on over 1000 single cells of Neurospora crassa every half hour for 10 days under each of varied light and temperature conditions. Single cells were able to entrain to light over a wide range of day lengths, including 6, 12, or 36 h days. In addition, the period of oscillations in fluorescence remained remarkably stable over a physiological range of temperatures from 20 o C to 30 o C (Q 10 = 1.00-1.07). These results provide evidence of an autonomous clock in most single cells of N. crassa. While most cells had clocks, there was substantial variation between clocks as measured by their phase, raising the question of how such cellular clocks in single cells phase-synchronize to achieve circadian behavior in eukaryotic systems at the macroscopic level of 10 7 cells, where most measurements on the clock are performed. Single cells were placed out of phase by allowing one population to receive 6 or 12 h more light before lights out (D/D). The average phase difference was reduced in the mixed population relative to two unmixed control populations.

Most genetic networks, such as that for the biological clock, are part of much larger modules controlling fundamental processes in the cell, such as metabolism, development, and response to environmental signals. For example, the biological clock is part of a much larger network controlling the circadian rhythms of about 2418 distinct genes in the genome (with 11 000 genes) of the model system, Neurospora crassa. Predicting and understanding the dynamics of all of these genes and their products in a genetic network describing how the clock functions is a challenge and beyond the current capability of the fastest serial computers. We have implemented a novel variable-topology supernet ensemble method using Markov chain Monte Carlo simulations to fit and discover a regulatory network of unknown topology composed of 2418 genes describing the entire clock circadian network, a network that is found in organisms ranging from bacteria to humans, by harnessing the power of the general-purpose graphics processing unit and exploiting the hierarchical structure of that genetic network. The result is the construction of a genetic network that explains mechanistically how the biological clock functions in the filamentous fungus N. crassa and is validated against over 31 000 data points from microarray experiments. Two transcription factors are identified targeting ribosome biogenesis in the clock network.INDEX TERMS Biological clock, general-purpose graphical processing unit, ensemble method, supernet, systems biology, and regulatory network topologies. VOLUME 3, 2015 2169-3536 2015 IEEE. Translations and content mining are permitted for academic research only.Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 28 VOLUME 3, 2015 A. Al-Omari et al.: Discovering Regulatory Network Topologies Using Ensemble Methods on GPGPUsFIGURE 3.The simplest model is one in which all 2,418 genes are regulated by one transcription factor, WCC. Molecular species (i.e., reactants or products) in the network are represented by boxes. The white-collar-1 (wc-1), white-collar-2 (wc-2), frequency (frq), and clock controlled gene (ccg) gene symbols are sometimes superscripted 0, 1, r 0 , r 1 , indicating, respectively, a transcriptionally inactive (0) or active (1) gene or a translationally inactive (r 0 ) or active (r 1 ) mRNA. The notational convention for protein species is all capitals. A phot (box in yellow) symbolizes the photon species. Reactions in the network are represented by circles. Arrows pointing to circles identify reactants; arrows leaving circles identify products; and bi-directional arrows identify catalysts. The labels on each reaction, such as S 4 , also double as the rate coefficients for each reaction. Reactions with an A or B label are either activation or deactivation reactions. Reactions labeled with an S, L, or D represent transcription, translation, or degradation reactions, respectivel...

Motivation Time-series NMR has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra. Results We introduce RTExtract (Ridge Tracking based Extract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than two hours instead of ∼48 hours, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked. Availability Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R. Supplementary information Supplementary data are available at Bioinformatics online.