82 research outputs found

    An instrument for measuring size-resolved aerosol hygroscopicity at both sub- and super-micron sizes

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    The article of record as published may be found at http://dx.doi.org/10.1080/02786820701506955A new instrument to measure the size-resolved hygroscopic growth of both sub- and super-micron atmospheric aerosol is described. It consists of two white-light optical particle counters measuring the same sample aerosol simultaneously at two different controlled relative humidities. Calibration with aerosols of different refractive index confirms the expected relative insensitivity of the instrument to index of refraction. Data obtained in the field from airborne sampling support the utility of the instrument in measuring differences in size-resolved hygroscopicity in the marine boundary layer and also in addressing the issue of kinetic limitations to aerosol condensational growth.Office of Naval ResearchGrant no. N0014-97-1-013

    Context-Specific Metabolic Networks Are Consistent with Experiments

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    Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are “genome-scale” and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available

    Detecting controlling nodes of boolean regulatory networks

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    Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied

    Systems Biology of the qa Gene Cluster in Neurospora crassa

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    An ensemble of genetic networks that describe how the model fungal system, Neurospora crassa, utilizes quinic acid (QA) as a sole carbon source has been identified previously. A genetic network for QA metabolism involves the genes, qa-1F and qa-1S, that encode a transcriptional activator and repressor, respectively and structural genes, qa-2, qa-3, qa-4, qa-x, and qa-y. By a series of 4 separate and independent, model-guided, microarray experiments a total of 50 genes are identified as QA-responsive and hypothesized to be under QA-1F control and/or the control of a second QA-responsive transcription factor (NCU03643) both in the fungal binuclear Zn(II)2Cys6 cluster family. QA-1F regulation is not sufficient to explain the quantitative variation in expression profiles of the 50 QA-responsive genes. QA-responsive genes include genes with products in 8 mutually connected metabolic pathways with 7 of them one step removed from the tricarboxylic (TCA) Cycle and with 7 of them one step removed from glycolysis: (1) starch and sucrose metabolism; (2) glycolysis/glucanogenesis; (3) TCA Cycle; (4) butanoate metabolism; (5) pyruvate metabolism; (6) aromatic amino acid and QA metabolism; (7) valine, leucine, and isoleucine degradation; and (8) transport of sugars and amino acids. Gene products both in aromatic amino acid and QA metabolism and transport show an immediate response to shift to QA, while genes with products in the remaining 7 metabolic modules generally show a delayed response to shift to QA. The additional QA-responsive cutinase transcription factor-1β (NCU03643) is found to have a delayed response to shift to QA. The series of microarray experiments are used to expand the previously identified genetic network describing the qa gene cluster to include all 50 QA-responsive genes including the second transcription factor (NCU03643). These studies illustrate new methodologies from systems biology to guide model-driven discoveries about a core metabolic network involving carbon and amino acid metabolism in N. crassa

    Particulate emissions from commercial shipping: chemical, physical, and optical properties

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    , 39% OM, and 15% BC and differs from inventories that used 81%, 14%, and 5% and 31%, 63%, and 6% SO 4 2À , OM, and BC, respectively. SO 4 2À and OM mass were found to be dependent on fuel sulfur content as were SSA, hygroscopicity, and CCN concentrations. BC mass was dependent on engine type and combustion efficiency. A plume evolution study conducted on one vessel showed conservation of particle light absorption, decrease in CN > 5 nm, increase in particle hygroscopicity, and an increase in average particle size with distance from emission. These results suggest emission of small nucleation mode particles that subsequently coagulate/condense onto larger BC and OM. This work contributes to an improved understanding of the impacts of ship emissions on climate and air quality and will also assist in determining potential effects of altering fuel standards
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