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computational model of aa metabolic network  (MathWorks Inc)


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    MathWorks Inc computational model of aa metabolic network
    Computational Model Of Aa Metabolic Network, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Computational pipeline to predict tissue function using tissue‐level gene expression data. Cartoon outlining the update of the C. elegans metabolic network model. GPR, gene‐protein‐reaction association. Conceptual overview of integration of iCEL1314 with four categories of genes: highly, moderately, lowly, and rarely expressed. The predicted flux state in a tissue is a flux distribution that trails reactions associated with highly expressed genes in that tissue, while avoiding those associated with lowly expressed and rarely expressed genes. Circles and arrows indicate metabolites and reactions, respectively. Black arrows show flux, with thicker arrows indicating higher flux. Boxes depict enzymes encoded by genes that have expression levels indicated by color. Dashed arrows indicate reactions with no flux in the preliminary flux distribution stage according to Fig B but are then detected as latent reactions and are forced to carry flux when possible (see text for details). To derive tissue‐relevant metabolic network functions, a gene expression dataset obtained with single‐cell RNA‐seq of L2 animals was used (Cao et al , ). Single‐cell data were combined by the authors to provide high‐quality gene expression data for the seven tissues shown. Distribution of metabolic genes in iCEL1314 in different expression categories in each individual tissue and in all tissues combined, with colors as in (B). For the combination of data, the union set of highly expressed genes and the intersection set of rarely and lowly expressed genes are illustrated with corresponding colors. One gene which was lowly expressed in some tissues and rarely expressed in others is not shown in the combined data.

    Journal: Molecular Systems Biology

    Article Title: Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels

    doi: 10.15252/msb.20209649

    Figure Lengend Snippet: Computational pipeline to predict tissue function using tissue‐level gene expression data. Cartoon outlining the update of the C. elegans metabolic network model. GPR, gene‐protein‐reaction association. Conceptual overview of integration of iCEL1314 with four categories of genes: highly, moderately, lowly, and rarely expressed. The predicted flux state in a tissue is a flux distribution that trails reactions associated with highly expressed genes in that tissue, while avoiding those associated with lowly expressed and rarely expressed genes. Circles and arrows indicate metabolites and reactions, respectively. Black arrows show flux, with thicker arrows indicating higher flux. Boxes depict enzymes encoded by genes that have expression levels indicated by color. Dashed arrows indicate reactions with no flux in the preliminary flux distribution stage according to Fig B but are then detected as latent reactions and are forced to carry flux when possible (see text for details). To derive tissue‐relevant metabolic network functions, a gene expression dataset obtained with single‐cell RNA‐seq of L2 animals was used (Cao et al , ). Single‐cell data were combined by the authors to provide high‐quality gene expression data for the seven tissues shown. Distribution of metabolic genes in iCEL1314 in different expression categories in each individual tissue and in all tissues combined, with colors as in (B). For the combination of data, the union set of highly expressed genes and the intersection set of rarely and lowly expressed genes are illustrated with corresponding colors. One gene which was lowly expressed in some tissues and rarely expressed in others is not shown in the combined data.

    Article Snippet: iCEL1314 (genome‐scale metabolic network model of C. elegans ) , This study, Yilmaz & Walhout ( ) , BioModels (Chelliah et al , ): MODEL2007280001 .

    Techniques: Gene Expression, Expressing, RNA Sequencing

    Histograms representing average gene expression in seven tissues combined (upper panel) and in the intestine alone as an example tissue (lower panel). The mean ( μ ) and standard deviation ( σ ) of high expression (HES) and low expression (LES) subpopulation of genes were determined by curve fitting and used to define the thresholds for gene categorization. Red ( μ LES ), yellow ( μ LES + σ LES ), and green ( μ HES ) lines are hard thresholds for rarely, lowly, and highly expressed genes, respectively (lower panel). Dashed green line ( μ HES + σ HES ) indicates the relative expression threshold for describing additional highly and lowly expressed genes based on enrichment/depletion analysis as described in Fig . Bars reflect frequency of genes in the corresponding category based on color. Metabolic genes that are part of iCEL1314 are indicated with hatched bars (see <xref ref-type=Appendix Fig S1 for the histograms of metabolic genes). " width="100%" height="100%">

    Journal: Molecular Systems Biology

    Article Title: Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels

    doi: 10.15252/msb.20209649

    Figure Lengend Snippet: Histograms representing average gene expression in seven tissues combined (upper panel) and in the intestine alone as an example tissue (lower panel). The mean ( μ ) and standard deviation ( σ ) of high expression (HES) and low expression (LES) subpopulation of genes were determined by curve fitting and used to define the thresholds for gene categorization. Red ( μ LES ), yellow ( μ LES + σ LES ), and green ( μ HES ) lines are hard thresholds for rarely, lowly, and highly expressed genes, respectively (lower panel). Dashed green line ( μ HES + σ HES ) indicates the relative expression threshold for describing additional highly and lowly expressed genes based on enrichment/depletion analysis as described in Fig . Bars reflect frequency of genes in the corresponding category based on color. Metabolic genes that are part of iCEL1314 are indicated with hatched bars (see Appendix Fig S1 for the histograms of metabolic genes).

    Article Snippet: iCEL1314 (genome‐scale metabolic network model of C. elegans ) , This study, Yilmaz & Walhout ( ) , BioModels (Chelliah et al , ): MODEL2007280001 .

    Techniques: Gene Expression, Standard Deviation, Expressing

    Dual‐tissue model used for compartmentalization of iCEL1314 during data integration. The two major compartments used are the intestine, which is the point of entry for bacterial nutrients, and another tissue. The lower panel shows the two main steps of integration. First, gene expression data for each tissue except the intestine is integrated with the model individually. Second, integrated flux distributions from the first step are combined using tissue weights that represent the relative mass and activity of each tissue (Fig A, <xref ref-type=Appendix Supplementary Methods ) and the intestine gene expression data is integrated. Flow chart of the optimized integration algorithm. A maximized or minimized variable from a step is carried to the next step as a constraint as shown by equations by the arrows (a bold uppercase term indicates a maximized or minimized sum of variables from the previous step). The δ term stands for small numbers that indicate the tolerance of deviation from the corresponding minimized flux sums. A latent reaction is a reaction that is only associated with highly expressed genes and converts metabolites that are available in the present state of the flux distribution, but does not carry any flux. See text and Appendix Supplementary Methods for details. Example pathways that share genes (only a relevant subset of reactions is shown for each pathway). Dashed arrows indicate skipped parts of the pathway and the rest of the metabolic network. Upper right panel shows expression categories of relevant genes in tissues. Lower right panel shows predicted flux in the propionate shunt obtained with iMAT and iMAT++ algorithms. Epsilon indicates the minimum flux imposed on reactions associated with highly expressed genes during integration ( ε = 0 .01 for every reaction shown). Analysis of agreement between experimental data and integrated flux distribution. The left panel shows percentage ( y ‐axis) and number (bold numbers) of highly expressed genes that have no association with any flux‐carrying reactions. The middle panel shows the same for reactions that depend on rarely expressed genes, but carry flux in the integrated network. The right panel shows the depletion rate of flux in lowly expressed reactions, which is calculated as one minus the ratio of total flux in these reactions to what is expected for the same number of flux‐carrying reactions on average. In each panel, the results for exactly the same set of genes or reactions were extracted from the output of each algorithm and compared ( Appendix Supplementary Methods ). " width="100%" height="100%">

    Journal: Molecular Systems Biology

    Article Title: Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels

    doi: 10.15252/msb.20209649

    Figure Lengend Snippet: Dual‐tissue model used for compartmentalization of iCEL1314 during data integration. The two major compartments used are the intestine, which is the point of entry for bacterial nutrients, and another tissue. The lower panel shows the two main steps of integration. First, gene expression data for each tissue except the intestine is integrated with the model individually. Second, integrated flux distributions from the first step are combined using tissue weights that represent the relative mass and activity of each tissue (Fig A, Appendix Supplementary Methods ) and the intestine gene expression data is integrated. Flow chart of the optimized integration algorithm. A maximized or minimized variable from a step is carried to the next step as a constraint as shown by equations by the arrows (a bold uppercase term indicates a maximized or minimized sum of variables from the previous step). The δ term stands for small numbers that indicate the tolerance of deviation from the corresponding minimized flux sums. A latent reaction is a reaction that is only associated with highly expressed genes and converts metabolites that are available in the present state of the flux distribution, but does not carry any flux. See text and Appendix Supplementary Methods for details. Example pathways that share genes (only a relevant subset of reactions is shown for each pathway). Dashed arrows indicate skipped parts of the pathway and the rest of the metabolic network. Upper right panel shows expression categories of relevant genes in tissues. Lower right panel shows predicted flux in the propionate shunt obtained with iMAT and iMAT++ algorithms. Epsilon indicates the minimum flux imposed on reactions associated with highly expressed genes during integration ( ε = 0 .01 for every reaction shown). Analysis of agreement between experimental data and integrated flux distribution. The left panel shows percentage ( y ‐axis) and number (bold numbers) of highly expressed genes that have no association with any flux‐carrying reactions. The middle panel shows the same for reactions that depend on rarely expressed genes, but carry flux in the integrated network. The right panel shows the depletion rate of flux in lowly expressed reactions, which is calculated as one minus the ratio of total flux in these reactions to what is expected for the same number of flux‐carrying reactions on average. In each panel, the results for exactly the same set of genes or reactions were extracted from the output of each algorithm and compared ( Appendix Supplementary Methods ).

    Article Snippet: iCEL1314 (genome‐scale metabolic network model of C. elegans ) , This study, Yilmaz & Walhout ( ) , BioModels (Chelliah et al , ): MODEL2007280001 .

    Techniques: Gene Expression, Activity Assay, Expressing

    Journal: Molecular Systems Biology

    Article Title: Modeling tissue‐relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels

    doi: 10.15252/msb.20209649

    Figure Lengend Snippet:

    Article Snippet: iCEL1314 (genome‐scale metabolic network model of C. elegans ) , This study, Yilmaz & Walhout ( ) , BioModels (Chelliah et al , ): MODEL2007280001 .

    Techniques: Software

    Host-Pathogen Interaction (HPI) network. The constructed HPI network consisted of 174 interactions (edges) involving 148 human proteins (green) and 30 M. tuberculosis H37Rv (Mtb) proteins (red)

    Journal: BMC Genomics

    Article Title: Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

    doi: 10.1186/s12864-018-4947-8

    Figure Lengend Snippet: Host-Pathogen Interaction (HPI) network. The constructed HPI network consisted of 174 interactions (edges) involving 148 human proteins (green) and 30 M. tuberculosis H37Rv (Mtb) proteins (red)

    Article Snippet: Expression values exhibiting at least 2-fold differential expression are indicated. (XLSX 2703 kb) Additional file 4: Human pathways (KEGG biological pathways) associated with the host-pathogen interaction network. (DOCX 635 kb) Additional file 5: List of genes involved in hypoxic response regulation of M. tuberculosis H37Rv. (XLSX 14 kb) Additional file 6: Detailed method for analysis of gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 412 kb) Additional file 7: List of interacting “transcription factor - gene” pairs of M. tuberculosis H37Rv collated from literature. (XLSX 102 kb) Additional file 8: Comparison of results of the multi-level Boolean model simulation with experimentally obtained gene expression data. (DOCX 71 kb) Additional file 9: Results of the analysis on gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 14 kb) Additional file 10: Details of the adopted method and corresponding results of Flux Balance Analysis (FBA) of M. tuberculosis H37Rv metabolism during hypoxia. (DOCX 27 kb) Additional file 11: The metabolic network model of M. tuberculosis H37Rv (Mtb) which was used for metabolic simulations in the current study (.xml format, can be used with MATLAB - COBRA toolbox or COBRApy framework; url- https://opencobra.github.io /). (XML 1043 kb) Additional file 12: List of metabolic reactions in M. tuberculosis H37Rv which were significantly perturbed (over 2-fold) during hypoxia as compared to aerobic condition (results obtained through FBA simulations) (.xlsx format). (XLSX 14 kb) Additional file 13: Significantly enriched GO biological process terms in the set of M. tuberculosis H37Rv proteins involved in HPIs are listed.

    Techniques: Construct

    Pathways enriched in hypoxia associated genes of M. tuberculosis H37Rv (Mtb). Pathways (GO terms) in Mtb that are observed to be ( a ) enriched in genes corresponding to enduring hypoxic response (EHR), ( b ) negatively enriched in EHR genes and ( c ) enriched in dosR regulon genes involved in initial hypoxic response

    Journal: BMC Genomics

    Article Title: Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

    doi: 10.1186/s12864-018-4947-8

    Figure Lengend Snippet: Pathways enriched in hypoxia associated genes of M. tuberculosis H37Rv (Mtb). Pathways (GO terms) in Mtb that are observed to be ( a ) enriched in genes corresponding to enduring hypoxic response (EHR), ( b ) negatively enriched in EHR genes and ( c ) enriched in dosR regulon genes involved in initial hypoxic response

    Article Snippet: Expression values exhibiting at least 2-fold differential expression are indicated. (XLSX 2703 kb) Additional file 4: Human pathways (KEGG biological pathways) associated with the host-pathogen interaction network. (DOCX 635 kb) Additional file 5: List of genes involved in hypoxic response regulation of M. tuberculosis H37Rv. (XLSX 14 kb) Additional file 6: Detailed method for analysis of gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 412 kb) Additional file 7: List of interacting “transcription factor - gene” pairs of M. tuberculosis H37Rv collated from literature. (XLSX 102 kb) Additional file 8: Comparison of results of the multi-level Boolean model simulation with experimentally obtained gene expression data. (DOCX 71 kb) Additional file 9: Results of the analysis on gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 14 kb) Additional file 10: Details of the adopted method and corresponding results of Flux Balance Analysis (FBA) of M. tuberculosis H37Rv metabolism during hypoxia. (DOCX 27 kb) Additional file 11: The metabolic network model of M. tuberculosis H37Rv (Mtb) which was used for metabolic simulations in the current study (.xml format, can be used with MATLAB - COBRA toolbox or COBRApy framework; url- https://opencobra.github.io /). (XML 1043 kb) Additional file 12: List of metabolic reactions in M. tuberculosis H37Rv which were significantly perturbed (over 2-fold) during hypoxia as compared to aerobic condition (results obtained through FBA simulations) (.xlsx format). (XLSX 14 kb) Additional file 13: Significantly enriched GO biological process terms in the set of M. tuberculosis H37Rv proteins involved in HPIs are listed.

    Techniques:

    Comparative growth rates of different mutants of M. tuberculosis H37Rv (Mtb) compared to wild type bacilli. Relative simulated growth rates (obtained through FBA simulations mimicking hypoxia) of different in silico gene knock-outs of Mtb as compared to the wild type bacilli. Biological functions associated to the knocked-out genes are also indicated in the plot

    Journal: BMC Genomics

    Article Title: Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

    doi: 10.1186/s12864-018-4947-8

    Figure Lengend Snippet: Comparative growth rates of different mutants of M. tuberculosis H37Rv (Mtb) compared to wild type bacilli. Relative simulated growth rates (obtained through FBA simulations mimicking hypoxia) of different in silico gene knock-outs of Mtb as compared to the wild type bacilli. Biological functions associated to the knocked-out genes are also indicated in the plot

    Article Snippet: Expression values exhibiting at least 2-fold differential expression are indicated. (XLSX 2703 kb) Additional file 4: Human pathways (KEGG biological pathways) associated with the host-pathogen interaction network. (DOCX 635 kb) Additional file 5: List of genes involved in hypoxic response regulation of M. tuberculosis H37Rv. (XLSX 14 kb) Additional file 6: Detailed method for analysis of gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 412 kb) Additional file 7: List of interacting “transcription factor - gene” pairs of M. tuberculosis H37Rv collated from literature. (XLSX 102 kb) Additional file 8: Comparison of results of the multi-level Boolean model simulation with experimentally obtained gene expression data. (DOCX 71 kb) Additional file 9: Results of the analysis on gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 14 kb) Additional file 10: Details of the adopted method and corresponding results of Flux Balance Analysis (FBA) of M. tuberculosis H37Rv metabolism during hypoxia. (DOCX 27 kb) Additional file 11: The metabolic network model of M. tuberculosis H37Rv (Mtb) which was used for metabolic simulations in the current study (.xml format, can be used with MATLAB - COBRA toolbox or COBRApy framework; url- https://opencobra.github.io /). (XML 1043 kb) Additional file 12: List of metabolic reactions in M. tuberculosis H37Rv which were significantly perturbed (over 2-fold) during hypoxia as compared to aerobic condition (results obtained through FBA simulations) (.xlsx format). (XLSX 14 kb) Additional file 13: Significantly enriched GO biological process terms in the set of M. tuberculosis H37Rv proteins involved in HPIs are listed.

    Techniques: In Silico

    Number of identified shortest paths. Paths (of length ≤ 5) connecting ( a ) HPI-network, ( b ) the M. tuberculosis H37Rv (Mtb) hypoxic-GRN, and ( c ) the hypoxic-metabolism network of Mtb during its sustenance inside the host cell. The (shortest) paths were traced through a Mtb PPI network (derived from STRING database), and paths wherein at least 50% of the constituent nodes (genes/proteins) were observed to be perturbed during hypoxia were selected

    Journal: BMC Genomics

    Article Title: Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

    doi: 10.1186/s12864-018-4947-8

    Figure Lengend Snippet: Number of identified shortest paths. Paths (of length ≤ 5) connecting ( a ) HPI-network, ( b ) the M. tuberculosis H37Rv (Mtb) hypoxic-GRN, and ( c ) the hypoxic-metabolism network of Mtb during its sustenance inside the host cell. The (shortest) paths were traced through a Mtb PPI network (derived from STRING database), and paths wherein at least 50% of the constituent nodes (genes/proteins) were observed to be perturbed during hypoxia were selected

    Article Snippet: Expression values exhibiting at least 2-fold differential expression are indicated. (XLSX 2703 kb) Additional file 4: Human pathways (KEGG biological pathways) associated with the host-pathogen interaction network. (DOCX 635 kb) Additional file 5: List of genes involved in hypoxic response regulation of M. tuberculosis H37Rv. (XLSX 14 kb) Additional file 6: Detailed method for analysis of gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 412 kb) Additional file 7: List of interacting “transcription factor - gene” pairs of M. tuberculosis H37Rv collated from literature. (XLSX 102 kb) Additional file 8: Comparison of results of the multi-level Boolean model simulation with experimentally obtained gene expression data. (DOCX 71 kb) Additional file 9: Results of the analysis on gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 14 kb) Additional file 10: Details of the adopted method and corresponding results of Flux Balance Analysis (FBA) of M. tuberculosis H37Rv metabolism during hypoxia. (DOCX 27 kb) Additional file 11: The metabolic network model of M. tuberculosis H37Rv (Mtb) which was used for metabolic simulations in the current study (.xml format, can be used with MATLAB - COBRA toolbox or COBRApy framework; url- https://opencobra.github.io /). (XML 1043 kb) Additional file 12: List of metabolic reactions in M. tuberculosis H37Rv which were significantly perturbed (over 2-fold) during hypoxia as compared to aerobic condition (results obtained through FBA simulations) (.xlsx format). (XLSX 14 kb) Additional file 13: Significantly enriched GO biological process terms in the set of M. tuberculosis H37Rv proteins involved in HPIs are listed.

    Techniques: Derivative Assay

    Plots depicting change in reaction fluxes during hypoxia versus lengths of shortest paths connecting metabolic enzymes to HPI and GRN networks. Plots depicting the magnitude of change in reaction fluxes during hypoxia (obtained using FBA simulations) versus the lengths of shortest paths (path lengths) connecting the corresponding enzymes to ( a ) M. tuberculosis H37Rv (Mtb) proteins involved in HPIs, and ( b ) transcription factors in the hypoxia gene regulatory network of Mtb. The median (red line), IQR (box), 1.5 × IQR (whisker), and outliers (red asterisks) corresponding to flux fold change values are indicated in the plot. The plot indicates that metabolic enzymes associated with reactions experiencing higher fold changes during hypoxia are more closely connected (in terms of shorter path lengths) to the proteins involved in HPIs, as compared to the transcription factors known to regulate hypoxic response

    Journal: BMC Genomics

    Article Title: Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

    doi: 10.1186/s12864-018-4947-8

    Figure Lengend Snippet: Plots depicting change in reaction fluxes during hypoxia versus lengths of shortest paths connecting metabolic enzymes to HPI and GRN networks. Plots depicting the magnitude of change in reaction fluxes during hypoxia (obtained using FBA simulations) versus the lengths of shortest paths (path lengths) connecting the corresponding enzymes to ( a ) M. tuberculosis H37Rv (Mtb) proteins involved in HPIs, and ( b ) transcription factors in the hypoxia gene regulatory network of Mtb. The median (red line), IQR (box), 1.5 × IQR (whisker), and outliers (red asterisks) corresponding to flux fold change values are indicated in the plot. The plot indicates that metabolic enzymes associated with reactions experiencing higher fold changes during hypoxia are more closely connected (in terms of shorter path lengths) to the proteins involved in HPIs, as compared to the transcription factors known to regulate hypoxic response

    Article Snippet: Expression values exhibiting at least 2-fold differential expression are indicated. (XLSX 2703 kb) Additional file 4: Human pathways (KEGG biological pathways) associated with the host-pathogen interaction network. (DOCX 635 kb) Additional file 5: List of genes involved in hypoxic response regulation of M. tuberculosis H37Rv. (XLSX 14 kb) Additional file 6: Detailed method for analysis of gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 412 kb) Additional file 7: List of interacting “transcription factor - gene” pairs of M. tuberculosis H37Rv collated from literature. (XLSX 102 kb) Additional file 8: Comparison of results of the multi-level Boolean model simulation with experimentally obtained gene expression data. (DOCX 71 kb) Additional file 9: Results of the analysis on gene regulatory network (GRN) controlling hypoxic response in M. tuberculosis H37Rv. (DOCX 14 kb) Additional file 10: Details of the adopted method and corresponding results of Flux Balance Analysis (FBA) of M. tuberculosis H37Rv metabolism during hypoxia. (DOCX 27 kb) Additional file 11: The metabolic network model of M. tuberculosis H37Rv (Mtb) which was used for metabolic simulations in the current study (.xml format, can be used with MATLAB - COBRA toolbox or COBRApy framework; url- https://opencobra.github.io /). (XML 1043 kb) Additional file 12: List of metabolic reactions in M. tuberculosis H37Rv which were significantly perturbed (over 2-fold) during hypoxia as compared to aerobic condition (results obtained through FBA simulations) (.xlsx format). (XLSX 14 kb) Additional file 13: Significantly enriched GO biological process terms in the set of M. tuberculosis H37Rv proteins involved in HPIs are listed.

    Techniques: Whisker Assay