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t7 odes  (MathWorks Inc)


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    MathWorks Inc t7 odes
    (A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual <t>T7</t> <t>ODEs</t> and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.
    T7 Odes, 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
    https://www.bioz.com/result/t7 odes/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    t7 odes - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation"

    Article Title: Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation

    Journal: PLoS Computational Biology

    doi: 10.1371/journal.pcbi.1002746

    (A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.
    Figure Legend Snippet: (A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.

    Techniques Used: Plasmid Preparation, Infection

    (A) Dynamic time courses of experimental host population data uninfected (line is mean of n = 2) and infected cultures (line is mean of n = 3); an immediate drop in population density occurs when the solution of phage is added at , due to dilution. Initial infection multiplicity was 0.1. (B) Measured and simulated phage production per infected host in tryptone broth media (circles are mean, error bars shown are the standard deviation, n = 3). Simulation presented for the integrated model and T7 ODEs alone simulated at . (C) Expanded comparison of the simulated concentrations of critical phage replication machinery and phage virion components compared to T7 ODEs alone. Gene Product 1 is the T7 RNA polymerase; Gene product 10A is the major capsid protein.
    Figure Legend Snippet: (A) Dynamic time courses of experimental host population data uninfected (line is mean of n = 2) and infected cultures (line is mean of n = 3); an immediate drop in population density occurs when the solution of phage is added at , due to dilution. Initial infection multiplicity was 0.1. (B) Measured and simulated phage production per infected host in tryptone broth media (circles are mean, error bars shown are the standard deviation, n = 3). Simulation presented for the integrated model and T7 ODEs alone simulated at . (C) Expanded comparison of the simulated concentrations of critical phage replication machinery and phage virion components compared to T7 ODEs alone. Gene Product 1 is the T7 RNA polymerase; Gene product 10A is the major capsid protein.

    Techniques Used: Infection, Standard Deviation, Comparison

    Shown per infected host, across time, experiment compared to model predictions for integrated model system, and the T7 ODEs alone, on M9 minimal media with glucose, succinate, or acetate as carbon source (growth rates for T7 ODEs alone are , respectively). Error bars are standard deviation of n = 3. For glucose and succinate media the T7 ODEs time course is not visible because it falls directly beneath the integrated simulation line. The lower right panel quantifies the goodness of fit of the integrated simulation and the T7 ODEs alone to experimental observations using normalized mean squared error.
    Figure Legend Snippet: Shown per infected host, across time, experiment compared to model predictions for integrated model system, and the T7 ODEs alone, on M9 minimal media with glucose, succinate, or acetate as carbon source (growth rates for T7 ODEs alone are , respectively). Error bars are standard deviation of n = 3. For glucose and succinate media the T7 ODEs time course is not visible because it falls directly beneath the integrated simulation line. The lower right panel quantifies the goodness of fit of the integrated simulation and the T7 ODEs alone to experimental observations using normalized mean squared error.

    Techniques Used: Infection, Standard Deviation

    Modeling results overlaid with experimental phage production measurements. The machinery-feasible region represents phage production values from T7 ODEs alone, with the growth rate supplied to correlations for availability of the host replication machinery; phage production values above the machinery-feasible boundary are considered machinery infeasible. The upper boundary of the metabolically feasible region was calculated using the integrated simulation, but with access to excess host replication factors, which we simulated by multiplying the host growth rate from FBA by a factor of 1.25 when it was passed to the T7 ODE host machinery correlations. Growth rate variation for calculating limitation boundaries and integrated simulation was evaluated with a set of modified flux bounds, with most growth rate sampling values simulated with both carbon and oxygen limitation, which produced essentially identical phage production predictions (resulting points lie within width of the line displayed). Error bars are standard deviation of n = 3.
    Figure Legend Snippet: Modeling results overlaid with experimental phage production measurements. The machinery-feasible region represents phage production values from T7 ODEs alone, with the growth rate supplied to correlations for availability of the host replication machinery; phage production values above the machinery-feasible boundary are considered machinery infeasible. The upper boundary of the metabolically feasible region was calculated using the integrated simulation, but with access to excess host replication factors, which we simulated by multiplying the host growth rate from FBA by a factor of 1.25 when it was passed to the T7 ODE host machinery correlations. Growth rate variation for calculating limitation boundaries and integrated simulation was evaluated with a set of modified flux bounds, with most growth rate sampling values simulated with both carbon and oxygen limitation, which produced essentially identical phage production predictions (resulting points lie within width of the line displayed). Error bars are standard deviation of n = 3.

    Techniques Used: Metabolic Labelling, Modification, Sampling, Produced, Standard Deviation



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    t7 odes  (MathWorks Inc)
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    MathWorks Inc t7 odes
    (A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual <t>T7</t> <t>ODEs</t> and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.
    T7 Odes, 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
    https://www.bioz.com/result/t7 odes/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    t7 odes - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

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    (A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.

    Journal: PLoS Computational Biology

    Article Title: Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation

    doi: 10.1371/journal.pcbi.1002746

    Figure Lengend Snippet: (A) The combined host-viral form of the integrated FBA problem is a stoichiometric matrix (Stoich.) that can be considered as blocks: left, the independent host stoichiometric matrix; right, viral reactions consuming host metabolites. The combined matrix may be further organized by host metabolites that do not supply viral reactions (rows of the matrix in the upper right) and host metabolites that are consumed by viral reactions (rows at the bottom aligned with Host-Viral Stoich). The vector of fluxes contains host reaction rates at the top and viral reaction fluxes at the bottom to multiply properly with the host-left and viral-right organization of reactions in the stoichiometric matrix. Accumulation is allowed at the intersections of host viral metabolism (Met. Accumulation; right), but the steady-state assumption is enforced for host-only metabolites (0). A simplified flowchart (B) of the algorithm for integrated simulations, where Initialize indicates the definition of media nutritional conditions and the start of iterations across time, simulating at each integration time point the individual T7 ODEs and E. coli FBA, then reconciling the viral rate metabolite demand with host network state supply (Allocate). Both models are then recalculated to incorporate information on their mutual constraint (Revised Viral Demand, and Infected Host Fluxes). Update of environmental information and regulatory constraints at the initiation of each integration step (not specifically denoted on figure) further constrains the host-viral system.

    Article Snippet: We implemented the T7 ODEs in MATLAB (R2011a The MathWorks Inc.), informed by the equations presented in the initial publication as well as the code available for the most recent version .

    Techniques: Plasmid Preparation, Infection

    (A) Dynamic time courses of experimental host population data uninfected (line is mean of n = 2) and infected cultures (line is mean of n = 3); an immediate drop in population density occurs when the solution of phage is added at , due to dilution. Initial infection multiplicity was 0.1. (B) Measured and simulated phage production per infected host in tryptone broth media (circles are mean, error bars shown are the standard deviation, n = 3). Simulation presented for the integrated model and T7 ODEs alone simulated at . (C) Expanded comparison of the simulated concentrations of critical phage replication machinery and phage virion components compared to T7 ODEs alone. Gene Product 1 is the T7 RNA polymerase; Gene product 10A is the major capsid protein.

    Journal: PLoS Computational Biology

    Article Title: Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation

    doi: 10.1371/journal.pcbi.1002746

    Figure Lengend Snippet: (A) Dynamic time courses of experimental host population data uninfected (line is mean of n = 2) and infected cultures (line is mean of n = 3); an immediate drop in population density occurs when the solution of phage is added at , due to dilution. Initial infection multiplicity was 0.1. (B) Measured and simulated phage production per infected host in tryptone broth media (circles are mean, error bars shown are the standard deviation, n = 3). Simulation presented for the integrated model and T7 ODEs alone simulated at . (C) Expanded comparison of the simulated concentrations of critical phage replication machinery and phage virion components compared to T7 ODEs alone. Gene Product 1 is the T7 RNA polymerase; Gene product 10A is the major capsid protein.

    Article Snippet: We implemented the T7 ODEs in MATLAB (R2011a The MathWorks Inc.), informed by the equations presented in the initial publication as well as the code available for the most recent version .

    Techniques: Infection, Standard Deviation, Comparison

    Shown per infected host, across time, experiment compared to model predictions for integrated model system, and the T7 ODEs alone, on M9 minimal media with glucose, succinate, or acetate as carbon source (growth rates for T7 ODEs alone are , respectively). Error bars are standard deviation of n = 3. For glucose and succinate media the T7 ODEs time course is not visible because it falls directly beneath the integrated simulation line. The lower right panel quantifies the goodness of fit of the integrated simulation and the T7 ODEs alone to experimental observations using normalized mean squared error.

    Journal: PLoS Computational Biology

    Article Title: Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation

    doi: 10.1371/journal.pcbi.1002746

    Figure Lengend Snippet: Shown per infected host, across time, experiment compared to model predictions for integrated model system, and the T7 ODEs alone, on M9 minimal media with glucose, succinate, or acetate as carbon source (growth rates for T7 ODEs alone are , respectively). Error bars are standard deviation of n = 3. For glucose and succinate media the T7 ODEs time course is not visible because it falls directly beneath the integrated simulation line. The lower right panel quantifies the goodness of fit of the integrated simulation and the T7 ODEs alone to experimental observations using normalized mean squared error.

    Article Snippet: We implemented the T7 ODEs in MATLAB (R2011a The MathWorks Inc.), informed by the equations presented in the initial publication as well as the code available for the most recent version .

    Techniques: Infection, Standard Deviation

    Modeling results overlaid with experimental phage production measurements. The machinery-feasible region represents phage production values from T7 ODEs alone, with the growth rate supplied to correlations for availability of the host replication machinery; phage production values above the machinery-feasible boundary are considered machinery infeasible. The upper boundary of the metabolically feasible region was calculated using the integrated simulation, but with access to excess host replication factors, which we simulated by multiplying the host growth rate from FBA by a factor of 1.25 when it was passed to the T7 ODE host machinery correlations. Growth rate variation for calculating limitation boundaries and integrated simulation was evaluated with a set of modified flux bounds, with most growth rate sampling values simulated with both carbon and oxygen limitation, which produced essentially identical phage production predictions (resulting points lie within width of the line displayed). Error bars are standard deviation of n = 3.

    Journal: PLoS Computational Biology

    Article Title: Determining Host Metabolic Limitations on Viral Replication via Integrated Modeling and Experimental Perturbation

    doi: 10.1371/journal.pcbi.1002746

    Figure Lengend Snippet: Modeling results overlaid with experimental phage production measurements. The machinery-feasible region represents phage production values from T7 ODEs alone, with the growth rate supplied to correlations for availability of the host replication machinery; phage production values above the machinery-feasible boundary are considered machinery infeasible. The upper boundary of the metabolically feasible region was calculated using the integrated simulation, but with access to excess host replication factors, which we simulated by multiplying the host growth rate from FBA by a factor of 1.25 when it was passed to the T7 ODE host machinery correlations. Growth rate variation for calculating limitation boundaries and integrated simulation was evaluated with a set of modified flux bounds, with most growth rate sampling values simulated with both carbon and oxygen limitation, which produced essentially identical phage production predictions (resulting points lie within width of the line displayed). Error bars are standard deviation of n = 3.

    Article Snippet: We implemented the T7 ODEs in MATLAB (R2011a The MathWorks Inc.), informed by the equations presented in the initial publication as well as the code available for the most recent version .

    Techniques: Metabolic Labelling, Modification, Sampling, Produced, Standard Deviation