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stochastic monte carlo (mc) simulation  (MathWorks Inc)


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    MathWorks Inc stochastic monte carlo (mc) simulation
    Stochastic Monte Carlo (Mc) Simulation, 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/product/stochastic+simulation/pm40344242-68-4-11
    Average 90 stars, based on 1 article reviews
    stochastic monte carlo (mc) simulation - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: A farm-to-consumption quantitative microbiological risk assessment for hepatitis E in pigs.
    Article Snippet: Funding information Horizon 2020 Framework Programme, Grant/Award Number: 773830; Department for Environment, Food and Rural Affairs, UK Government, Grant/Award Number: CR2006 Abstract Foodborne transmission appears to be a significant route for human hepatitis E virus (HEV) infection in Europe.. We have developed a quantitative microbiological risk assessment (QMRA) for HEV infection due to consumption of three selected pork products (liver pâté, minced meat, and sliced liver), which models the steps from farm to human consumption in high detail, including within-farm transmission dynamics and microbiological processes such as cross contamination and thermal inactivation.. Our model is unique in that it considers prevalence and viral load of two microbiological variables, HEV RNA and infectious HEV, expressing the latter in terms of the former through so-called “adjustment factors” where data are lacking.

    Article Title: A stochastic model to investigate the effects of control strategies on calves exposed to Ostertagia ostertagi
    Article Snippet: The modelled herd comprised 500 calves generated using a stochastic Monte-Carlo simulation, created in MATLAB ( ).

    Article Title: Phenomenological isotropic visco-hyperelasticity: a differential model based on fractional derivatives
    Article Snippet: The rheological equation of a standard linear solid, i.e., the Zener model, is thermodynamically consistent.. Thus, it was often used as a starting point for the development of nonlinear viscoelastic models, especially for elastomers.. The basic idea of this paper is a generalization of the one-dimensional fractional constitutive equation of the Zener model to large strains.

    Article Title: PSI-SMALP, a Detergent-free Cyanobacterial Photosystem I, Reveals Faster Femtosecond Photochemistry
    Article Snippet: 3 was carried out by a Monte Carlo stochastic optimization method implemented in MATLAB (The MathWorks, Natick, MA).

    Article Title: Comparative Femtosecond Spectroscopy of Primary Photoreactions of Exiguobacterium sibiricum Rhodopsin and Halobacterium salinarum Bacteriorhodopsin.
    Article Snippet: The primary stages of the Exiguobacterium sibiricum rhodopsin (ESR) photocycle were investigated by femtosecond absorption laser spectroscopy in the spectral range of 400−900 nm with a time resolution of 25 fs.. The dynamics of the ESR photoreaction were compared with the reactions of bacteriorhodopsin (bR) in purple membranes (bRPM) and in recombinant form (bRrec).. The primary intermediates of the ESR photocycle were similar to intermediates I, J, and K in bacteriorhodopsin photoconversion.

    Article Title: Electron-Phonon Coupling in Cyanobacterial Photosystem I.
    Article Snippet: is published by the American Chemical Society.. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society.. Copyright © American Chemical Society.

    Software:

    Article Title: Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer
    Article Snippet: .. Monte Carlo simulations of three-state stochastic profiling Monte Carlo simulations of stochastic profiling under the assumption of two regulatory states were performed in MATLAB with available software ( 22 ). ..

    Article Title: Pan-Cancer Drivers Are Recurrent Transcriptional Regulatory Heterogeneities in Early-Stage Luminal Breast Cancer
    Article Snippet: .. Monte Carlo simulations of three-state stochastic profiling Monte Carlo simulations of stochastic profiling under the assumption of two regulatory states were performed in MATLAB with available software (22). ..



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    (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production <t>stochastic</t> model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .
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    (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production stochastic model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet: (A) Pulsing (blue) and constant (orange) 0A~P signal used as input to the biofilm matrix production stochastic model. Pulsing signal corresponds to the signal for growth rate equal to 0.4 h −1 predicted by the phosphorelay network model. The constant 0A~P signal corresponds to the mean of the pulsing signal. (B) Stochastic simulations starting from biofilm matrix inactive state for both constant and pulsing 0A~P signal. Matrix production is considered inactive if the number of TapA molecules < 200 (dashed line). (C) Stochastic simulations starting from matrix active state for both constant and pulsing 0A~P signal. Matrix production is considered active if the number of TapA molecules ≥ 200 (dashed line). (D) Fraction of active cells as a function of time for constant (dark orange) and pulsing 0A~P signal (dark blue) estimated from fitting and to the stochastic simulation data of ‘ Initially OFF cells ’ (light blue) and ‘ Initially ON’ (light orange) cells, respectively. All fits have R 2 >= 0.77 and MSE < 0.005. A total of 2000 simulations were performed. (inset) Estimated values of the biofilm activation rate ( k ON ) and of the matrix deactivation rate assuming pulsing and constant 0A~P input .

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: Activation Assay

    Histogram of TapA molecules obtained from 3000 stochastic simulations (blue bars). Simulation time was set to 50 h. Solid lines represent Gaussian fits derived from a Gaussian Mixture Model with 2 components fitted to the data. The value of 200 molecules was set to be the threshold to distinguish between a biofilm matrix production active and inactive.

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet: Histogram of TapA molecules obtained from 3000 stochastic simulations (blue bars). Simulation time was set to 50 h. Solid lines represent Gaussian fits derived from a Gaussian Mixture Model with 2 components fitted to the data. The value of 200 molecules was set to be the threshold to distinguish between a biofilm matrix production active and inactive.

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: Derivative Assay

    Journal: bioRxiv

    Article Title: Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

    doi: 10.1101/2025.02.13.638117

    Figure Lengend Snippet:

    Article Snippet: The stochastic model was implemented in MATLAB R2023b, and the stochastic simulations ( ) were done using the SimBiology tool of MATLAB R2023b.

    Techniques: