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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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    Article Title: Optimal control of traffic light signals using stochastic simulation
    Article Snippet: Traffic congestion is one of the serious problems facing modern cities, posing a huge challenge to people’s travel and urban development.. And it has been proved that traffic lights should be responsible for the congestion instead of too many cars on the road occasionally.. So it is very important to find the optimal control of the traffic light signal.

    Article Title: Determining lineage pathways from cellular barcoding experiments.
    Article Snippet: Simulation Given the best-fit ELP model, we implemented a stochastic simulation in MATLAB that follows precisely the rules of the model.

    Article Title: Differential expansion of T central memory precursor and effector subsets is regulated by division speed
    Article Snippet: Stochastic Simulation: In order to find the optimal time of measurement and to check the accuracy of our method, a stochastic simulation was implemented in MATLAB.

    Article Title: Cytokinetic nodes in fission yeast arise from two distinct types of nodes that merge during interphase
    Article Snippet: In the stochastic simulation (MATLAB), type 2 nodes (red) started at x = 0 (top) and diffused with D = 1,000 nm2/s until they were captured (yellow) by passing within 60 nm of 1 of the 65 type 1 nodes (green) located in a Gaussian distribution across a band centered 3 ± 0.8 μm from the new tip.

    Article Title: Incomplete inhibition of HIV infection results in more HIV infected lymph node cells by reducing cell death
    Article Snippet: A stochastic simulation in Matlab was used to generate a distribution for the number of positive wells per cell for each mean number of DNA copies per cell λ .

    Article Title: Feedback between tissue packing and neurogenesis in the zebrafish neural tube
    Article Snippet: For the plot of R as a function of d , R(d) corresponds to the differentiation rate for all cells whose nuclear distance exceeds the value d. Mathematical model A stochastic simulation was implemented in MATLAB.

    Article Title: Feedback between tissue packing and neurogenesis in the zebrafish neural tube
    Article Snippet: A stochastic simulation was implemented in MATLAB.

    Article Title: Synchronized stimulation and continuous insulin sensing in a microfluidic human Islet on a Chip designed for scalable manufacturing
    Article Snippet: Trapping success was predicted using a stochastic simulation in MATLAB that considered the probability for each successive islet being captured by a trap to be a result of the relative flow through that trap compared to the total flow.



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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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    Image Search Results


    (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: