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network-wide gillespie simulation  (MathWorks Inc)


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    MathWorks Inc network-wide gillespie simulation
    Demonstration and test of CaiNet using a repressive gene cascade. a Regulatory logic of a repressive gene cascade of four genes (left panel) and sketch of the corresponding GRN including transcription repressors and gene elements (right panel). The input species (constant level of 100 molecules) and each gene product repress the subsequent gene element in the GRN. b , c Simulation scenario 1 of CaiNet with deterministic treatment of gene on/off switching and birth/death events of gene products. b Sketches of the probability of activated expression of a gene (upper panel) and the corresponding gene product level (lower panel) of scenario 1. The activation of a gene is constant within one synchronization time step. After each time-step, all gene product levels are synchronized and the activation probabilities of all gene elements are updated. c Comparison of CaiNet simulations of the repressive gene cascade according to scenario 1 performed with different synchronization time steps (red, yellow and purple lines) with the numerical solution of an ODE solver (dashed blue line). d , e Simulation scenario 2 of CaiNet with stochastic treatment of gene on/off switching and deterministic treatment of birth/death events of gene products. d Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 2. e Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (red line) with the numerical solution of an ODE solver (blue line). f – j Simulation scenario 3 of CaiNet with stochastic treatment of gene on/off switching and birth/death events of gene products. f Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 3. g Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (blue line) and scenario 3 (red line) with a <t>Gillespie</t> simulation (yellow line). h , i As in f , g , but with faster gene on/off switching rates. j As in i , but with a constant input level of 1 molecule. Middle and right panels of c , e , g , i , j : histograms and autocorrelation curves of respective gene product levels. In panels ( c , e , g , i , j ) the expression level of the last transcription factor in the cascade is shown
    Network Wide Gillespie 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/result/network-wide gillespie simulation/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    network-wide gillespie simulation - by Bioz Stars, 2026-05
    90/100 stars

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    1) Product Images from "Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics"

    Article Title: Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics

    Journal: BMC Bioinformatics

    doi: 10.1186/s12859-021-04541-6

    Demonstration and test of CaiNet using a repressive gene cascade. a Regulatory logic of a repressive gene cascade of four genes (left panel) and sketch of the corresponding GRN including transcription repressors and gene elements (right panel). The input species (constant level of 100 molecules) and each gene product repress the subsequent gene element in the GRN. b , c Simulation scenario 1 of CaiNet with deterministic treatment of gene on/off switching and birth/death events of gene products. b Sketches of the probability of activated expression of a gene (upper panel) and the corresponding gene product level (lower panel) of scenario 1. The activation of a gene is constant within one synchronization time step. After each time-step, all gene product levels are synchronized and the activation probabilities of all gene elements are updated. c Comparison of CaiNet simulations of the repressive gene cascade according to scenario 1 performed with different synchronization time steps (red, yellow and purple lines) with the numerical solution of an ODE solver (dashed blue line). d , e Simulation scenario 2 of CaiNet with stochastic treatment of gene on/off switching and deterministic treatment of birth/death events of gene products. d Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 2. e Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (red line) with the numerical solution of an ODE solver (blue line). f – j Simulation scenario 3 of CaiNet with stochastic treatment of gene on/off switching and birth/death events of gene products. f Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 3. g Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). h , i As in f , g , but with faster gene on/off switching rates. j As in i , but with a constant input level of 1 molecule. Middle and right panels of c , e , g , i , j : histograms and autocorrelation curves of respective gene product levels. In panels ( c , e , g , i , j ) the expression level of the last transcription factor in the cascade is shown
    Figure Legend Snippet: Demonstration and test of CaiNet using a repressive gene cascade. a Regulatory logic of a repressive gene cascade of four genes (left panel) and sketch of the corresponding GRN including transcription repressors and gene elements (right panel). The input species (constant level of 100 molecules) and each gene product repress the subsequent gene element in the GRN. b , c Simulation scenario 1 of CaiNet with deterministic treatment of gene on/off switching and birth/death events of gene products. b Sketches of the probability of activated expression of a gene (upper panel) and the corresponding gene product level (lower panel) of scenario 1. The activation of a gene is constant within one synchronization time step. After each time-step, all gene product levels are synchronized and the activation probabilities of all gene elements are updated. c Comparison of CaiNet simulations of the repressive gene cascade according to scenario 1 performed with different synchronization time steps (red, yellow and purple lines) with the numerical solution of an ODE solver (dashed blue line). d , e Simulation scenario 2 of CaiNet with stochastic treatment of gene on/off switching and deterministic treatment of birth/death events of gene products. d Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 2. e Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (red line) with the numerical solution of an ODE solver (blue line). f – j Simulation scenario 3 of CaiNet with stochastic treatment of gene on/off switching and birth/death events of gene products. f Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 3. g Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). h , i As in f , g , but with faster gene on/off switching rates. j As in i , but with a constant input level of 1 molecule. Middle and right panels of c , e , g , i , j : histograms and autocorrelation curves of respective gene product levels. In panels ( c , e , g , i , j ) the expression level of the last transcription factor in the cascade is shown

    Techniques Used: Expressing, Activation Assay, Comparison

    CaiNet recovers noise-induced bi-stability and oscillations. a Sketch of a positive autoregulatory feedback motive combined with enzyme-mediated degradation. b Left panel: comparison of a CaiNet simulation of the positive autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: histogram of respective gene product levels. c Sketch of a negative autoregulatory feedback motive combined with enzyme-mediated degradation. d Left panel: comparison of a CaiNet simulation of the negative autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: Fourier transformation of the time traces in the left panel
    Figure Legend Snippet: CaiNet recovers noise-induced bi-stability and oscillations. a Sketch of a positive autoregulatory feedback motive combined with enzyme-mediated degradation. b Left panel: comparison of a CaiNet simulation of the positive autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: histogram of respective gene product levels. c Sketch of a negative autoregulatory feedback motive combined with enzyme-mediated degradation. d Left panel: comparison of a CaiNet simulation of the negative autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: Fourier transformation of the time traces in the left panel

    Techniques Used: Comparison, Transformation Assay



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    network-wide gillespie simulation  (MathWorks Inc)
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    MathWorks Inc network-wide gillespie simulation
    Demonstration and test of CaiNet using a repressive gene cascade. a Regulatory logic of a repressive gene cascade of four genes (left panel) and sketch of the corresponding GRN including transcription repressors and gene elements (right panel). The input species (constant level of 100 molecules) and each gene product repress the subsequent gene element in the GRN. b , c Simulation scenario 1 of CaiNet with deterministic treatment of gene on/off switching and birth/death events of gene products. b Sketches of the probability of activated expression of a gene (upper panel) and the corresponding gene product level (lower panel) of scenario 1. The activation of a gene is constant within one synchronization time step. After each time-step, all gene product levels are synchronized and the activation probabilities of all gene elements are updated. c Comparison of CaiNet simulations of the repressive gene cascade according to scenario 1 performed with different synchronization time steps (red, yellow and purple lines) with the numerical solution of an ODE solver (dashed blue line). d , e Simulation scenario 2 of CaiNet with stochastic treatment of gene on/off switching and deterministic treatment of birth/death events of gene products. d Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 2. e Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (red line) with the numerical solution of an ODE solver (blue line). f – j Simulation scenario 3 of CaiNet with stochastic treatment of gene on/off switching and birth/death events of gene products. f Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 3. g Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (blue line) and scenario 3 (red line) with a <t>Gillespie</t> simulation (yellow line). h , i As in f , g , but with faster gene on/off switching rates. j As in i , but with a constant input level of 1 molecule. Middle and right panels of c , e , g , i , j : histograms and autocorrelation curves of respective gene product levels. In panels ( c , e , g , i , j ) the expression level of the last transcription factor in the cascade is shown
    Network Wide Gillespie 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/result/network-wide gillespie simulation/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    network-wide gillespie simulation - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

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    Demonstration and test of CaiNet using a repressive gene cascade. a Regulatory logic of a repressive gene cascade of four genes (left panel) and sketch of the corresponding GRN including transcription repressors and gene elements (right panel). The input species (constant level of 100 molecules) and each gene product repress the subsequent gene element in the GRN. b , c Simulation scenario 1 of CaiNet with deterministic treatment of gene on/off switching and birth/death events of gene products. b Sketches of the probability of activated expression of a gene (upper panel) and the corresponding gene product level (lower panel) of scenario 1. The activation of a gene is constant within one synchronization time step. After each time-step, all gene product levels are synchronized and the activation probabilities of all gene elements are updated. c Comparison of CaiNet simulations of the repressive gene cascade according to scenario 1 performed with different synchronization time steps (red, yellow and purple lines) with the numerical solution of an ODE solver (dashed blue line). d , e Simulation scenario 2 of CaiNet with stochastic treatment of gene on/off switching and deterministic treatment of birth/death events of gene products. d Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 2. e Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (red line) with the numerical solution of an ODE solver (blue line). f – j Simulation scenario 3 of CaiNet with stochastic treatment of gene on/off switching and birth/death events of gene products. f Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 3. g Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). h , i As in f , g , but with faster gene on/off switching rates. j As in i , but with a constant input level of 1 molecule. Middle and right panels of c , e , g , i , j : histograms and autocorrelation curves of respective gene product levels. In panels ( c , e , g , i , j ) the expression level of the last transcription factor in the cascade is shown

    Journal: BMC Bioinformatics

    Article Title: Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics

    doi: 10.1186/s12859-021-04541-6

    Figure Lengend Snippet: Demonstration and test of CaiNet using a repressive gene cascade. a Regulatory logic of a repressive gene cascade of four genes (left panel) and sketch of the corresponding GRN including transcription repressors and gene elements (right panel). The input species (constant level of 100 molecules) and each gene product repress the subsequent gene element in the GRN. b , c Simulation scenario 1 of CaiNet with deterministic treatment of gene on/off switching and birth/death events of gene products. b Sketches of the probability of activated expression of a gene (upper panel) and the corresponding gene product level (lower panel) of scenario 1. The activation of a gene is constant within one synchronization time step. After each time-step, all gene product levels are synchronized and the activation probabilities of all gene elements are updated. c Comparison of CaiNet simulations of the repressive gene cascade according to scenario 1 performed with different synchronization time steps (red, yellow and purple lines) with the numerical solution of an ODE solver (dashed blue line). d , e Simulation scenario 2 of CaiNet with stochastic treatment of gene on/off switching and deterministic treatment of birth/death events of gene products. d Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 2. e Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (red line) with the numerical solution of an ODE solver (blue line). f – j Simulation scenario 3 of CaiNet with stochastic treatment of gene on/off switching and birth/death events of gene products. f Sketches of the production state of a gene (upper panel, either on or off)) and the corresponding gene product level (lower panel) of scenario 3. g Left panel: comparison of a CaiNet simulation of the repressive gene cascade according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). h , i As in f , g , but with faster gene on/off switching rates. j As in i , but with a constant input level of 1 molecule. Middle and right panels of c , e , g , i , j : histograms and autocorrelation curves of respective gene product levels. In panels ( c , e , g , i , j ) the expression level of the last transcription factor in the cascade is shown

    Article Snippet: A network-wide Gillespie simulation implemented in Matlab took 2 s. We note however, that the Gillespie-Simulation was specifically written and optimized for the positive autofeedback GRN while CaiNet is a framework for general GRNs.

    Techniques: Expressing, Activation Assay, Comparison

    CaiNet recovers noise-induced bi-stability and oscillations. a Sketch of a positive autoregulatory feedback motive combined with enzyme-mediated degradation. b Left panel: comparison of a CaiNet simulation of the positive autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: histogram of respective gene product levels. c Sketch of a negative autoregulatory feedback motive combined with enzyme-mediated degradation. d Left panel: comparison of a CaiNet simulation of the negative autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: Fourier transformation of the time traces in the left panel

    Journal: BMC Bioinformatics

    Article Title: Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics

    doi: 10.1186/s12859-021-04541-6

    Figure Lengend Snippet: CaiNet recovers noise-induced bi-stability and oscillations. a Sketch of a positive autoregulatory feedback motive combined with enzyme-mediated degradation. b Left panel: comparison of a CaiNet simulation of the positive autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: histogram of respective gene product levels. c Sketch of a negative autoregulatory feedback motive combined with enzyme-mediated degradation. d Left panel: comparison of a CaiNet simulation of the negative autoregulatory feedback motive according to scenario 2 (blue line) and scenario 3 (red line) with a Gillespie simulation (yellow line). Right panel: Fourier transformation of the time traces in the left panel

    Article Snippet: A network-wide Gillespie simulation implemented in Matlab took 2 s. We note however, that the Gillespie-Simulation was specifically written and optimized for the positive autofeedback GRN while CaiNet is a framework for general GRNs.

    Techniques: Comparison, Transformation Assay