Journal: Scientific Reports

Article Title: Statistics and simulation of growth of single bacterial cells: illustrations with B. subtilis and E. coli

doi: 10.1038/s41598-017-15895-4

Figure Lengend Snippet: Expression data of the reporter gene of B. subtilis . Fluorescent protein expression scales linearly with cell length ( A ) and cell age ( B ), but the correlation is weaker for age. By transforming the extant length (Fig. ) and age (Fig. ) distributions with the linear relation between length and fluorescence and between age and fluorescence, respectively, predictions of the fluorescent distribution can be made. The result clearly shows that cell length ( C , dashed line) is a much better predictor of measured expression levels ( C , blue area), than age ( C , solid line). Also shown, is the distribution of expression levels obtained by stochastic simulation ( C , gray line). Measured fluorescence distributions at ( D ) birth and ( E ) division (blue areas) are compared to stochastic simulations (gray lines). ( F ) Shows the comparison of the measured distribution of the fluorescence concentration of all extant cells (blue) and the simulations (gray line).

Article Snippet: StochPy has basic stochastic simulation algorithms (i.e. of the ‘Gillespie-type’), is readily extendible by the user, uses command-line instructions, allows for coding and saving of models in scripts, has a suite of statistical analysis and plotting tools, is compliant with SBML and can exchange models with the multi-purpose, deterministic modeling software package PySCeS ( http://pysces.sourceforge.net ) for systems biology.

Techniques: Expressing, Fluorescence, Comparison, Concentration Assay

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

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