Models

SBML description of L. lactis glycolysis. Same as the uploaded Copasi file

The model includes glycolysis, pentosephosphate pathway, purine salvage reactions, purine de novo synthesis, redox balance and biomass growth. The network balances adenylate pool as opened moiety.

input: array of investigated quenching temperatures and volumetric flows output: quenching time and coil length as function of quenching temperature, and quenching time as function of temperature for varying coil lengths

The model can simulate the the dynamics of sigB dependent transcription at the transition to starvation. It is was developed along the comic in 'sigB-activation-comic_vol1'. Parameters were partly taken from Delumeau et al., 2002, J. Bact. and Igoshin et al., 2007, JMB. Parameter estimation was performed using experimental data from '0804_shake-flask'. Use the .m-file with matlab as: % reading initial conditions from the file: inic = sigb_model_liebal;

% performing the simulation: [t,y] = ...

Particularly figure 2 of of Abudulikemu et al 2020 in press

The zip-folder contains files for execution in matlab that allow for the simulation of stressosome dynamics and reproduction of published data on the stressosome. The important file for execution is 'liebal_stressosome-model_12_workflow-matlab.m'.

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson1) predicts the limit ...

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson2) predicts the damped ...

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson3) predicts the steady-state ...

Using optical tweezers to position yeast cells in a microfluidic chamber, we were able to observe sustained oscillations in individual isolated cells. Using a detailed kinetic model for the cellular reactions, we simulated the heterogeneity in the response of the individual cells, assuming small differences in a single internal parameter. By operating at two different flow rates per experiment, we observe four of categories of cell behaviour. The present model (gustavsson4) predicts the steady-state ...