Bacillus subtilis

The workshop focuses on the publication, curation, retrieval, and usage of kinetic data from the reaction kinetics database SABIO-RK and on the use of data in modeling. There will be experience reports from scientists who successfully used experimental data to formulate or verify biological hypotheses with the computer, and you will experience how experimental data can be used with computational models.

BaCell-SysMO 2 Modelling carbon core metabolism in Bacillus subtilis – Exploring the contribution of protein complexes in core carbon and nitrogen metabolism.

Bacillus subtilis is a prime model organism for systems biology approaches because it is one of the most advanced models for functional genomics. Furthermore, comprehensive information on cell and molecular biology, physiology and genetics is available and the European Bacillus community (BACELL) has a well-established reputation for applying ...

Measurement of intra- and extra-cellular metabolome.

We use BSA115 strain which lacks RsbU and RsbW proteins. Therefore, there is limited post-transcriptional regulation of sigmaB activity. SigmaB itself is placed downstream of Pspac, inducible by IPTG. The lacZ reporter gene is downstream of Pctc promoter. IPTG concentrations of 0.1, 0.2 and 1 mM are added in mid-exponential phase at an OD of appr. 0.3. The whole experiment runs for about eight hours.

Theoretical analysis of hypothetical sigma factor competition. Based on the model 'transcription factor competition' possible dynamics of sigma factor competition are simulated and analysed using Lineweaver-Burk representations.

B. subtilis was grown in SMM media with glucose as carbon source and the samples for RNA were harvested OD578nm- 1.0). The stress conditions that were applied over here are growthat 57°C, 16°C, 1.2M Nacl and 37°C(control). All the samples were analysed for transcriptome as biological triplicates.

B. subtilis was grown in M9 media with glucose as carbon source and the samples were harvested during exponential phase (OD600nm- 0.4), early stationary phase(OD600nm- 1.3), late stationary phase(OD600nm- 1.0). All the samples were analysed for transcriptome as biological triplicates.

Theoretical analysis of hypothetical sigma factor competition. Based on the model 'transcription factor competition' possible dynamics of sigma factor competition are simulated and analysed using Lineweaver-Burk representations.

We use BSA115 strain which lacks RsbU and RsbW proteins. Therefore, there is limited post-transcriptional regulation of sigmaB activity.

There occurs an unexpected drop in the beta-Gal activity after sigB induction. This modelling effort aims to clarify the reasons.

Absolute quantification of proteins using heavy labeled QconCAT as an internal standard and quantifying the native proteins in the complex sample via scheduled Multiple Reaction Monitoring(MRM) .

extracellular metabolite concentrations measured by 1H-NMR

intracellular metabolite measured by GC/MS and LC/MS

An ODE model of the gene regulation network governing sporulation initiation in Bacillus subtilis to be run in Matlab.

The network incorporates four sporulation-related signals: nutrient supply, DNA damage, the products of the competence genes and the bacterial population size.

Run execute_bacillus_sporulation_initiation.m to simulate the model. This file also contains the signal-related parameters which can be altered to investigate the effect of competing signals.

Some results for this model ...

The zip file contains two executable Matlab functions.

File named 'fnct_gen_tfcompmod.m' generates a Simbiology model based on the following interactions: R + X <-> RX -> R + X + Px R + Y <-> RY -> R + Y + Py R + Z <-> RZ -> R + Z + Pz Y + Pz -> Pz Px -> Py -> Pz ->

We assume much higher reaction speeds of sigma factor RNApol binding/unbinding compared to protein expression. Protein expression can therefore be represented by Michaelis-Menten like kinetic ...

only lacZ synthesis reduced by inhibitor in BSA115

The model file represents the expression of beta-gal from a sigB dependent promoter after sigb production was stimulated by IPTG. The model is based on an assumption that a hypothetical protein degrates the sigb factor.

This is a JWS model of the successful model for data representation. It realises regulation by a hypothetical sigB dependent protein that degrades beta-Gal.

The model represents a hypothetical situation in which an anti-sigmafactor reduces sigB efficacy.

The zip folder contains files that allow simulation of stressosome dynamics. The models are based on a cellular automaton approach. Each protein of RsbR and RsbS is located in the crystal structure of the stressosome. The proteins can be phosphorylated or not and these states determine the future of neighbouring proteins. To simulate the model open the file 'liebal_stressosome-model_12_workflow-matlab.m' in Matlab. It is written in the cell-model, put the cursor into a cell that you wish to ...

The zip file contains model files and an experiment file. Unpack it in a directory and navigate with matlab to there. Use the 'matlab_execution_guide.m' for simulation and visualisation of the model. This file is written in matlab cell mode, so it is not a stand alone function.

Three models have been developed to test their capacity to reproduce the experimental data from Study: 'Controlled sigmaB induction in shake flask' with Assay: 'IPTG induction of sigmaB in BSA115'. One model assumes a ...

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'.

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] = ...