COSMIC

Automated model building using Taverna workflows from KEGG-Database

methods developed during COSMIC

Despite a long history in using C. acetobutylicum, little is known about the regulation of the metabolic shift, the characteristics of key-regulatory elements as well as bottlenecks of the metabolism. Goal of the collaborative project ´COSMIC-2` (Clostridium acetobutylicum Systems Microbiology 2; part of ‘SysMO’) is to increase the knowledge of this clostridial metabolism and its regulatory patterns. The focus will be on the key regulatory and metabolic events that occur during the shift from the
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Clostridia are very ancient bacteria which evolved before the earth had an oxygen atmosphere. To them the air we breathe is a poison. To survive they produce a spore resting stage, resistant to physical and chemical agents.

Some species cause devastating diseases, such as the superbug Clostridium difficile. On the other hand, most are totally harmless, and make a wide range of chemicals useful to man. The best example is Clostridium acetobutylicum which makes butanol. Butanol is an alcohol, which
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Conversion from KEGG Reactome Information to SBTOOLBOX2 format.

Annotation retrieval

Creation of the KEGG based Reactome

We developed a new metabolomics protocol, which involved a comparison of different harvesting techniques, quenching solutions and extraction methods.

Investigation of the dynamic switch at pH values between 5.8 and 4.5 (pH 5.5, 5.3, 5.1, 4.9, 4.7 and 4.5).

Comparison of the transcriptome at steady state in acidogenesis and at steady state in solventogenesis.

Investigation of all steady state pH-values between pH 5.7 and 4.5 (pH 5.5, 5.3, 5.1, 4.9, 4.7).

Measurements of acetone, butanol, acetate, butyrate and ethanol taken during dynamic shift (pH 5.8, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5) and at steady state (pH 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5).

call
ConvertStdModel2SBTtest('Conti_Ferm_Std', 'CD_Transcriptomic.csv', 'glucose.csv', 'OD.csv',0)

transcriptome, glucose, biomass data inside

acetic acid pulse 100mM
acetic acid step 50mM, 70mM

- automated integration of transcriptomic and reactome data to differential equations
- structure of the paths is maintained
- continuous fermentation model in standard format for data integration, two component model (cell and fermenter)

call >> Kegg2SBToolbox2('model_map.txt', 'reactions_compounds_final.csv','extracellular.txt','testmodel.txt') for an example

where
model_map is the desired mapping of species,
reaction_compounds_final.csv is the entire network,
extracellular.txt is a manual
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acetic acid addition

step experiment:
50mM, 70mM, 100mM, 120mM

pulse
200mM

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
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Here, we use hyperbolic tangents to fit experimental data of AB fermentation in C. acetobutylicum in continous culture at steady state for different external pHs. The estimated parameters are used to define acidogenic and solventogenic phase. Furthermore, an transition phase is identified which cannot be assigned to acidogenesis or solventogenesis.

Several plots compare the fits to the experimental data.

This function estimates the parameters of growth functions of the acid-forming and solvent-forming population observed in 'forward'-shift experiments of phosphate-limited continuous cultures of C. acetobutylicum. The parameters are used in the 'Two-Populations'-Model of the pH-induced metabolic shift.

It assumed that the found behaviour of the optical density during these experiments results from a phenotypic switch caused by the changing pH level.

This model assumes a phenotypic switch between an acid- and solvent-forming population caused by the changing pH levels. The two phenotypes differ in their transcriptomic, proteomic, and ,thus, their metabolomic profile. Because the growth rates of these phenotypes depends on the extracellular pH, the initiation of the pH-shift results in a significant decline of the acidogenic population. Simultaneously, the solvent-forming population rises and establishes an new steady state.

The model is build
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The fitted function describes the pH-drop during 'forward'-shift experiments and the increase of the pH during 'reverse'-shift experiments. The estimated parameters are used to compute the changing pH level in the models of the pH.induced metabolic shift in continuous cultures under phosphate limitation of C. acetobutylicum. Furthermore, the parameters can be applied to join different independent experiments into a single data set.

To fit the changing pH level, an exponential function and a
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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

An ODE model representing the metabolic network governing acid and solvent production by Clostridium acetobutylicum, incorporating the effect of pH upon gene regulation and subsequent end-product levels.

The zip file containes 4 models (in SBML), each representing slightly different experimental conditions.

Protocol for transfer of plasmids into Clostridium
acetobutylicum ATCC 824 by electroporation

ClosTron mutants should always be subjected to Southern blot analysis to ensure that only
one intron insertion has occurred.

A protocol to improve conventional, recombination-based gene knock-out methodologies thtough the provision of negative selection markers, pyrE or codA.

Protocol for transfer of plasmids into
Clostridium spp. by conjugation

A refined and streamlined procedure to generate mutant in a wide range of different clostridial species, using group II intron retargeting methodologies.

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Progress Meeting