Team:NTNU Trondheim/Project/Modelling

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To aid in the identification of possible target genes for increasing CO<sub>2</sub>uptake in Synechocystis, <a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003081">the most recent model of the metabolic network in Synechocystis</a> was acquired and analyzed with  
To aid in the identification of possible target genes for increasing CO<sub>2</sub>uptake in Synechocystis, <a href="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003081">the most recent model of the metabolic network in Synechocystis</a> was acquired and analyzed with  
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<a href="http://www.nature.com/nbt/journal/v28/n3/abs/nbt.1614.html">flux balance analysis</a>. The approach initially chosen was simply to fix the CO<sub>2</sub> uptake flux above that achieved in the optimal FBA solution, and examining the effect on the growth rate (figure 1).  
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<a href="http://www.nature.com/nbt/journal/v28/n3/abs/nbt.1614.html">flux balance analysis</a>. Computation was performed it the <a href="http://opencobra.sourceforge.net/openCOBRA/Welcome.html">COBRA toolbox</a> in Matlab 2014a.The approach initially chosen was simply to fix the CO<sub>2</sub> uptake flux above that achieved in the optimal FBA solution, and examining the effect on the growth rate (figure 1).  
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Team:NTNU_Trondheim/Project/Modelling - 2014.igem.org

 

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NTNU Genetically Engineered Machines

Modelling

Flux balance analysis of the impact of increasing CO2-uptake and introducing Glucose Oxidase into Synechocystis sp. PCC6803

To aid in the identification of possible target genes for increasing CO2uptake in Synechocystis, the most recent model of the metabolic network in Synechocystis was acquired and analyzed with flux balance analysis. Computation was performed it the COBRA toolbox in Matlab 2014a.The approach initially chosen was simply to fix the CO2 uptake flux above that achieved in the optimal FBA solution, and examining the effect on the growth rate (figure 1).

Figure 1: Synechocystis growth rate as a function of CO2 uptake flux in an optimal FBA solution.

The growth rate increases from 0 as the CO2 uptake flux is increased from zero to its optimal value (1.19), and decreases after this point. The decrease in growth rate after this optimal CO2 uptake flux is linear with a slope of -0.0083. As the growth rate decreases with increasing CO2 fixaton after this point, this means that any metabolic reaction added to the model that increases the CO2 fixation without also increasing the growth rate, will reduce the growth rate by at least -0.0089/h per unit of flux the CO2 uptake is increased by.

The glucose oxidase gene was added to the model in order to examine its effect on cellular growth. This was done by implementing the reaction:

Since the metabolite D-glucono-1,5-lactone was not present in the original metabolic model, this issue was addressed by the addition of a passive export reaction. The flux through this reaction was then increased from 0, and the CO2 uptake predicted by the FBA solution recorded (figure 2)

Figure 2: CO2 uptake as a function of glucose oxidase flux in an optimal FBA solution.

The FBA solution then predicts that the CO2 uptake increases as the glucose oxidase flux increases. In accordance with the previous observation that the reduction in growth rate would be at least -0.0089/h per per unit of the flux the CO2 uptake was increased by, the reduction in growth rate was found to be -0.0533 per unit of CO2 uptake flux.

Simulation of genetic circuit

The construct chosen to express glucose odxidase (GOx) is a lac promotor regulated by LacI. GOx is therefore inducible by the addition of IPTG. The genetic circuit can be modelled by the following chemical reaction equations:

The system was simulated deterministically using Cain with the parameters in table 1. Parameters were taken from [1].

Table 1: Parameters for the inducible circuit

Reaction rate constant Propensity Comment
k1 0.23 nM minute -1 Transcription of lacI mRNA
k2 15 minute -1 Translation of LacI
k3 0.5 Transcription of GOx mRNA
k4 30 minute -1 Translation of GOx
k5 0.92 minute -1 Passive IPTG influx
k-5 0.92 minute -1 Passive IPTG efflux
k6 50 nM -1 minute -1 LacI dimerization
k-6 1e-3 minute -1 LacI dimer dissociation
k7 3e-7 nM-2 minute -1 LacI dimer IPTG binding
k-7 12 minute -1 LacI dimer IPTG unbinding
k8 960 nM-1 minute -1 Repression of GOx
k-8 2.4 minute -1 Derepression of GOx
d1 0.2 minute -1 Degradation of GOx
d2 0.2 minute -1 Degradation of LacI
d3 0.462 minute -1 Degradation of GOx mRNA
d4 0.462 minute -1 Degradation of LacI mRNA
d5 0.2 minute -1 Degradation of LacI dimer
d6 0.2 minute -1 Degradation of LacI dimer-IPTG complex

A .zip file with the model file can be found here. Simulating the system with various levels of IPTG (figure 3) yields increasing steady state levels of GOx.

Figure 3: Cellular GOx concentration as a function of IPTG concentration