Biology Reference
In-Depth Information
the computational results with the experimental data collected 6 hours post-induction.
Finally, by clicking on
other parameters such as the cell vol-
ume, integration step, time between saved points, and division time (if any) can be
defined. In many cases, the cell volume has been set equal to 10
2
15
L, as the simulations
refer to
E. coli
cells, and the cell division time and standard deviation were set to
30
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Show Advanced Options
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6
5 min. More advanced simulation parameters available by clicking on
Show More
include the type of algorithm used for the simulation, the tolerance,
and the population threshold under which continuous approaches are replaced by dis-
crete simulation approaches. For the proTeOn and proTeOff simulation the default
values of these parameters were used.
6.
Next, click on
Advanced Options
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to save your model for future implementation. You are
provided with the option to save it in .xml or. nc format.
7.
Finally, click on
'
Export Model
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to run the simulation.
8.
Once your simulation is finished, click on the highlighted
'
Run Simulation
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to save the simu-
lation results for post-processing. You are given the option to save the data either as an
xls. or .nc file.
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Export Data
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Simulating synthetic biological systems stochastically using the SynBioSS DS provides the
user with the opportunity to compare computational with experimental results at both the
population level and the single-cell level. Thus, the simulations capture the average behavior
of the system, as well as the distribution of the protein(s) of interest over the cell
population. While deterministic simulations aim to capture mean population behavior as
well, they cannot provide information at single-cell resolution.
Figure 7.8
illustrates experimental and computational results of 100 000 cells carrying the
proTeOn device. In both cases, the intracellular amount of green fluorescence protein (GFP)
was monitored. The upper panel of
Figure 7.8
compares the average behavior of the cells, as
provided by flow cytometry measurements and SynBioSS DS, at 1, 5, and 10 hours post aTc
induction. The lower panel of
Figure 7.8
shows computational and experimental results at
the single-cell level, the distribution of GFP over 100 000 cells. Overall, both experiments
and simulations report that cells carrying proTeOn exhibit tight aTc-dependent gene
expression regulation. The higher the inducer concentration is, the greater the increase in
gene expression. Interestingly, this protein up-regulation is maintained in the cells for a long
period post-induction. As evident, the SynBioSS DS simulation results agree well with the
experimental phenotype over different inducer concentrations, and for different post-
induction times.
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Similarly to proTeOn, the upper panel of
Figure 7.9
depicts the simulation and
experimental data of the mean GFP value of 100 000 cells possessing the proTeOff system,
and the lower panel of
Figure 7.9
depicts the distribution of GFP across 100 000 cells. As
these four plots show, proTeOff efficiently maintains basal gene expression levels even with
low inducer concentrations. However, it robustly up-regulates expression in the absence of
inducer. Intriguingly, gene expression drops to low levels within a very short time of aTc
administration, and it remains at basal levels for a long period. It should be stressed that
the results provided by SynBioSS DS are consistent with the experimental measurements. As
discussed previously, the model describing proTeOff differs from the proTeOn model by
only the five kinetic parameters that differ between the two experimental systems.
SynBioSS DS is a powerful tool that provides simulation results that accurately describe the
experimental phenotype of synthetic biological systems. Demonstrating that the proTeOn
and proTeOff models match the in vivo system
s behavior well allows for further
quantification of the parameters related to the biological constructs, and provides a guide
for building more complex synthetic systems from these components. Specifically, the
binding strength between the PROTEON and PROTEOFF protein and
tetO
operator can be
quantified both when the former is free and when it is bound to aTc. In addition, the
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