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A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli.

Fehér T, Libis V, Carbonell P, Faulon JL - Front Bioeng Biotechnol (2015)

Bottom Line: Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis.In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels.The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

View Article: PubMed Central - PubMed

Affiliation: Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences , Szeged , Hungary.

ABSTRACT
Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

No MeSH data available.


Example of the mode of fitting models to describe the response of the pBFR1k_RFP_8FapR sensor in the presence of pACYCmatCmatB, induced with 1 mM of IPTG. Na-malonate was included in the medium. The input signal is the measured OD (shown in dotted line and scaled to 50% of maximum in the plot) and the simulated response of the model for the observed output RFP (in black) is shown for a fitting to a first-order model with input derivative (red), a second-order with input derivative (blue), and a third-order with input derivative (green).
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Figure 10: Example of the mode of fitting models to describe the response of the pBFR1k_RFP_8FapR sensor in the presence of pACYCmatCmatB, induced with 1 mM of IPTG. Na-malonate was included in the medium. The input signal is the measured OD (shown in dotted line and scaled to 50% of maximum in the plot) and the simulated response of the model for the observed output RFP (in black) is shown for a fitting to a first-order model with input derivative (red), a second-order with input derivative (blue), and a third-order with input derivative (green).

Mentions: As shown in Figure 10, measured responses were in general fitted with an increasing level of accuracy to each of the three approximate sensor models. For some constructs, however, measurements could not be successfully fitted to model 3 (see Figure S4 in Supplementary Material). Therefore, we decided to use model 2 (the one considering one derivative and two integral terms) as the reference model in order to characterize the dynamics of the sensor. Table 1 provides a list of estimated parameters for different constructs. Time constants ranged between approximately 1 and 10 h.


A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli.

Fehér T, Libis V, Carbonell P, Faulon JL - Front Bioeng Biotechnol (2015)

Example of the mode of fitting models to describe the response of the pBFR1k_RFP_8FapR sensor in the presence of pACYCmatCmatB, induced with 1 mM of IPTG. Na-malonate was included in the medium. The input signal is the measured OD (shown in dotted line and scaled to 50% of maximum in the plot) and the simulated response of the model for the observed output RFP (in black) is shown for a fitting to a first-order model with input derivative (red), a second-order with input derivative (blue), and a third-order with input derivative (green).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4389729&req=5

Figure 10: Example of the mode of fitting models to describe the response of the pBFR1k_RFP_8FapR sensor in the presence of pACYCmatCmatB, induced with 1 mM of IPTG. Na-malonate was included in the medium. The input signal is the measured OD (shown in dotted line and scaled to 50% of maximum in the plot) and the simulated response of the model for the observed output RFP (in black) is shown for a fitting to a first-order model with input derivative (red), a second-order with input derivative (blue), and a third-order with input derivative (green).
Mentions: As shown in Figure 10, measured responses were in general fitted with an increasing level of accuracy to each of the three approximate sensor models. For some constructs, however, measurements could not be successfully fitted to model 3 (see Figure S4 in Supplementary Material). Therefore, we decided to use model 2 (the one considering one derivative and two integral terms) as the reference model in order to characterize the dynamics of the sensor. Table 1 provides a list of estimated parameters for different constructs. Time constants ranged between approximately 1 and 10 h.

Bottom Line: Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis.In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels.The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

View Article: PubMed Central - PubMed

Affiliation: Institute of Systems and Synthetic Biology, University of Evry Val d'Essonne , Evry , France ; Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences , Szeged , Hungary.

ABSTRACT
Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose-response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor's use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

No MeSH data available.