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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.


An approximate model of the response behavior of the sensor based on a cascade of first-order filters. The biomass X(t) is assumed to be approximately related to the concentration of malonyl-CoA M(t) through a first-order pole-zero filter. The sensor complex malonyl-CoA-FapR concentration C(t) is then related to M(t) through a first-order filter and finally, the RFP concentration R(t) is again related to C(t) through another first-order filter.
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Figure 9: An approximate model of the response behavior of the sensor based on a cascade of first-order filters. The biomass X(t) is assumed to be approximately related to the concentration of malonyl-CoA M(t) through a first-order pole-zero filter. The sensor complex malonyl-CoA-FapR concentration C(t) is then related to M(t) through a first-order filter and finally, the RFP concentration R(t) is again related to C(t) through another first-order filter.

Mentions: Under the assumptions given by the model described by Eqs 1–4, the response of the sensor can be approximated as a cascade of filters as shown in Figure 9. The advantage of using such approximation is that we can assume that the dynamics due to the time constants of these three filters occur at different time scales and therefore, under the appropriate conditions, time-scale separations can be performed. Based on this principle, we can approximate the model by considering main time constants present in the model, i.e., by estimating parameters of a cascade of first-order models (see Materials and Methods).


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)

An approximate model of the response behavior of the sensor based on a cascade of first-order filters. The biomass X(t) is assumed to be approximately related to the concentration of malonyl-CoA M(t) through a first-order pole-zero filter. The sensor complex malonyl-CoA-FapR concentration C(t) is then related to M(t) through a first-order filter and finally, the RFP concentration R(t) is again related to C(t) through another first-order filter.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: An approximate model of the response behavior of the sensor based on a cascade of first-order filters. The biomass X(t) is assumed to be approximately related to the concentration of malonyl-CoA M(t) through a first-order pole-zero filter. The sensor complex malonyl-CoA-FapR concentration C(t) is then related to M(t) through a first-order filter and finally, the RFP concentration R(t) is again related to C(t) through another first-order filter.
Mentions: Under the assumptions given by the model described by Eqs 1–4, the response of the sensor can be approximated as a cascade of filters as shown in Figure 9. The advantage of using such approximation is that we can assume that the dynamics due to the time constants of these three filters occur at different time scales and therefore, under the appropriate conditions, time-scale separations can be performed. Based on this principle, we can approximate the model by considering main time constants present in the model, i.e., by estimating parameters of a cascade of first-order models (see Materials and Methods).

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.