Depending on the genetic network and host, different perturbations can be applied to control variability.  To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control.  Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli.  (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.
SCA
Depending on the genetic network and host, different perturbations can be applied to control variability.  To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control.  Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli.  (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.
SCA
Depending on the genetic network and host, different perturbations can be applied to control variability.  To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control.  Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli.  (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.
SCA
Depending on the genetic network and host, different perturbations can be applied to control variability.  To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control.  Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli.  (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.
SCA
Depending on the genetic network and host, different perturbations can be applied to control variability.  To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control.  Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli.  (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.
SCA