PROJECT TITLE :

Controlling E. coli Gene Expression Noise

ABSTRACT:

Intracellular protein copy numbers show important cell-to-cell variability inside an isogenic population due to the random nature of biological reactions. Here we tend to show how the variability in copy range will be controlled by perturbing gene expression. Depending on the genetic network and host, totally different perturbations will be applied to manage variability. To understand more totally how noise propagates and behaves in biochemical networks we tend to developed stochastic control analysis (SCA) which could be a sensitivity-based analysis framework for the study of noise control. Here we have a tendency to apply SCA to synthetic gene expression systems encoded on plasmids that are reworked into Escherichia coli. We tend to show that (1) dual control of transcription and translation efficiencies provides the most economical approach of noise-versus-mean management. (two) The expressed proteins follow the gamma distribution function as found in chromosomal proteins. (3) One in every of the major sources of noise, leading to the cell-to-cell variability in protein copy numbers, is related to bursty translation. (four) By taking into account stochastic fluctuations in autofluorescence, the right scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.