Abstract
Accurate characterization of promoter activity is important when designing expression systems for systems biology and metabolic engineering applications. Promoters that respond to changes in the environment enable the dynamic control of gene expression without the necessity of inducer compounds, for example. However, the dynamic nature of these processes poses challenges for estimating promoter activity. Most experimental approaches utilize reporter gene expression to estimate promoter activity. Typically the reporter gene encodes a fluorescent protein that is used to infer a constant promoter activity despite the fact that the observed output may be dynamic and is a number of steps away from the transcription process. In fact, some promoters that are often thought of as constitutive can show changes in activity when growth conditions change. For these reasons, we have developed a system of ordinary differential equations for estimating dynamic promoter activity for promoters that change their activity in response to the environment that is robust to noise and changes in growth rate. Our approach, inference of dynamic promoter activity (PromAct), improves on existing methods by more accurately inferring known promoter activity profiles. This method is also capable of estimating the correct scale of promoter activity and can be applied to quantitative data sets to estimate quantitative rates.
Highlights
Recent developments in the fields of systems and synthetic biology have greatly expanded our ability to use engineering principles to model and design cellular pathways
In 2001, Leveau and Lindow modified this earlier method, presenting a model of promoter activity, which they defined as the combined rate of transcription and translation, based on measurement of green fluorescent protein (GFP) fluorescence driven by the promoter of interest.[4]
This model relies on three assumptions that are not necessarily valid in conditions that are relevant to industrial bioprocesses, which are the ultimate targets of most metabolic engineering efforts: first, that the growth rate of the culture is constant, second, that the culture is in the exponential growth phase, and third, that protein levels are at steady state
Summary
Research Article described in bacteria by Jacques Monod in 1941.17 As a result, cultures may have several exponential growth phases with different rates of growth, separated by lag phases of little to no growth. It is currently difficult to measure the number of proteins in a single cell at a high temporal resolution for many strains or replicates, though technologies are being developed to address this problem.[32] currently it is more common to make bulk measurements of the fluorescence or gene expression and biomass of an entire cell culture, and most experimentalists have access to instrumentation for this purpose Using such data, the profile of the average cell in the population can be obtained, which is valid as the measured fluorescence F (often measured in relative fluorescence units [RFU]) is proportional to the number of FP molecules within the culture.[33].
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