Abstract
By exploiting photonic reagents (i.e., coherent control by shaped laser pulses), we employ Optimal Dynamic Discrimination (ODD) as a novel means for quantitatively characterizing mixtures of fluorescent proteins with a large spectral overlap. To illustrate ODD, we simultaneously measured concentrations of in vitro mixtures of Enhanced Blue Fluorescent Protein (EBFP) and Enhanced Cyan Fluorescent Protein (ECFP). Building on this foundational study, the ultimate goal is to exploit the capabilities of ODD for parallel monitoring of genetic and protein circuits by suppressing the spectral cross-talk among multiple fluorescent reporters.
Highlights
Closed-loop, adaptive feedback optimization in the laboratory to discover the specially shaped laser pulses that produce distinct responses from each fluorescent proteins (FPs) within the mixture[13,16,20,21,22]
Utilizing Optimal Dynamic Discrimination (ODD), we analyzed a series of two-component mixtures of Enhanced Blue Fluorescent Protein (EBFP) and Enhanced Cyan Fluorescent Protein (ECFP) dissolved in cell extract at biologically relevant concentrations
The reference solutions were prepared by diluting the stock cell extract to micromolar concentration and characterized by UV excitation/emission spectroscopy
Summary
Optimal Dynamic Discrimination is implemented in two stages. In the learning stage, a stochastic algorithm discovers an optimal series of photonic reagents where each yields distinguishable signals from pure samples of individual FPs. This procedure is iterated to discover the combination of photonic reagents that maximizes the objective function |D| In principle, this algorithm may be repeated until full convergence is reached; in practice the FPs photo-degrade if exposed to laser radiation for extended periods of time, even when circulated, yielding an increasingly uninformative signal at long optimization times. At the end of the learning stage, we have a final N pairs of photonic reagents ranked in descending order by the value of the objective function |D|, characterizing the accuracy of the concentration measurement At this point, the top ranking pair of photonic reagents is the optimum solution, which is nominally sufficient to determine the sample concentrations within a mixture of FPs for analysis. Constrained least squares fitting, D-MORPH regression[26], Bayesian inference, etc. could be used to incorporate knowledge about the system, such as forbidding negative concentrations: nj ≥ 0
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