Abstract
We propose a computational method to quantitatively evaluate the systematic uncertainties that arise from undetectable sources in biological measurements using live-cell imaging techniques. We then demonstrate this method in measuring the biological cooperativity of molecular binding networks, in particular, ligand molecules binding to cell-surface receptor proteins. Our results show how the nonstatistical uncertainties lead to invalid identifications of the measured cooperativity. Through this computational scheme, the biological interpretation can be more objectively evaluated and understood under a specific experimental configuration of interest.
Highlights
Recent progress in robotic and automated techniques for sampling and analyzing complex biological data can reduce statistical uncertainties, increasing precision in measuring biological and physical properties in living cells [1,2,3,4]
The computational method presented here enables us to evaluate the systematic variance that arises from undetectable sources in cooperative binding measurements using fluorescence microscopy
The study and estimation of the experimental uncertainties have been generally known as an error analysis, its main function being to allow biophysicists to numerically indicate the validity and confidence of their experimental results [6,7,8]
Summary
We propose a computational method to quantitatively evaluate the systematic uncertainties that arise from undetectable sources in biological measurements using live-cell imaging techniques. We demonstrate this method in measuring the biological cooperativity of molecular binding networks, in particular, ligand molecules binding to cell-surface receptor proteins. Our results show how the nonstatistical uncertainties lead to invalid identifications of the measured cooperativity. Through this computational scheme, the biological interpretation can be more objectively evaluated and understood under a specific experimental configuration of interest
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