Simulation of live-cell imaging system reveals hidden uncertainties in cooperative binding measurements
Masaki Watabe, Satya N. V. Arjunan, Wei Xiang Chew, Kazunari Kaizu and, Koichi Takahashi
TL;DR
This paper introduces a computational approach to assess hidden uncertainties in live-cell imaging measurements, specifically revealing how non-statistical errors can distort the assessment of molecular cooperativity.
Contribution
It presents a novel computational method to evaluate systematic uncertainties in live-cell imaging, improving the accuracy of biological cooperativity measurements.
Findings
Non-statistical uncertainties can lead to incorrect cooperativity identification.
The method enables more objective interpretation of live-cell imaging data.
Systematic errors significantly impact measurement reliability.
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 biological cooperativity of molecular binding networks: in particular, ligand molecules binding to cell surface receptor proteins. Our results show how the non-statistical uncertainties lead to invalid identification 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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