Cooperative binding processes in living cells represent a primary and highly general mechanism for achieving the collective behaviour that emerges at the systems-level properties of macromolecules, pathways, cells URLanisms. Recent progress in robotics and automated techniques for sampling and analyzing complex biological data can reduce statistical uncertainties (or errors) in measuring cooperativity. An identification of the systematic uncertainties that arise from inaccuracy in the measurement processes, however, has been often non-empirical. Here we propose a comprehensive method to quantitatively evaluate the systematic variances computed not only from a computer simulation of live-cell imaging systems, but also image processing and pattern recognition algorithms for biological images . We then demonstrate that such non-statistical variances can affect our biological interpretation of cooperativity in a binding system of interest. Within this computational scheme, the biological interpretation can be more objectively evaluated and understood under a specific configuration of the measurement processes
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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Cooperative binding processes in living cells represent a primary and highly general mechanism for achieving the collective behaviour that emerges at the systems-level properties of macromolecules, pathways, cells URLanisms. Recent progress in robotics and automated techniques for sampling and analyzing complex biological data can reduce statistical uncertainties (or errors) in measuring cooperativity. An identification of the systematic uncertainties that arise from inaccuracy in the measurement processes, however, has been often non-empirical. Here we propose a comprehensive
We propose a computational
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variances computed not only from a computer simulation of
uncertainties that arise from undetectable sources in biological measurements using
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systems, but also image processing and pattern recognition algorithms for biological images
techniques
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that such
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
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variances can affect our biological interpretation of cooperativity in a binding system of interest. Within
uncertainties lead to invalid identification of the measured cooperativity. Through
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configuration of the measurement processes
experimental configuration of interest.