For the past century, biologists have attempted to identify and document qualitative properties that underlie molecular binding processes in living cells . Cooperativity in the binding processes represents a primary and highly general mechanism for achieving the collective behaviour that emerges in 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 .
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 
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