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J Comput Aided Mol Des. 2015 Dec;29(12):1073-86. doi: 10.1007/s10822-015-9888-6. Epub 2015 Dec 17.

Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using different dispensing technologies.

Author information

1
Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. sonya.hanson@choderalab.org.
2
Collaborations in Chemistry, Fuquay-Varina, NC, 27526, USA.
3
Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. john.chodera@choderalab.org.

Abstract

All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations--such as the creation of a dilution series with a robotic liquid handler--can significantly amplify imprecision and even contribute substantially to bias. To illustrate these techniques, we review an example of how the choice of dispensing technology can impact assay measurements, and show how large contributions to discrepancies between assays can be easily understood and potentially corrected for. These simple modeling techniques--illustrated with an accompanying IPython notebook--can allow modelers to understand the expected error and bias in experimental datasets, and even help experimentalists design assays to more effectively reach accuracy and imprecision goals.

KEYWORDS:

Acoustic droplet ejection; Assay modeling; Bootstrap principle; Direct dispensing; Dispensing technologies; Error modeling; Liquid handling

PMID:
26678597
PMCID:
PMC4696763
DOI:
10.1007/s10822-015-9888-6
[Indexed for MEDLINE]
Free PMC Article

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