Explicit Tracking of Uncertainty Increases the Power of Quantitative RuleofThumb Reasoning in Cell Biology
Abstract
"Backoftheenvelope" or "ruleofthumb" calculations involving rough estimates of quantities play a central scientific role in developing intuition about the structure and behaviour of physical systems, for example in socalled `Fermi problems' in the physical sciences. Such calculations can be used to powerfully and quantitatively reason about biological systems, particularly at the interface between physics and biology. However, substantial uncertainties are often associated with values in cell biology, and performing calculations without taking this uncertainty into account may limit the extent to which results can be interpreted for a given problem. We present a means to facilitate such calculations where uncertainties are explicitly tracked through the line of reasoning, and introduce a `probabilistic calculator' called Caladis, a web tool freely available at www.caladis.org, designed to perform this tracking. This approach allows users to perform more statistically robust calculations in cell biology despite having uncertain values, and to identify which quantities need to be measured more precisely in order to make confident statements, facilitating efficient experimental design. We illustrate the use of our tool for tracking uncertainty in several example biological calculations, showing that the results yield powerful and interpretable statistics on the quantities of interest. We also demonstrate that the outcomes of calculations may differ from point estimates when uncertainty is accurately tracked. An integral link between Caladis and the Bionumbers repository of biological quantities further facilitates the straightforward location, selection, and use of a wealth of experimental data in cell biological calculations.
 Publication:

Biophysical Journal
 Pub Date:
 December 2014
 DOI:
 10.1016/j.bpj.2014.08.040
 arXiv:
 arXiv:1412.1597
 Bibcode:
 2014BpJ...107.2612J
 Keywords:

 Quantitative Biology  Quantitative Methods;
 Statistics  Methodology
 EPrint:
 8 pages, 3 figures