Ignoring, Accounting For, and Embracing Noise in Biology
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Date
2018-02-15
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Johns Hopkins University
Abstract
The universe is a noisy place in every sense of the word. From the detection of
cosmic microwave background radiation to the formulation and application
of the Heisenberg uncertainty principle, most corners of modern science have
some stochastic flavor. Biology, and cell biology in particular, is no exception.
Cells have a very large number of ever-changing, moving parts and seem to
only rarely lend themselves to simple, deterministic characterization. Having
accepted this, how do we move forward - how do we treat this stochasticity
when we seek to model and ultimately understand the governing functions of
a cell? I would like to discuss three strategies to answer this question.
Firstly, we may ignore the noise. While it is always important to first
acknowledge variations from mean behavior, sometimes grappling with the
prediction of higher moments just distracts from the concept we wish to
express. Second, we may “treat” the noise. Often, we are not writing down
an elegant theory but pipetting and we just want to know if our results are
statistically significant. Slightly better, we may wish to account for some bias in
the calculation of a mean value which is unmeasurable but well approximated,
like the age distribution of a population. Third, and the most interesting case,
we may embrace the noise. Occasionally, the heterogeneity of a population is more revealing of underlying mechanism than even mean behavior, as is
especially true when studying adaptability.
I will cover, in chronological order, five disparate applications of these
three strategies; 1) a model of surface interactions of helical filaments - with a
focused example of amyloid beta fibrils - in which noise is ignored; 2) a study
of bacterial cell cycle duration regulation - in which noise is embraced and
heterogeneity is predicted to confer adaptability to environmental stressors; 3)
an investigation into mammalian cell volume regulation - in which noise is
treated to gauge statistical significance across ensembles of single cells and
age-related heterogeneity is invoked to explain higher moments of observed
volume distributions; 4) a study of stem cell pattern formation - in which
noise is treated to measure the predictive value of a data-driven model of
cell specification; and 5) an investigation into the impacts of ergodicity on
cell cycle duration and population growth rate - in which noise is embraced
and heterogeneity is predicted to imply asymmetry in division under some
commonly assumed conditions.
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Keywords
Cell Biology, Stochasticity, Mathematical Modelling