A Monte Carlo method to estimate cell population heterogeneity

Abstract

1 Abstract 1Variation is characteristic of all living systems. Laboratory techniques 2 such as flow cytometry can probe individual cells, and, after decades of 3 experimentation, it is clear that even members of genetically identical cell 4 populations can exhibit differences. To understand whether variation is 5 biologically meaningful, it is essential to discern its source. Mathematical 6 models of biological systems are tools that can be used to investigate causes 7 of cell-to-cell variation. From mathematical analysis and simulation of these 8 models, biological hypotheses can be posed and investigated, then parameter 9 inference can determine which of these is compatible with experimental data. 10 Data from laboratory experiments often consist of "snapshots" representing 11 distributions of cellular properties at different points in time, rather than 12 individual cell trajectories. These data are not straightforward to fit using 13 hierarchical Bayesian methods, which require the number of cell population 14 clusters to be chosen a priori. Here, we introduce a computational sampling 15 method named "Contour Monte Carlo" for estimating mathematical model 16 parameters from snapshot distributions, which is straightforward to imple-17 ment and does not require cells be assigned to predefined categories. Our 18 method is appropriate for systems where observed variation is mostly due to 19 variability in cellular processes rather than experimental measurement error, 20 which may be the case for many systems due to continued improvements in 21 resolution of laboratory techniques. In this paper, we apply our method 22 to quantify cellular variation for three biological systems of interest and 23 provide Julia code enabling others to use this method. 24

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