Statistical Mechanical Framework for Predicting Cellular Responses from Single-Cell Data

Abstract

Developments in single-cell analysis techniques have provided detailed resolution on cellular component copy number and variation within a cell population. These data provide a probability distribution for all possible states of the cell, as determined by the measured component copy number per cell. We have developed a statistical mechanical framework that uses single-cell protein population data to model the effective interactions of the measured proteins. This method enables us to predict ensemble changes in cellular components under a variety of perturbations. This framework uses Bayesian inference to compare given models or descriptions of the system, as well as determine the uncertainty in the parameter estimations given the data. We have applied this methodology to study the impact of changes in oxygen partial pressure on the behavior of glioblastoma multiforme (GBM) cancer cells. Our framework shows how we can determine the coupling of oxygen concentration changes to the measured proteins and their underlying effective interactions.

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