Statistical Mechanical Framework for Predicting Cellular Response

Citation

Forte, Lila (2016) Statistical Mechanical Framework for Predicting Cellular Response. Master's thesis, California Institute of Technology. doi:10.7907/Z9K07273. https://resolver.caltech.edu/CaltechTHESIS:05272016-101847646

Abstract

Developments in singe-cell analysis techniques allow simultaneous high-resolution measurements of 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 highly-flexible, theoretical statistical mechanical framework that uses single-cell cellular component data to model the evolution of the probability distribution of those components in a cell in response to an external, physical or molecular, perturbation. This framework uses Bayesian inference to compare potential functional descriptions of how the perturbation couples to the system, and to 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 multiform cancer cells. We find that oxygen concentration couples not only to individual proteins, but effects the underlying effective interactions between the studied proteins as well. The underlying effective interactions were found to couple linearly to the system, indicating a simple proportional change in the protein network across oxygen concentrations. This description of the system provides improved predictive capabilities for describing the probability distribution of the measured cellular components across a wider range of perturbation conditions than previous methods. Additionally, we apply this methodology to show how it could be used to predict effects in difficult experimental perturbation regimes, identifying undruggable regimes, as well as the result of knocking our individual or combinations of proteins or protein interactions.

Thesis Files