CaltechTHESIS
  A Caltech Library Service

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. http://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.

Item Type:Thesis (Master's thesis)
Subject Keywords:single cell, statistical mechanics, perturbation, bayesian, hypoxia, steady state
Degree Grantor:California Institute of Technology
Division:Chemistry and Chemical Engineering
Major Option:Chemistry
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Miller, Thomas F.
Thesis Committee:
  • Miller, Thomas F. (chair)
Defense Date:16 May 2016
Record Number:CaltechTHESIS:05272016-101847646
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:05272016-101847646
DOI:10.7907/Z9K07273
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:9782
Collection:CaltechTHESIS
Deposited By: Lila Forte
Deposited On:31 May 2016 16:05
Last Modified:18 May 2017 17:28

Thesis Files

[img]
Preview
PDF - Final Version
See Usage Policy.

2307Kb

Repository Staff Only: item control page