Title page for ETD etd-08132012-160727


Type of Document Master's Thesis
Author Hofmann, Ariane Leoni
URN etd-08132012-160727
Title A Stochastic Framework to Model Extrinsic Noise in Gene Regulatory Networks
Degree Master of Science
Department Mathematics
Advisory Committee
Advisor Name Title
Laubenbacher, Reinhard C. Committee Chair
Burns, John A. Committee Member
Ciupe, Mihaela Staca Committee Member
Keywords
  • mathematical modeling
  • extrinsic noise
  • gene regulatory networks
Date of Defense 2012-07-31
Availability unrestricted
Abstract
Stochastic modeling to represent intrinsic and extrinsic noise is an important challenge in molecular systems biology. There are numerous ways to model intrinsic noise. One framework for intrinsic noise in gene regulatory networks was recently proposed within the discrete setting. In contrast, extrinsic perturbations were rarely modeled due to the complex mechanisms that contribute to its emergence. Here a discrete framework to model extrinsic noise is proposed. The interacting species of the model are represented by discrete variables and are perturbed to represent extrinsic noise. In particular, they are subject to a discretized lognormal distribution. Additionally, a delay is imposed on the update with a certain probability.

These two perturbations represent global extrinsic noise and pathway-speci c extrinsic noise. It leads to large variations in the concentration of proteins, which is consistent with an existing continuous way of modeling extrinsic fluctuations. The framework is applied to three di fferent published discrete models: the cell fate of lambda phage infection of bacteria, the lactose utilization system in E. coli, and a signaling network in melanoma cells. The framework captures factors that signi cantly contribute to the random decision between lysis and lysogeny as well as explains the bistable switch in the model of the lac operon. Finally, a feed-forward loop analysis is conducted by measuring and comparing the noise level in the target protein of feed-forward loops. This analysis reveals the ability of certain feed-forward loops to attenuate or amplify fluctuations, dependent upon various levels of noise. In conclusion, this thesis aims to resolve the question of how the extrinsic noise can be modeled and how biological systems are able to maintain functionality in the wake of such large variations.

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