Title page for ETD etd-08252005-075644


Type of Document Dissertation
Author Stigler, Brandilyn Suzanne
URN etd-08252005-075644
Title An Algebraic Approach to Reverse Engineering with an Application to Biochemical Networks
Degree PhD
Department Mathematics
Advisory Committee
Advisor Name Title
Laubenbacher, Reinhard C. Committee Chair
Beattie, Christopher A. Committee Member
Jarrah, Abdul Salam Committee Member
Mendes, Pedro J. P. Committee Member
Keywords
  • discrete modeling
  • polynomial dynamical systems
  • computational algebra
  • gene regulatory networks
  • Reverse engineering
Date of Defense 2005-08-04
Availability unrestricted
Abstract
One goal of systems biology is to predict and modify the behavior of biological networks by accurately monitoring and modeling their responses to certain types of perturbations. The construction of mathematical models based on observation of these responses, referred to as reverse engineering, is an important step in elucidating the structure and dynamics of such networks. Continuous models, described by systems of differential equations, have been used to reverse engineer biochemical networks. Of increasing interest is the use of discrete models, which may provide a conceptual description of the network.

In this dissertation we introduce a discrete modeling approach, rooted in computational algebra, to reverse-engineer networks from experimental time series data. The algebraic method uses algorithmic tools, including Groebner-basis techniques, to build the set of all discrete models that fit time series data and to select minimal models from this set. The models used in this work are discrete-time finite dynamical systems, which, when defined over a finite field, are described by systems of polynomial functions. We present novel reverse-engineering algorithms for discrete models, where each algorithm is suitable for different amounts and types of data. We demonstrate the effectiveness of the algorithms on simulated networks and conclude with a description of an ongoing project to reverse-engineer a real gene regulatory network in yeast.

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