The purpose of reverse engineering is to analyze a system to identify its components, infer their interrelationships, and to create representations of the system at a higher level of abstraction. When applied to biology, the reverse engineering approach must meet the challenge of analyzing complex systems. In this thesis, I apply reverse engineering approaches to (1) infer biochemical pathways from time course data and to (2) understand the contribution of nucleation and polymerization parameters to the formation of cytoskeleton arrays in cells.

Traditional approaches to reverse engineering biochemical pathways either provide the interactions that occur among the molecules of the pathway, or optimize kinetic rates from known interactions. I participated in developing a method to infer both the participating molecules and the reaction rates. I addressed limitations of the method by finding optimal experimental conditions that maximize the performance of the method. The method is shown to successfully recover the Michaelis-Menten mechanism and the glycolytic pathway of the Lactococcus lactis. To expand the accessibility of this method for researchers in the biochemical field, I also developed a graphical user interface (GUI).

Microtubules (MTs) are intracellular polymers critical to many cell functions. In animal cells, MTs initiate (nucleate) from templates on the centrosome to form an array of fixed average MT number and length. MTs stochastically switch between growing and shortening states. I used reverse engineering approaches to determine the contributions of MT nucleation and MT dynamics parameters to the steady-state MT array morphology. I developed of a mathematical model a prediction that nucleation and complete polymer depolymerization additively biases the system toward growing MTs. I tested the model predictions using a Monte-Carlo simulation of the MT array and concluded that the system adjusts to this bias by finding a lower steady-state free subunit concentration favoring shortening MTs.