Parameter-Component Dependency: Identifying the Biological Functions of Interchangeable Genetic Components

Abstract

Synthetic biology can benefit from characterization and analysis of biological components that enable simulation and engineering of large scale networks with complex behavior. In this thesis, we introduce the Parameter Component Dependency (PCD) matrix, a characterization and analysis framework that enables users to quantify the biological functions of interchangeable genetic components, using datasets generated by combinatorial libraries composed of multiple components. We use two synthetic auxin signaling pathways to demonstrate that PCD matrices represent hypotheses about dependencies of model parameters to components. Using the PCD framework, we discriminated and verified multiple such hypotheses systematically and gained mechanistic insights into synthetic auxin signaling. We also present a case study of a synthetic biological system to demonstrate that the PCD framework can be used to analyze systems with little a priori information. By systematically searching through the PCD matrices, we showed that the dependency relationships used to simulate the dataset is recovered exactly.