Design, Analysis, And Computational Methods For Engineering Synthetic Biological Networks

Abstract

This thesis advances our understanding of three important aspects of biological systems engineering: analysis, design, and computational methods. First, biological circuit design is necessary to engineer biological systems that behave consistently and follow our design specifications. We contribute by formulating and solving novel problems in stochastic biological circuit design. Second, computational methods for solving biological systems are often limited by the nonlinearity and high dimensionality of the system’s dynamics. This problem is particularly extreme for the parameter identification of stochastic, nonlinear systems. Thus, we develop a method for parameter identification that relies on data-driven stochastic model reduction. Finally, biological system analysis encompasses understanding the stability, performance, and robustness of these systems, which is critical for their implementation. We analyze a sequestration feedback motif for implementing biological control. First, we discuss biological circuit design for the stationary and the transient distributional responses of stochastic biochemical systems. Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture their dynamics. The chemical master equation is a commonly used stochastic model that describes how the probability distribution of a chemically reacting system varies with time. Here we design the distributional response of these stochastic models by formulating and solving it as a constrained optimization problem. Second, we analyze the stability and the performance of a biological controller implemented by a sequestration feedback network motif. Sequestration feedback networks have been implemented in synthetic biology using an array of biological parts. However, their properties of stability and performance are poorly understood. We provide insight into the stability and performance of sequestration feedback networks. Additionally, we provide guidelines for the implementation of sequestration feedback networks. Third, we develop computational methods for the parameter identification of stochastic models of biochemical reaction networks. It is often not possible to find analytic solutions to problems where the dynamics of the underlying biological circuit are stochastic, nonlinear or both. Stochastic models are often challenging due to their high dimensionality and their nonlinearity, which further limits the availability of analytical tools. To address these challenges, we develop a computational method for data-driven stochastic model reduction and we use it to perform parameter identification. Last, we provide concluding remarks and future research directions.