Foundational platform for mammalian synthetic biology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, February 2013Cataloged from PDF version of thesis.Includes bibliographical references (p. 116-129).The emergent field of synthetic biology is different from many other biological engineering efforts, in that its roots, design principles, and forward engineering perspective have been adopted from electrical engineering and computer science. Synthetic biology is uniquely poised to make great contributions to numerous fields such as bio-fuel, energy production, agriculture and eco-remediation, national defense, and biomedical and tissue engineering. Considerable progress has been made in engineering novel genetic circuits in many different organisms. However, not much progress has been made toward developing a formal methodology to engineer complex genetic systems in mammalian cells. One of the most promising areas of research is the study of embryonic and adult stem cells. Synthetic biology has the potential to greatly impact the progression and development of research in this area of study. A critical impediment to the development of stem cell engineering is the innate complexity, little to no characterization of parts, and limited compositional predictive capabilities. In this thesis, I discuss the strategies used for constructing and optimizing the performance of signaling pathways, the development of a large mammalian genetic part and circuit library, and the characterization and implementation of novel genetic parts and components aimed at developing a foundation for mammalian synthetic biology. I have designed and tested several orthogonal strategies aimed at cell-cell communication in mammalian cells. I have designed a characterization framework for the complete and proper characterization of genetic parts that allows for modular predictive composition of genetic circuits. With this characterization framework I have generated a small library of characterized parts and composite circuits that have well defined input-output relationships that can be used in novel genetic architectures. I also aided in the development of novel analysis and computational tools necessary for accurate predictive composition of these novel circuits. This work collectively provides a foundation for engineering complex intracellular transcriptional networks and intercellular signaling systems in mammalian cells.by Noah Davidsohn.Ph.D

Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity

Synthetic biology efforts have largely focused on small engineered gene networks, yet understanding how to integrate multiple synthetic modules and interface them with endogenous pathways remains a challenge. Here we present the design, system integration, and analysis of several large scale synthetic gene circuits for artificial tissue homeostasis. Diabetes therapy represents a possible application for engineered homeostasis, where genetically programmed stem cells maintain a steady population of β-cells despite continuous turnover. We develop a new iterative process that incorporates modular design principles with hierarchical performance optimization targeted for environments with uncertainty and incomplete information. We employ theoretical analysis and computational simulations of multicellular reaction/diffusion models to design and understand system behavior, and find that certain features often associated with robustness (e.g., multicellular synchronization and noise attenuation) are actually detrimental for tissue homeostasis. We overcome these problems by engineering a new class of genetic modules for ‘synthetic cellular heterogeneity’ that function to generate beneficial population diversity. We design two such modules (an asynchronous genetic oscillator and a signaling throttle mechanism), demonstrate their capacity for enhancing robust control, and provide guidance for experimental implementation with various computational techniques. We found that designing modules for synthetic heterogeneity can be complex, and in general requires a framework for non-linear and multifactorial analysis. Consequently, we adapt a ‘phenotypic sensitivity analysis’ method to determine how functional module behaviors combine to achieve optimal system performance. We ultimately combine this analysis with Bayesian network inference to extract critical, causal relationships between a module's biochemical rate-constants, its high level functional behavior in isolation, and its impact on overall system performance once integrated.National Institutes of Health (U.S.) (NIH NIGMS grant R01GM086881)National Science Foundation (U.S.) (NSF Award #1001092)National Science Foundation (U.S.) (NSF Graduate Research Fellowship Program)Swiss National Science Foundation (SystemsX.ch grant