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

A Method for Fast, High-Precision Characterization of Synthetic Biology Devices

Engineering biological systems with predictable behavior is a foundational goal of synthetic biology. To accomplish this, it is important to accurately characterize the behavior of biological devices. Prior characterization efforts, however, have generally not yielded enough high-quality information to enable compositional design. In the TASBE (A Tool-Chain to Accelerate Synthetic Biological Engineering) project we have developed a new characterization technique capable of producing such data. This document describes the techniques we have developed, along with examples of their application, so that the techniques can be accurately used by others

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

An enhanced CRISPR repressor for targeted mammalian gene regulation.

The RNA-guided endonuclease Cas9 can be converted into a programmable transcriptional repressor, but inefficiencies in target-gene silencing have limited its utility. Here we describe an improved Cas9 repressor based on the C-terminal fusion of a rationally designed bipartite repressor domain, KRAB-MeCP2, to nuclease-dead Cas9. We demonstrate the system's superiority in silencing coding and noncoding genes, simultaneously repressing a series of target genes, improving the results of single and dual guide RNA library screens, and enabling new architectures of synthetic genetic circuits

Highly-efficient Cas9-mediated transcriptional programming

The RNA-guided nuclease Cas9 can be reengineered as a programmable transcription factor. However, modest levels of gene activation have limited potential applications. We describe an improved transcriptional regulator obtained through the rational design of a tripartite activator, VP64-p65-Rta (VPR), fused to nuclease-null Cas9. We demonstrate its utility in activating endogenous coding and noncoding genes, targeting several genes simultaneously and stimulating neuronal differentiation of human induced pluripotent stem cells (iPSCs).National Human Genome Research Institute (U.S.) (Grant P50 HG005550)United States. Dept. of Energy (Grant DE-FG02-02ER63445)Wyss Institute for Biologically Inspired EngineeringNational Science Foundation (U.S.). Graduate Research FellowshipMassachusetts Institute of Technology. Department of Biological EngineeringHarvard Medical School. Department of Genetic

Accurate Predictions of Genetic Circuit Behavior from Part Characterization and Modular Composition

A long-standing goal of synthetic biology is to rapidly engineer new regulatory circuits from simpler devices. As circuit complexity grows, it becomes increasingly important to guide design with quantitative models, but previous efforts have been hindered by lack of predictive accuracy. To address this, we developed Empirical Quantitative Incremental Prediction (EQuIP), a new method for accurate prediction of genetic regulatory network behavior from detailed characterizations of their components. In EQuIP, precisely calibrated time-series and dosage-response assays are used to construct hybrid phenotypic/mechanistic models of regulatory processes. This hybrid method ensures that model parameters match observable phenomena, using phenotypic formulation where current hypotheses about biological mechanisms do not agree closely with experimental observations. We demonstrate EQuIP’s precision at predicting distributions of cell behaviors for six transcriptional cascades and three feed-forward circuits in mammalian cells. Our cascade predictions have only 1.6-fold mean error over a 261-fold mean range of fluorescence variation, owing primarily to calibrated measurements and piecewise-linear models. Predictions for three feed-forward circuits had a 2.0-fold mean error on a 333-fold mean range, further demonstrating that EQuIP can scale to more complex systems. Such accurate predictions will foster reliable forward engineering of complex biological circuits from libraries of standardized devices

Overview of system design.

<p>(A) The general tissue homeostasis design. Proliferation of stem cells (blue) is regulated by their population size through negative feedback (dashed blue line). Sequential differentiation into endodermic, pancreatic, and finally -cells (red) occurs when the stem cell population has sufficient size, and is governed through negative feedback from differentiated cells (dashed red line). (B) Design workflow. Starting with a high-level objective, iterative design proceeds through a top-down decomposition into modules and then basic reactions of the system, followed by analysis and redesign (left). The table columns (right) show the four iterations of system designs presented in this work. Table rows describe the top-down decomposition for each system, and correspond to the workflow at left.</p

Parametric sensitivity analysis.

<p>(A,G) Circuit diagrams of the genetic components considered in (A) oscillator and (G) throttle optimization. (B,H) The most significant RS-HDMR sensitivity indices, , for parametric variations of the oscillator and throttle, respectively. (C,I) Observed S/N values as a function of randomly sampled rate constant values. Around 2000 different parameter sets were tested, with all oscillator or throttle parameters simultaneously varied. Each point represents an individual parameter set. Warmer colors and contour lines indicate higher point density. (D,J) Inferred first-order RS-HDMR functions describing S/N as a function of the parameters sampled in <i>C</i> and <i>I</i>. (E,K) Heat map of the S/N values against the parameters resulting from the 2000 parameter sets tested in <i>C</i> and <i>I</i>. (F,L) RS-HDMR second-order functions describing the cooperative effects between rate constants, corresponding to <i>E</i> and <i>K</i>. Second-order RS-HDMR functions capture remaining variance after the first-order functions (see <i>D</i> and <i>J</i>) have been subtracted from the data.</p

Phenotypic sensitivity analysis.

<p>(A,F) Phenotypic behavior of the oscillator (A) and throttle (F), when isolated from the full system. Roughly 2000 different sets of rate constants were tested, with all oscillator or throttle rate constants simultaneously varied. Module phenotypes were recorded for each set of rate constants. (B) Observed S/N values as a function of variance in the “duration high” of the oscillator. (C) Heat map of the S/N values against the phenotypes resulting from the random parameter sets. (G) Average ‘images’ for the phenotype <i>R7 T to St. St.</i>, observed from the random parameter sets yielding an S/N value of either 5, 15 or 25. Black represents regions where no switch occurs and no value for <i>R7 T to St. St.</i> is recorded. (D,H) The most significant RS-HDMR sensitivity indices, , for phenotypic variations of the oscillator and throttle, respectively (see also Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002579#pcbi.1002579.s027" target="_blank">Table S8</a>). (E,I) For the oscillator and throttle, respectively, RS-HDMR cross-validation predication accuracy using rate constants, phenotypes, or both.</p