Dissertationen zum Thema „Synthetic gene circuits“

Synthetic biology is an emergent field incorporating aspects of computer science molecular biology-based methodologies in a systems biology context, taking naturally occurring cellular systems, pathways, and molecules, and selectively engineering them for the generation of novel or beneficial synthetic behaviour. This study described the construction of a novel synthetic gene circuit, which utilises the inducible downstream transcriptional activation properties of the pheromone-response pathway in the budding yeast Saccharomyces cerevisiae as the basis for initiation. The circuit was composed of three novel yeast expression plasmids; (1) a reporter plasmid in which the luciferase reporter gene was fused to the iron response element (IRE), and expressed under the control of the pheromone-inducible FUS1 promoter, (2) a repressor plasmid which constitutively expressed the mammalian iron response protein (IRP), which can bind to the IRE in the luciferase mRNA transcript, blocking translation, and (3) a de-repressor plasmid which also utilised the pheromone-inducible FUS1 promoter to express the bacterial LexA protein that represses transcription of the IRP gene, and thereby de-represses luciferase translation. Yeast cultures were propagated in media that selected for cells containing all three plasmid components of the gene circuit. In these cells, during vegetative growth conditions, reporter gene translation is constitutively repressed by IRP until addition of pheromone. Upon pheromone-induction, the pheromone response pathway up-regulated the expression of the LexA protein which represses transcription of IRP, enabling the translation of luciferase, which is itself up-regulated by the pheromone response pathway. The combination of the repressors functioned to increase the ratio of induction of the reporter gene between pheromone-induced and un-induced states. Proteins and mRNA species expressed by each plasmid were semi-quantified using SDS-PAGE, Western blot, and RT-qPCR. Luciferase expression was measured using an in vitro whole cell luminescence assay, and the data used to define the circuit 'output'. Metabolic control analysis was used prior to building the circuit in silico, and identified the transcription of IRP, as well as the IRP protein half-life as significant control points for increasing the expression of luciferase in vivo. Modelling resulted in the development of multiple variations of the circuit, incorporating strong and weak constitutive promoters for the IRP. For the degradation rate, the IRP was fused with a degradation tag from the PEST rich C-terminal residue of the Cln2 protein, forming IRPPEST , with approximately a 10-fold reduced half-life compared to wild type. By varying the promoter strength and half-life of the IRP, the circuit could be tuned in terms of the amplitude and period of luciferase expression during pheromone induction. Simulated annealing and Hooke-Jeeves algorithms were used to estimate model parameter values from the experimental luminescence data, refining the modelling such that it produced accurate time course simulation of the circuit output. While further characterisation of the individual components would be advantageous, the construction of the system represents a completed cycle of extensive modelling, experimentation, and further model refinement.

La biología sintética se define como la ingeniería de la biología: el (re)diseño y construcción de nuevas partes, dispositivos y sistemas biológicos para realizar nuevas funciones con fines útiles, que se basan en principios elucidados de la biología y la ingeniería. Para facilitar la construcción rápida, reproducible y predecible de estos sistemas biológicos a partir de conjuntos de componentes es necesario desarrollar nuevos métodos y herramientas. La tesis plantea la optimización multiobjetivo como el marco adecuado para tratar los problemas comunes que surgen en el diseño racional y el ajuste óptimo de los circuitos genéticos sintéticos. Utilizando un enfoque clásico de ingeniería de sistemas, la tesis se centra principalmente en: i) el modelado de circuitos genéticos sintéticos basado en los primeros principios, ii) la estimación de parámetros de modelos a partir de datos experimentales y iii) el ajuste basado en modelos para lograr el desempeño deseado de los circuitos. Se han utilizado dos circuitos genéticos sintéticos de diferente naturaleza y con diferentes objetivos y problemas: un circuito de realimentación de tipo 1 incoherente (I1-FFL) que exhibe la importante propiedad biológica de adaptación, y un circuito de detección de quorum sensing y realimentación (QS/Fb) que comprende dos bucles de realimentación entrelazados -uno intracelular y uno basado en la comunicación de célula a célula- diseñado para regular el nivel medio de expresión de una proteína de interés mientras se minimiza su varianza a través de la población de células. Ambos circuitos han sido analizados in silico e implementados in vivo. En ambos casos, se han desarrollado modelos de estos circuitos basado en primeros principios. Se presta especial atención a ilustrar cómo obtener modelos de orden reducido susceptibles de estimación de parámetros, pero manteniendo el significado biológico. La estimación de los parámetros del modelo a partir de los datos experimentales se considera en diferentes escenarios, tanto utilizando modelos determinísticos como estocásticos. Para el circuito I1-FFL se consideran modelos determinísticos. Aquí, la tesis plantea la utilización de modelos locales utilizando la optimización multiobjetivo para realizar la estimación de parámetros del modelo bajo escenarios con estructura de modelo incompleta. Para el circuito QS/Fb, una estructura controlada por realimentación, el problema tratado es la falta de excitabilidad de las señales. La tesis propone una metodología de estimación en dos etapas utilizando modelos estocásticos. La metodología permite utilizar datos de curso temporal promediados de la población y mediciones de distribución en estado estacionario para una sola célula. El ajuste de circuitos basado en modelos para lograr un desempeño deseado también se aborda mediante la optimización multiobjetivo. Para el circuito QS/Fb se realiza un análisis estocástico completo. La tesis aborda cómo tener en cuenta correctamente tanto el ruido intrínseco como el extrínseco, las dos principales fuentes de ruido en los circuitos genéticos. Se analiza el equilibrio entre ambas fuentes de ruido y el papel que desempeñan en el bucle de realimentación intracelular, y en la realimentación extracelular de toda la población. La principal conclusión es que la compleja interacción entre ambos canales de realimentación obliga al uso de la optimización multiobjetivo para el adecuado ajuste del circuito. En esta tesis además del uso adecuado de herramientas de optimización multiobjetivo, la principal preocupación es cómo derivar directrices para el ajuste in silico de parámetros de circuitos que puedan aplicarse de forma realista in vivo en un laboratorio estándar. Como alternativa al análisis de sensibilidad de parámetros clásico, la tesis propone el uso de técnicas de clustering a lo largo de los frentes de Pareto, relacionando el compr
La biologia sintètica es defineix com l'enginyeria de la biologia: el (re) disseny i construcció de noves parts, dispositius i sistemes biològics per a realitzar noves funcions útils que es basen a principis elucidats de la biologia i l'enginyeria. Per facilitar la construcció ràpida, reproduïble i predictible de aquests sistemes biològics a partir de conjunts de components és necessari desenvolupar nous mètodes i eines. La tesi planteja la optimització multiobjectiu com el marc adequat per a tractar els problemes comuns que apareixen en el disseny racional i l' ajust òptim dels circuits genètics sintètics. Utilitzant un enfocament clàssic d'enginyeria de sistemes, la tesi es centra principalment en: i) el modelatge de circuits genètics sintètics basat en primers principis, ii) l' estimació de paràmetres de models a partir de dades experimentals i iii) l' ajust basat en models per aconseguir el rendiment desitjat dels circuits. S'han utilitzat dos circuits genètics sintètics de diferent naturalesa i amb diferents objectius i problemes: un circuit de prealimentació de tipus 1 incoherent (I1-FFL) que exhibeix la important propietat biològica d'adaptació, i un circuit de quorum sensing i realimentació (QS/Fb) que comprèn dos bucles de realimentació entrellaçats -un intracel·lular i un basat en la comunicació de cèl·lula a cèl·lula- dis-senyat per regular el nivell mitjà d'expressió normal d'una proteïna d'interès mentre es minimitza la seua variació al llarg de la població de cèl·lules. Els dos circuits han estat analitzats in silico i implementats in vivo. En tots dos casos, s'han desenvolupat models basats en primers principis d'aquests circuits. Després es presta especial atenció a delinear com obtenir models d'ordre reduït susceptibles de estimació de paràmetres, però mantenint el significat biològic. L' estimació dels paràmetres del model a partir de les dades experimentals es considera en diferents escenaris, tant utilitzant models determinístics com estocàstics. Per al circuit I1-FFL es consideren models determinístics. La tesi planteja la utilització de models locals utilitzant la optimització multiobjectiu per realitzar l'estimació de parametres del model sota escenaris amb estructura de model incompleta (dinàmica no modelada). Per al circuit de QS/Fb, una estructura controlada per realimentació, el problema tractat és la manca d'excitabilitat dels senyals. La tesi proposa una metodologia de estimació en dues etapes utilitzant models estocàstics. La metodologia permet utilitzar dades de curs temporal promediats de la població i mesures de distribució en estat estacionari d'una sola una cèl·lula. L' ajust de circuits basat en models per aconseguir el rendiment desitjat dels circuits també s' aborda mitjançant la optimització multiobjectiu. Per al circuit QS/Fb, es fa un anàlisi estocàstic complet. La tesi aborda com tenir en compte correctament tant el soroll intrínsec com l' extrínsec, les dues principals fonts de soroll en els circuits genètics sintètics. S' analitza l'equilibri entre dues fonts de soroll i el paper que exerceixen en el bucle de realimentació intracel·lular, les i en la realimentació extracel·lular de tota la població. La principal conclusió es que la complexa interacció entre els dos canals de realimentació fa necessari l' ús de la optimització multiobjectiu per al adequat ajust del circuit. En aquesta tesi, a més de l'ús adequat d'eines d'optimització multiobjectiu, la principal preocupació és com derivar directives per al ajust in silico de paràmetres de circuits que puguin aplicar-se de forma realista en viu en un laboratori estàndard. Així, com a alternativa a l'anàlisi de sensibilitat de paràmetres clàssic, la tesi proposa l'ús de l' tècniques de l'agrupació al llarg dels fronts de Pareto, relacionant el compromís de dessempeny amb les regions en l'espai d'paràmetres.
Synthetic biology is defined as the engineering of biology: the deliberate (re)design and construction of novel biological and biologically based parts, devices and systems to perform new functions for useful purposes, that draws on principles elucidated from biology and engineering. Methods and tools are needed to facilitate fast, reproducible and predictable construction of biological systems from sets of biological components. This thesis raises multi-objective optimization as the proper framework to deal with common problems arising in rational design and optimal tuning of synthetic gene circuits. Using a classical systems engineering approach, the thesis mainly addresses: i) synthetic gene circuit modeling based on first principles, ii) model parameters estimation from experimental data and iii) model-based tuning to achieve desired circuit performance. Two gene synthetic circuits of different nature and with different goals and inherent problems have been used throughout the thesis: an Incoherent type 1 feedforward circuit (I1-FFL) that exhibits the important biological property of adaptation, and a Quorum sensing/Feedback circuit (QS/Fb) comprising two intertwined feedback loops -an intracellular one and a cell-to-cell communication-based one-- designed to regulate the mean expression level of a protein of interest while minimizing its variance across the population of cells. Both circuits have been analyzed in silico and implemented in vivo. In both cases, circuit modeling based on first principles has been carried out. Then, special attention is paid to illustrate how to obtain reduced order models amenable for parameters estimation yet keeping biological significance. Model parameters estimation from experimental data is considered in different scenarios, both using deterministic and stochastic models. For the I1-FFL circuit, deterministic models are considered. In this case, the thesis raises ensemble modeling using multi-objective optimization to perform model parameters estimation under scenarios with incomplete model structure (unmodeled dynamics). For the QS/Fb gene circuit, a feedback controlled structure, the lack of excitability of the signals is the problem addressed. The thesis proposes a two-stage estimation methodology using stochastic models. The methodology allows using population averaged time-course data and steady state distribution measurements at the single-cell level. Model-based circuit tuning to achieve desired circuit performance is also addressed using multi-objective optimization. First, for the QS/Fb feedback control circuit, a complete stochastic analysis is performed. Here, the thesis addresses how to correctly take into account both intrinsic and extrinsic noise, the two main sources of noise in gene synthetic circuits. The trade-off between both sources of noise, and the role played by in the intracellular single-cell feedback loop and the extracellular population-wide feedback is analyzed. The main conclusion being that the complex interplay between both feedback channels compel the use of multi-objective optimization for proper tuning of the circuit to achieve desired performance. Thus, the thesis wraps up all the previous results and uses them to address circuit tuning for desired performance. Here, besides the proper use of multi-objective optimization tools, the main concern is how to derive guidelines for circuit parameters tuning in silico that can realistically be applied in vivo in a standard laboratory. Thus, as an alternative to classical parameters sensitivity analysis, the thesis proposes the use of clustering techniques along the optimal Pareto fronts relating the performance trade-offs with regions in the circuits parameters space.
This work has been partially supported by the Spanish Government (CICYT DPI2014- 55276-C5-1) and the European Union (FEDER). The author was recipient of the grant Formación de Personal Investigador by the Universitat Politècnica de València, subprogram 1 (FPI/2013-3242). She was also recipient of the competitive grants for pre-doctoral stays Erasmus Student Placement-European Programme 2015, and FPI Mobility program 2016 of the Universitat Politècnica de València. She also received the competitive grant for a pre-doctoral stay Becas de movilidad para Jóvenes Profesores e Investigadores 2016, Programa de Becas Iberoamérica of the Santander Bank.
Boada Acosta, YF. (2018). A systems engineering approach to model, tune and test synthetic gene circuits [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/112725
TESIS

A cellular counting system based on synthetic gene circuits would enable complex biological programming and be used in many biotechnology applications. Although a variety of synthetic memory circuits have been constructed, basic modules that can be assembled into a counting system are lacking. This thesis focuses on engineering a binary counting module, which can alternate between two states in response to a single repeating input signal. The highly directional large serine bacteriophage integrases were utilised as the basis for the synthetic circuits constructed in this study. Integrases and their protein co-factors, the recombination directionality factor (RDF) can change the orientation of a specific DNA segment flanked by two recombination sites. Integrase alone switches the orientation in one direction, and this directionality is reversed by the addition of its corresponding RDF. The two orientations can be used to turn gene expression on and off, leading to distinct output states which can be thought of as representing a single binary digit (0 and 1) heritably stored in the DNA. In this study, three different serine integrase-based synthetic gene circuits for cellular memory and counting were engineered and characterised. A set-reset latch was first constructed. By expressing ϕC31 integrase and co-expressing integrase with RDF Gp3 from two independent inducible systems, the orientation of the invertible DNA in the set-reset latch was inverted and restored respectively. This device demonstrated that ϕC31 integrase can successfully encode information into plasmid DNA. Next, a state-based latch was constructed, in which the gp3 gene was placed inside the invertible DNA segment to couple its transcriptional regulation to the circuit state. Integrase expression triggered by one input signal resulted in inversion of the invertible DNA, placing the gp3 gene in the correct orientation for transcription. Gp3 expression can then be triggered by another input signal to reverse the directionality of integrase, restoring the DNA back to its original configuration. By optimising the stoichiometry and kinetics of integrase and Gp3 expression, efficient switching of both multi-copy plasmid and single copy chromosomal DNA was achieved. Finally, the state-based latch was developed into a binary counting module by introducing a delay mechanism, in which gp3 transcription was inhibited by a state-based repressor during recombination requiring the absence of Gp3. Placing expression of gp3 under the control of the invertible DNA, allowed a single input signal controlling only integrase expression to switch the module between OFF (0) and ON (1). This is the first integrase-based module that generates different outputs in response to the same input signal and a fundamental step towards building a genetic binary counter with large counting capacity.

La biologie synthétique promet de révolutionner la façon dont les scientifiques manipulent et analysent les systèmes vivants. Dans ce projet, nous proposons de développer une nouvelle classe de réseaux de gènes synthétiques, basée sur la compétition entre la forme active et inactive d'un facteur de transcription synthétique. Afin de déterminer les paramètres moléculaires et les topologies requises pour une fonction voulue, nous utilisons une approche in silico évolutionnaire couplée à de la modélisation. Avec cette méthodologie, nous voulons construire des circuits à multiples entrées, ainsi que de nouveaux réseaux bistables et oscillatoires. Cette nouvelle classe de réseaux pourra par la suite être étendue à des réseaux multi-cellulaires montrant des motifs dissymétriques ou oscillatoires. Ce projet fondamental à l'interface entre la modélisation et la validation expérimentale permettra de promouvoir le développement de circuits avancés avec des applications prometteuses en diagnostique, en thérapie génique et en ingénierie tissulaire avancée
Synthetic biology, by its engineering approach, promise to revolutionize the way scientists manipulate and analyze living systems. In this project, we propose to develop a new class of synthetic gene circuits whose fine tuning rely on the affinity competition between active and inactive forms of a transcription factor. Modelling, together with an in silico evolutionary approach, will be used to determine molecular parameters and network topologies required for a given functionality. Circuits will be assembled accordingly and their expression in mammalian cells measured to confirm the expected response or correct our model. Using this methodology, we plan to build multi-inputs circuits with tunable response function, as well as new bistable and oscillatory circuits. The new investigated class of circuits will also be extended to multi-cellular networks exhibiting symmetry breaking or oscillating patterns. This fundamental project bridging modelling and experimental validation will promote the development of advanced targeting circuits with promising applications in diagnosis, gene therapy and complex tissue engineering

Synthetic Biology is an interdisciplinary research field seeking to correct faulty cellular processes or implement predictable de-novo tasks by engineering biological systems. In this perspective, the potential of developing biosynthetic devices of industrial and medical relevance is hindered by the requirement of accounting for, controlling and finally exploiting the randomness of biochemical events through which biological complexity is implemented. In this thesis mathematical modelling and experimental acquisitions of basic synthetic circuits are adopted to guide the selection of gene expression control mechanisms and network topologies in the design of synthetic devices able to reliably operate in the stochastic cellular context. To this end, a noise tester circuits’ catalogue, intended as a tool for quantitatively investigating the robustness of newly designed synthetic devices, is implemented. Two synthetic gene circuits, exerting either a transcriptional or post-transcriptional control in the expression of a fluorescent reporter, are selected from the circuits’ library for detailed characterization. Based on bulk measurements, deterministic models are defined to identify the kinetic rates of biochemical reactions governing the circuits’ function. The inherently derived stochastic models are further used in numerical computations of plasmid copy number effect on gene expression stochasticity. Subsequently, flow cytometry analysis is used to quantify the steady-state dispersion in protein levels within an isogenic population of transformants. An intriguing feature of the stochastic models describing the observed variance in protein levels is the necessity of including extrinsic components (e.g. cell division events). Numerical analysis identified post-transcriptional control as the best candidate for noise minimization. Finally, we report the results of research undertaken during a period staying at the “Centre for Synthetic and System Biology” of the University of Edinburgh, where the phenotypic consequences of a long-non coding RNA on the transcriptional activation of GAL1-10 promoter in Saccharomyces Cerevisiae are investigated using fluorescence microscopy and microfluidics.

Synthetic biology in mammalian cells holds great promise for reverse engineering biological processes and rewiring cellular behaviors for therapeutic purpose. An essential aspect in our ability to reprogram the cellular code is the availability of highly orthogonal, inducible transcriptional regulators. CRISPR-based strategies employing effector-domain tethering to the single guide RNA (sgRNA)-dCas9 complex have greatly advanced this field by allowing for precise activation or repression of any gene via simple sgRNA reprograming. However, the implementation of inducible CRISPR-based transcriptional regulators (CRISPR-TRs) has so far been restricted to dCas9 protein engineering and conditional effector tethering. Although elegant, these approaches are limited by dCas9 promiscuous loading of sgRNAs, which hinders their use for the creation of independent multi-gene transcriptional programs. To address this limitation, I have developed a modular framework for the rational design of inducible CRISPR-TR, based on simple and reversible modifications of the sgRNA sequence. At the core of this conceptual framework lies the ability to inactivate native sgRNAs by appending on their 5'-end a short RNA segment, which folds to form a spacer-blocking hairpin (SBH). Base-pairing between the extension and the sgRNA spacer prevents docking of the CRISPR-TR on-target, fully abrogating its activity. Subsequently, I have created inducible SBH variants (iSBH) by replacing the hairpin loop with conditional RNA cleaving units. Using a variety of sensing-loops, I was able to engineer a panel of switchable iSBH-sgRNAs, designed to activate specifically in the presence of protein, oligonucleotide, and small molecule inducers. Leveraging the versatility of this method, I demonstrate that iSBH-sgRNAs expression can be multiplexed to assemble synthetic gene circuits implementing parallel and orthogonal regulation of multiple endogenous gene targets. Finally, I have distilled the design principles derived throughout this project to develop a web tool that automates the creation of iSBH- sgRNAs. Already a valuable addition to the synthetic biology toolkit, iSBH-based inducibility should in theory also be applicate to all CRISPR-Cas9 derivatives (genome editing, epigenetic alteration, DNA labelling, etc.) as well as other newly characterized RNA-guide nucleases from the CRISPR family.

The objective of the field of Synthetic Biology is to implement novel functionalities in a biological context or redesign existing biological systems. To achieve this, it employs tried and tested engineering principles, such as standardisation and the design-build-test cycle. A crucial part of this process is the convergence of modelling and experiment. The aim of this thesis is to improve the design principles employed by Synthetic Biology in the context of Gene Regulatory Networks (GRNs). Small Ribonucleic Acids (sRNAs), in particular, are focussed on as a mechanism for post-transcriptional expression regulation, as they present great potential as a tool to be harnessed in GRNs. Modelling sRNA regulation and its interaction with its associated chaperone Host-Factor of Bacteriophage Qβ (Hfq) is investigated. Inclusion of Hfq is found to be necessary in stochastic models, but not in deterministic models. Secondly, feedback is core to the thesis, as it presents a means to scale-up designed systems. A linear design framework for GRNs is then presented, focussing on Transcription Factor (TF) interactions. Such frameworks are powerful as they facilitate the design of feedback. The framework supplies a block diagram methodology for visualisation and analysis of the designed circuit. In this context, phase lead and lag controllers, well-known in the context of Control Engineering, are presented as genetic motifs. A design example, employing the genetic phase lag controller, is then presented, demonstrating how the developed framework can be used to design a genetic circuit. The framework is then extended to include sRNA regulation. Four GRNs, demonstrating the simplest forms of genetic feedback, are then modelled and implemented. The feedback occurs at three different levels: autoregulation, through an sRNA and through another TF. The models of these GRNs are inspired by the implemented biological topologies, focussing on steady state behaviour and various setups. Both deterministic and stochastic models are studied. Dynamic responses of the circuits are also briefly compared. Data is presented, showing good qualitative agreement between models and experiment. Both culture level data and cell population data is presented. The latter of these is particularly useful as the moments of the distributions can be calculated and compared to results from stochastic simulation. The fit of a deterministic model to data is attempted, which results in a suggested extension of the model. The conclusion summarises the thesis, stating that modelling and experiment are in good qualitative agreement. The required next step is to be able to predict behaviour quantitatively.

The present thesis employs ideas of set invariance and sliding modes in order to deal with different relevant problems control of nonlinear systems. Initially, it reviews the techniques of set invariance as well as the more relevant results about sliding modes control. Then the main methodologies used are presented: sliding mode reference conditioning, second order sliding modes and continuous approximation of sliding modes. Finally, the methodologies are applied to different problems in control theory and to a variety of biologically inspired applications. The contributions of the thesis are: The development of a method to coordinate dynamical systems with different dynamic properties by means of a sliding mode auxiliary loop shaping the references given to the systems as function of the local and global goals, the achievable performance of each system and the available information of each system. Design methods for second order sliding mode algorithms. The methods decouple the problem of stability analysis from that of finite-time convergence of the super-twisting sliding mode algorithm. A nonlinear change of coordinates and a time-scaling are used to provide simple, yet flexible design methods and stability proofs. Application of the method to the design of finite-time convergence estimators of bioprocess kinetic rates and specific biomass growth rate, from biomass measurements. Also the estimators are validated with experimental data. The proposal of a strategy to reduce the variability of a cell-to-cell communication signal in synthetic genetic circuits. The method uses set invariance and sliding mode ideas applied to gene expression networks to obtain a reduction in the variance of the communication signal. Experimental approaches available to modify the characteristics of the gene regulation function are described.
Vignoni, A. (2014). Invariance and Sliding Modes. Application to coordination of multi-agent systems, bioprocesses estimation, and control in living cells [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37743
Alfresco

Synthetic biology enables us to create artificial systems using existing biological components, allowing for an exertion of control over the system so that we can further understand how these components interact or bestow them with new capabilities. A multitude of such applications have emerged in recent decades, among them the introduction of protein chimeras and genetic circuits to cells that can be used to accelerate the development of medical treatments and make them safer. T cell immunotherapy is an example of such a technology, and has shown promising results in the treatment of various cancers. However, a persisting obstacle is the inability to control the activity of these engineered cells, as they can become overactive or display off-target activities. We have developed two approaches for controlling T cell activity: a dual small molecule gated ZAP70 switch, and a collection of drug-inducible chimeric antigen receptors (CARs) encompassing the NS3 protease domain. These artificial components allow for increased regulation over T cell therapy and can potentially make T cell therapy safer in the clinic. In addition to improving the safety of clinical treatments, engineered mammalian cells can also act as pathway-sensitive reporters for use in the discovery of gene and drug targets in large-scale screens. However, in traditional compound screens, temporally transient and weak responses may not be detected. Additionally, the decision of what constitutes a “hit” cell population in genome-wide screens can be relatively arbitrary, and thus key target genes could be missed. To address this, a recombinase-based circuit was developed that provides cells with memory, enhanced sensitivity, and an analog-to-digital readout. This reporter facilitates the screening process and enhances both drug and genome-wide screens.
2022-05-18T00:00:00Z

The field of synthetic gene circuits is concerned with engineering novel gene expression dynamics into organisms. This field, a subset of synthetic biology, was started almost two decades ago with the creation of two synthetic circuits: a bistable toggle switch and an oscillator. From the very outset, modeling has played a role in the development of synthetic circuits. However, modeling has been used to gain qualitative insight into dynamics, and actual quantitative modeling has been lagging behind.

Parameters for quantitative models for biological systems often cannot be adequately estimated from measured data, because far too many sets of parameters can produce the same set of limited measured outputs. Additionally, models for synthetic gene circuits are often not correct the first time, and iterating on cycles of modeling and parameter estimation is difficult. Finally, there is a gap between development of modeling and system identification tools and their application to experiments on actual synthetic gene circuits.

This thesis attempts to work towards addressing these issues with quantitative modeling for synthetic gene circuits. First, we derive theoretical conditions for identifiability of stochastic linear systems from heterogenous types of measurement data. These identifiability conditions can provide insight into what type of model to use and what measurements to collect in order to ensure that the resulting model can be identified.

Second, we develop a software package for fast and flexible modeling and parameter estimation for synthetic gene circuits. The user can input models into our software using a simple text format and perform simulations of all types at optimized speeds. By inputting measured experimental data along with the model, the software can be used to perform Bayesian parameter estimation in an automated manner. To bridge the gap between computation and application, we apply this software to parameter estimation of DNA recombinase dynamics using real experimental data collected in an in vitro cell extract.

Finally, we use modeling to guide the design of an improved single gene synthetic oscillator. While the original synthetic genetic oscillator contained three genes, we show that a simple circuit with a single gene can produce robust and synchronized oscillations across a population.

Our results constitute an additional step towards the incorporation of quantitative modeling and parameter inference as part of the design-build-test cycle for synthetic gene circuits.

The study of synthetic gene regulatory networks allows the isolation and investigation of components and motifs in natural regulatory networks. Many synthetic gene networks are regulated at the transcriptional level. In this work, two methods of regulating gene expression at the transcriptional level were studied with the objective of gaining finer control over network behaviour. The first approach focuses on activation and repression of promoters by transcription factors. A synthetic repressor-activator network was engineered using the cI and cro genes and the PRM promoter from bacteriophage λ. The cI and cro genes activated and repressed PRM, respectively, and the monomeric red fluorescent protein (mrfp) gene reported PRM activity. Experimental testing showed an increase in mrfp expression in response to CI, a decrease in mrfp expression in response to Cro, and a differential output that reflected the relative concentrations of CI and Cro when both inputs were applied together. A positive feedback network was then created by placing a cI gene downstream of PRM. The network showed increased expression in response to CI and decreased expression in response to Cro. A negative feedback network was created by placing a cro gene downstream of PRM. Experimental testing showed decreased mrfp expression in response to both inputs. The second approach employed two methods for tuning expression levels without modifying the genes or promoters. First, using a series of networks with tandem mrfp genes under the control of the PLtet0-1 promoter, it was demonstrated that magnitude and range of expression levels could be tuned by adjusting the number of genes in the operon. A network was tuned using this principle by placing luxR genes in tandem to increase the activity of the luxPR promoter. It was then demonstrated that the level of gene expression could be varied through the placement of the gene within an operon. Operons that were three, five and seven genes and contained one green fluorescent protein gene in the first, middle, or end position were created. By comparing green fluorescence levels in induced and uninduced networks, it was found that the gene closest to the promoter was the most inducible.

Living cells are exquisite systems. They are strongly regulated to perform in highly specific ways, but are at the same time wonderfully robust. This combination arises from the sophistication of their construction and operation: their internal variables are carefully controlled by complex networks of dynamic biochemical interactions, crafted and refined by billions of years of evolution. Using mod- ern DNA engineering technology, scientists have begun to circumvent the long process of evolution by employing a rational design-based approach to construct novel gene networks inside living cells. Currently, these synthetic networks are relatively simple when compared to their natural counter- parts, but future prospects are promising, and synthetic biologists would one day like to be able to control cells using genetic circuits much in the way that electronic devices are controlled using electrical circuits. The importance of precise dynamical behaviour in living organisms suggests that this endeavour would benefit greatly from the insights of control theory. However, the nature of bio- chemical networks can make the implementation of even basic control structures challenging. This thesis focusses specifically on the concept of integral control in this context. Integral control is a fun- damental strategy in control theory that is central to regulation, sensory adaptation, and long-term robustness. Consequently, its implementation in a synthetic gene network is an attractive prospect. Here, the general challenges and important design considerations associated with engineering an in-cell synthetic integral controller are laid out. Specific implementations using transcriptional regu- lation are studied analytically and then in silico using models constructed with commonly available parts from the bacterium Escherichia coli. Finally, using a controller based on post-translational signalling, an on-paper design is proposed for an integral-controlled biosynthesis network intended to allow a population of engineered Saccharomyces cerevisiae cells to actively regulate the extracellular concentration of a small molecule.

abstract: Synthetic biology is a novel method that reengineers functional parts of natural genes of interest to build new biomolecular devices able to express as designed. There is increasing interest in synthetic biology due to wide potential applications in various fields such as clinics and fuel production. However, there are still many challenges in synthetic biology. For example, many natural biological processes are poorly understood, and these could be more thoroughly studied through model synthetic gene networks. Additionally, since synthetic biology applications may have numerous design constraints, more inducer systems should be developed to satisfy different requirements for genetic design. This thesis covers two topics. First, I attempt to generate stochastic resonance (SR) in a biological system. Synthetic bistable systems were chosen because the inducer range in which they exhibit bistability can satisfy one of the three requirements of SR: a weak periodic force is unable to make the transition between states happen. I synthesized several different bistable systems, including toggle switches and self-activators, to select systems matching another requirement: the system has a clear threshold between the two energy states. Their bistability was verified and characterized. At the same time, I attempted to figure out the third requirement for SR – an effective noise serving as the stochastic force – through one of the most widespread toggles, the mutual inhibition toggle, in both yeast and E. coli. A mathematic model for SR was written and adjusted. Secondly, I began work on designing a new genetic system capable of responding to pulsed magnetic fields. The operators responding to pulsed magnetic stimuli in the rpoH promoter were extracted and reorganized. Different versions of the rpoH promoter were generated and tested, and their varying responsiveness to magnetic fields was recorded. In order to improve efficiency and produce better operators, a directed evolution method was applied with the help of a CRISPR-dCas9 nicking system. The best performing promoters thus far show a five-fold difference in gene expression between trials with and without the magnetic field.
Dissertation/Thesis
Masters Thesis Bioengineering 2016

Synthetic biology combines biological parts from different sources in order to engineer non-native, functional systems. While there is a lot of potential for synthetic biology to revolutionize processes, such as the production of pharmaceuticals, engineering synthetic systems has been challenging. It is oftentimes necessary to explore a large design space to balance the levels of interacting components in the circuit. There are also times where it is desirable to incorporate enzymes that have non-biological functions into a synthetic circuit. Tuning the levels of different components, however, is often restricted to a fixed operating point, and this makes synthetic systems sensitive to changes in the environment. Natural systems are able to respond dynamically to a changing environment by obtaining information relevant to the function of the circuit. This work addresses these problems by establishing frameworks and mechanisms that allow synthetic circuits to communicate with the environment, maintain fixed ratios between components, and potentially add new parts that are outside the realm of current biological function. These frameworks provide a way for synthetic circuits to behave more like natural circuits by enabling a dynamic response, and provide a systematic and rational way to search design space to an experimentally tractable size where likely solutions exist. We hope that the contributions described below will aid in allowing synthetic biology to realize its potential.

Synthetic biology may be defined as an attempt at using engineering principles to design and build novel biological functionalities. An important class of such functionalities involves the bottom up design of genetic networks (or 'circuits') to control cellular behavior. Performing design iterations on these circuits in vivo is often a time consuming process. One approach that has been developed to address these long design times is to use E. coli cell extracts as simplified circuit prototyping environments. The analogy with similar approaches in engineering, such as prototyping using wind tunnels and breadboards, may be extended by developing accompanying computer aided design tools. In this thesis, we discuss the development of computational and mathematical tools to accelerate circuit prototyping in the TX-TL cell free prototyping platform, and demonstrate some applications of these tools.

We start by discussing the problem of reducing circuit behavior variability between different batches of TX-TL cell extracts. To this end, we demonstrate a model-based methodology for calibrating extract batches, and for using the calibrations to 'correct' the behavior of genetic circuits between batches. We also look at the interaction of this methodology with the phenomenon of parameter non-identifiability, which occurs when the parameter identification inverse problem has multiple solutions. In particular, we derive conditions under which parameter non-identifiability does not hinder our modeling objectives, and subsequently demonstrate the use of such non-identifiable models in performing data variability reduction.

Next, we describe txtlsim, a MATLAB Simbiology based toolbox for automatically generating models of genetic circuits in TX-TL, and for using these models for part characterization and circuit behavior prediction. Large genetic circuits can have non-negligible resource usage needs, leading to unintended interactions between circuit nodes arising due to the loading of cellular machinery, transcription factors or other regulatory elements. The usage of consumable resources like nucleotides and amino acids can also have non-trivial effects on complex genetic circuits. These types of effects are handled by the modeling framework of txtlsim in a natural way.

We also highlight mcmc-simbio, a smaller toolbox within txtlsim for performing concurrent Bayesian parameter inference on Simbiology models. Concurrent inference here means that a common set of parameters can be identified using data from an ensemble of different circuits and experiments, with each experiment informing a subset of the parameters. The combination of the concurrence feature with the fact that Markov chain Monte Carlo based Bayesian inference methods allow for the direct visualization of parameter non-identifiability enables the design of ensembles of experiments that reduce such non-identifiability.

Finally, we end with a method for performing model order reduction on transcription and translation elongation models while maintaining the ability of these models to track resource consumption. We show that due to their network topology, our models cannot be brought into the two-timescale form of singular perturbation theory when written in species concentration coordinates. We identify a coordinate system in which singular perturbation theory may be applied to chemical reaction networks more naturally, and use this to achieve the desired model reduction.

Driven by the development of new technologies and an ever expanding knowledge base of molecular and cellular function, Biology is rapidly gaining the potential to develop into a veritable engineering discipline - the so-called `era of synthetic biology' is upon us. Designing biological systems is advantageous because the engineer can leverage existing capacity for self-replication, elaborate chemistry, and dynamic information processing. On the other hand these functions are complex, highly intertwined, and in most cases, remain incompletely understood. Brazenly designing within these systems, despite large gaps in understanding, engenders understanding because the design process itself highlights gaps and discredits false assumptions.

Here we cover results from design projects that span several scales of complexity. First we describe the adaptation and experimental validation of protein functional assays on minute amounts of material. This work enables the application of cell-free protein expression tools in a high-throughput protein engineering pipeline, dramatically increasing turnaround time and reducing costs. The parts production pipeline can provide new building blocks for synthetic biology efforts with unprecedented speed. Tools to streamline the transition from the in vitro pipeline to conventional cloning were also developed. Next we detail an effort to expand the scope of a cysteine reactivity assay for generating information-rich datasets on protein stability and unfolding kinetics. We go on to demonstrate how the degree of site-specific local unfolding can also be determined by this method. This knowledge will be critical to understanding how proteins behave in the cellular context, particularly with regards to covalent modification reactions. Finally, we present results from an effort to engineer bacterial cell suicide in a population-dependent manner, and show how an underappreciated facet of plasmid physiology can produce complex oscillatory dynamics. This work is a prime example of engineering towards understanding.

Dissertation

abstract: Gene circuit engineering facilitates the discovery and understanding of fundamental biology and has been widely used in various biological applications. In synthetic biology, gene circuits are often constructed by two main strategies: either monocistronic or polycistronic constructions. The Latter architecture can be commonly found in prokaryotes, eukaryotes, and viruses and has been largely applied in gene circuit engineering. In this work, the effect of adjacent genes and noncoding regions are systematically investigated through the construction of batteries of gene circuits in diverse scenarios. Data-driven analysis yields a protein expression metric that strongly correlates with the features of adjacent transcriptional regions (ATRs). This novel mathematical tool helps the guide for circuit construction and has the implication for the design of synthetic ATRs to tune gene expression, illustrating its potential to facilitate engineering complex gene networks. The ability to tune RNA dynamics is greatly needed for biotech applications, including therapeutics and diagnostics. Diverse methods have been developed to tune gene expression through transcriptional or translational manipulation. Control of RNA stability/degradation is often overlooked and can be the lightweight alternative to regulate protein yields. To further extend the utility of engineered ATRs to regulate gene expression, a library of RNA modules named degradation-tuning RNAs (dtRNAs) are designed with the ability to form specific 5’ secondary structures prior to RBS. These modules can modulate transcript stability while having a minimal interference on translation initiation. Optimization of their functional structural features enables gene expression level to be tuned over a wide dynamic range. These engineered dtRNAs are capable of regulating gene circuit dynamics as well as noncoding RNA levels and can be further expanded into cell-free system for gene expression control in vitro. Finally, integrating dtRNA with synthetic toehold sensor enables improved paper-based viral diagnostics, illustrating the potential of using synthetic dtRNAs for biomedical applications.
Dissertation/Thesis
Doctoral Dissertation Biomedical Engineering 2020

abstract: Synthetic gene networks have evolved from simple proof-of-concept circuits to complex therapy-oriented networks over the past fifteen years. This advancement has greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a mechanism that cells use to achieve a discrete number of mutually exclusive states in response to environmental inputs. However, complex contextual connections of gene regulatory networks in natural settings often impede the experimental establishment of the function and dynamics of each specific gene network. In this work, diverse synthetic gene networks are rationally designed and constructed using well-characterized biological components to approach the cell fate determination and state transition dynamics in multistable systems. Results show that unimodality and bimodality and trimodality can be achieved through manipulation of the signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to communicate with each other. Moreover, a synthetic quadrastable circuit is also built and experimentally demonstrated to have four stable steady states. Experiments, guided by mathematical modeling predictions, reveal that sequential inductions generate distinct cell fates by changing the landscape in sequence and hence navigating cells to different final states. Circuit function depends on the specific protein expression levels in the circuit. We then establish a protein expression predictor taking into account adjacent transcriptional regions’ features through construction of ~120 synthetic gene circuits (operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks. These combined results illustrate applications of synthetic gene networks to understand the cell fate determination and state transition dynamics in multistable systems. A protein-expression predictor is also developed to evaluate and tune circuit dynamics.
Dissertation/Thesis
Doctoral Dissertation Biomedical Engineering 2017