Academic literature on the topic 'Synthetic gene circuits'

Synthetic gene circuits allow us to govern cell behavior in a programmable manner, which is central to almost any application aiming to harness engineered living cells for user-defined tasks. Transcription factors (TFs) constitute the ‘classic’ tool for synthetic circuit construction but some of their inherent constraints, such as insufficient modularity, orthogonality and programmability, limit progress in such forward-engineering endeavors. Here we review how CRISPR (clustered regularly interspaced short palindromic repeats) technology offers new and powerful possibilities for synthetic circuit design. CRISPR systems offer superior characteristics over TFs in many aspects relevant to a modular, predictable and standardized circuit design. Thus, the choice of CRISPR technology as a framework for synthetic circuit design constitutes a valid alternative to complement or replace TFs in synthetic circuits and promises the realization of more ambitious designs.

Deoxyribonucleic acid (DNA), a genetic material, encodes all living information and living characteristics, e.g., in cell, DNA signaling circuits control the transcription activities of specific genes. In recent years, various DNA circuits have been developed to implement a wide range of signaling and for regulating gene network functions. In particular, a synthetic DNA circuit, with a programmable design and easy construction, has become a crucial method through which to simulate and regulate DNA signaling networks. Importantly, the construction of a hierarchical DNA circuit provides a useful tool for regulating gene networks and for processing molecular information. Moreover, via their robust and modular properties, DNA circuits can amplify weak signals and establish programmable cascade systems, which are particularly suitable for the applications of biosensing and detecting. Furthermore, a biological enzyme can also be used to provide diverse circuit regulation elements. Currently, studies regarding the mechanisms and applications of synthetic DNA circuit are important for the establishment of more advanced artificial gene regulation systems and intelligent molecular sensing tools. We therefore summarize recent relevant research progress, contributing to the development of nanotechnology-based synthetic DNA circuits. By summarizing the current highlights and the development of synthetic DNA circuits, this paper provides additional insights for future DNA circuit development and provides a foundation for the construction of more advanced DNA circuits.

A major goal of synthetic biology is to develop a deeper understanding of biological design principles from the bottom up, by building circuits and studying their behavior in cells. Investigators initially sought to design circuits “from scratch” that functioned as independently as possible from the underlying cellular system. More recently, researchers have begun to develop a new generation of synthetic circuits that integrate more closely with endogenous cellular processes. These approaches are providing fundamental insights into the regulatory architecture, dynamics, and evolution of genetic circuits and enabling new levels of control across diverse biological systems.

Eukaryotic genes are regulated by multivalent transcription factor complexes. Through cooperative self-assembly, these complexes perform nonlinear regulatory operations involved in cellular decision-making and signal processing. In this study, we apply this design principle to synthetic networks, testing whether engineered cooperative assemblies can program nonlinear gene circuit behavior in yeast. Using a model-guided approach, we show that specifying the strength and number of assembly subunits enables predictive tuning between linear and nonlinear regulatory responses for single- and multi-input circuits. We demonstrate that assemblies can be adjusted to control circuit dynamics. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Programmable cooperative assembly provides a versatile way to tune the nonlinearity of network connections, markedly expanding the engineerable behaviors available to synthetic 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.

AbstractSynthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. Here, we present a computational framework to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a NonLinear Mixed-Effect (NLME) framework into an existing algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.

It is currently thought that glucocorticosteriods have a direct effect on the transcription of the alpha, beta and gamma fibrinogen genes. However, our studies indicate that while corticosteriods play a role in fibrinogen production, this role is not due to transcriptional activation via glucocorticosteriod receptors. In initial experiments, we compared the levels of fibrinogen mRNA in hepatocytes isolated from hypophysectomized rats to those from control animals. The levels of mRNA in hypophysectomized rats, which produce little ACTH or corticosteriods, were significantly higher than the levels in control animals. Albumin mRNA levels were unaffected by hypophysectomy. These results are in opposition to those which we had anticipated. Based on previously published data, we had thought that physiologic deprivation of corticosteriods would lead to decreased levels of fibrinogen. We propose that these results are related to the negative feedback that corticosteroids have on Hepatocyte Stimulating Factor (HSF) production through a tightly controlled feedback circuit. To investigate the role of corticosteriods in fibrinogen gene regulation, we have conducted experiments with primary hepatocytes in culture and rat FAZA cells (continuous hepatoma cell line). There is a 4 to 5 fold increase in fibrinogen production when these cells are treated with HSF but no change when these cells are treated with dexamethasone alone. However, there is a marked additional increase in the production of fibrinogen with the combination of dexamethasone and HSF. Data gathered through kinetic analysis of this synergistic interaction suggest that the maximum response to HSF requires another gene product whose production is responsive to dexamethasone. Detailed analysis of the rate of transcription of thegamma fibrinogen gene, its processing and mRNA turnover suggests a specific role for this gene product in regulating fibrinogen synthesis. Characterization of this gene product will lead to greater understanding of the regulation of the Acute Phase Reactants.

Introduction: The identification of where the different types of information are stored was one of the first questions asked about the memory neurobiology. The researchers wanted to know if there would be a large “memory center” or if there were multiple locations in the brain responsible for its storage. Therefore, a bibliographical research was carried out for the scientific knowledge of the theme. Methodology: Is a bibliographic study, carried out through a literary survey in the Google Scholar and SciELO databases, in addition to neuroscience textbooks. Results: The behavior observed in the learning process of the aplysya slug at the cellular and molecular level was developed from an experimental system made by Eric Kandel, austrian physician and neuroscientist. To try to explain these behavioral phenomena, Kandel sought to understand the functioning of the sinatic phenomena, studying the sinaptic transmission between neurons at the time the reflexes occur. Thus, it determined the molecular and cellular mechanisms of synaptic plasticity of aplysia, similar to vertebrate systems. Conclusions: In view of the clarifications of the components involved in the neural circuits, two stages that participate in the learning and memory process are considered: the first would be the acquisition of a short-lived memory, resulting from a transient reinforcement of the synapses, due to the modification of preexisting proteins. And the second consolidation, characterized by a persistent reinforcement of synapses, due to changes in gene expression, followed by protein synthesis, resulting in new synaptic connections.

The aim of this study was to carry out a global functional genomics analysis of plant cell transformation by Agrobacterium in order to define and characterize the physiology of Agrobacterium in the acidic environment of a wounded plant. We planed to study the proteome and transcriptome of Agrobacterium in response to a change in pH, from 7.2 to 5.5 and identify genes and circuits directly involved in this change. Bacteria-plant interactions involve a large number of global regulatory systems, which are essential for protection against new stressful conditions. The interaction of bacteria with their hosts has been previously studied by genetic-physiological methods. We wanted to make use of the new capabilities to study these interactions on a global scale, using transcription analysis (transcriptomics, microarrays) and proteomics (2D gel electrophoresis and mass spectrometry). The results provided extensive data on the functional genomics under conditions that partially mimic plant infection and – in addition - revealed some surprising and significant data. Thus, we identified the genes whose expression is modulated when Agrobacterium is grown under the acidic conditions found in the rhizosphere (pH 5.5), an essential environmental factor in Agrobacterium – plant interactions essential for induction of the virulence program by plant signal molecules. Among the 45 genes whose expression was significantly elevated, of special interest is the two-component chromosomally encoded system, ChvG/I which is involved in regulating acid inducible genes. A second exciting system under acid and ChvG/Icontrol is a secretion system for proteins, T6SS, encoded by 14 genes which appears to be important for Rhizobium leguminosarum nodule formation and nitrogen fixation and for virulence of Agrobacterium. The proteome analysis revealed that gamma aminobutyric acid (GABA), a metabolite secreted by wounded plants, induces the synthesis of an Agrobacterium lactonase which degrades the quorum sensing signal, N-acyl homoserine lactone (AHL), resulting in attenuation of virulence. In addition, through a transcriptomic analysis of Agrobacterium growing at the pH of the rhizosphere (pH=5.5), we demonstrated that salicylic acid (SA) a well-studied plant signal molecule important in plant defense, attenuates Agrobacterium virulence in two distinct ways - by down regulating the synthesis of the virulence (vir) genes required for the processing and transfer of the T-DNA and by inducing the same lactonase, which in turn degrades the AHL. Thus, GABA and SA with different molecular structures, induce the expression of these same genes. The identification of genes whose expression is modulated by conditions that mimic plant infection, as well as the identification of regulatory molecules that help control the early stages of infection, advance our understanding of this complex bacterial-plant interaction and has immediate potential applications to modify it. We expect that the data generated by our research will be used to develop novel strategies for the control of crown gall disease. Moreover, these results will also provide the basis for future biotechnological approaches that will use genetic manipulations to improve bacterial-plant interactions, leading to more efficient DNA transfer to recalcitrant plants and robust symbiosis. These advances will, in turn, contribute to plant protection by introducing genes for resistance against other bacteria, pests and environmental stress.