Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery

Optimization Alternatives for Robust Model-based Design of Synthetic Biological Circuits

[EN] Synthetic biology is reaching the situation where tuning devices by hand is no longer possible due to the complexity of the biological circuits being designed. Thus, mathematical models need to be used in order, not only to predict the behavior of the designed synthetic devices; but to help on the selection of the biological parts, i.e., guidelines for the experimental implementation. However, since uncertainties are inherent to biology, the desired dynamics for the circuit usually requires a trade-off among several goals. Hence, a multi-objective optimization design (MOOD) naturally arises to get a suitable parametrization (or range) of the required kinetic parameters to build a biological device with some desired properties. Biologists have classically addressed this problem by evaluating a set of random Monte Carlo simulations with parameters between an operation range. In this paper, We propose solving the MOOD by means of dynamic programming using both a global multi-objective evolutionary algorithm (MOLA) and a local gradient-based nonlinear programming (NLP) solver. The performance of both alternatives is then checked in the design of a well-known biological circuit: a genetic incoherent feed-forward loop showing adaptive behavior. (C) 2016, IFAC (International Federation of Antomatic Control) Hosting by Elsevier Ltd. All rights reserved.The research leading to these results has received funding from the European Union (FP7/2007-2013 under grant agreement no604068), the Spanish Government (FEDER-CICYT DPI2011-524 28112-C04-01, DPI2014-55276-C5-1-R, DPI2015-70975-P) and the National Council of Scientific and Technologic Development of Brazil (BJT-304804/2014-2). Yadira Boada thanks also grant FPI/2013-3242 of the Universitat Politecnica de ValenciaBoada-Acosta, YF.; Pitarch Pérez, JL.; Vignoni, A.; Reynoso Meza, G.; Picó, J. (2016). Optimization Alternatives for Robust Model-based Design of Synthetic Biological Circuits. IFAC-PapersOnLine. 49(7):821-826. https://doi.org/10.1016/j.ifacol.2016.07.291S82182649

Robustness in spatially driven bistability in signaling systems

Biological systems are spatially organized. This microscopic heterogeneity has been shown to produce emergent complex behaviors such as bistability. Even though the connection between spatiality and dynamic response is essential to understand biological output, its robustness and extent has not been sufficiently explored. This work focuses on a previously described system which is composed of two monostable modules acting on different cellular compartments and sharing species through linear shuttling reactions. One of the two main purposes of this paper is to quantify the frequency of occurrence of bistability throughout the parameter space and to identify which parameters and in which value ranges control the emergence and the properties of bistability. We found that a very small fraction of the sampled parameter space produced a bistable response. Most importantly, shuttling parameters were among the most influential ones to control this property. The other goal of this paper is to simplify the same system as much as possible without losing compartment-induced bistability. This procedure provided a simplified model that still connects two monostable systems by a reduced set of linear shuttling reactions that circulates all the species around the two compartments. Bistable systems are one of the main building blocks of more complex behaviors such as oscillations, memory, and digitalization. Therefore, we expect that the proposed minimal system provides insight into how these behaviors can arise from compartmentalization.Fil: Tenenbaum, Debora. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Brandeis University; Estados UnidosFil: Marrone, Juan Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Grecco, Hernan Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Ventura, Alejandra. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

in vitro Characterisation of the Complement Cascade for Predicting Patient Outcome Post-operatively

The identification of surgical patients at higher risk of infection enables targeted allocation of critical care resources to improve patient mortality. The Complement cascade of the innate immune system is known to increase risk of infection if compromised and can be tested in vitro as a potential method for stratification of high-risk patients. Existing assays of Complement function are laboratory bound and require trained personnel to operate and interpret. This thesis describes the development of novel immunoassays for C3, C5a, TCC and TNFα, based on a multiplex biosensor platform with a duty cycle of 0.05) from the serum data of 22 volunteers. The model and cohort data provide an initial estimate of effect size for future clinical studies investigating the ability of these Complement activation phenotypes to identify high-risk surgical patients or identify the onset of infection

Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization

[EN] Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological coin portents. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. (C) 2016 IFAC (International Federation Of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1). Y.B. thanks grant FP/2013-3242 of Universitat Politecnica de Valencia and Becas Iberoamerica of Santander Group, Spain 2015. G.R.M. thanks the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. We are grateful to Dr. C,Bauerl and Dr, D. Provencio at the SB2CLab for their help in plasmid construction and getting experimental data. Also to Dr. V. Monedero at IATACSIC for allowing us to use the POLARstar plate reader at his lab,Boada-Acosta, YF.; Vignoni, A.; Reynoso Meza, G.; Picó, J. (2016). Parameter Identification in Synthetic Biological Circuits Using Multi-Objective Optimization. IFAC-PapersOnLine. 49(26):77-82. https://doi.org/10.1016/j.ifacol.2016.12.106S7782492

A system dynamics model to predict the human monocyte response to endotoxins

System dynamics is a powerful tool that allows modeling of complex and highly networked systems such as those found in the human immune system. We have developed a model that reproduces how the exposure of human monocytes to lipopolysaccharides (LPSs) induces an inflammatory state characterized by high production of tumor necrosis factor alpha (TNFα), which is rapidly modulated to enter into a tolerant state, known as endotoxin tolerance (ET). The model contains two subsystems with a total of six states, seven flows, two auxiliary variables, and 14 parameters that interact through six differential and nine algebraic equations. The parameters were estimated and optimized to obtain a model that fits the experimental data obtained from human monocytes treated with various LPS doses. In contrast to publications on other animal models, stimulation of human monocytes with super-low-dose LPSs did not alter the response to a second LPSs challenge, neither inducing ET, nor enhancing the inflammatory response. Moreover, the model confirms the low production of TNFα and increased levels of C-C motif ligand 2 when monocytes exhibit a tolerant state similar to that of patients with sepsis. At present, the model can help us better understand the ET response and might offer new insights on sepsis diagnostics and prognosis by examining the monocyte response to endotoxins in patients with sepsisThis work was supported by grants from the “Instituto de Salud Carlos III” (ISCiii), “Fondo de Investigación Sanitaria” (FIS), and Fondos FEDER (PI14/01234, PIE15/00065) to EL-C. EA work contract is supported by the Torres Quevedo program from “Ministerio de Economía y Competitividad” (SPTQ1300X006175XV0). VT work contract is supported by the “Ministerio de Economía y Competitividad” (PTA2013-8265-I

Immunobiochemical Reconstruction of Influenza Lung Infection—Melanoma Skin Cancer Interactions

It was recently reported that acute influenza infection of the lung promoted distal melanoma growth in the dermis of mice. Melanoma-specific CD8+ T cells were shunted to the lung in the presence of the infection, where they expressed high levels of inflammation-induced cell-activation blocker PD-1, and became incapable of migrating back to the tumor site. At the same time, co-infection virus-specific CD8+ T cells remained functional while the infection was cleared. It was also unexpectedly found that PD-1 blockade immunotherapy reversed this effect. Here, we proceed to ground the experimental observations in a mechanistic immunobiochemical model that incorporates T cell pathways that control PD-1 expression. A core component of our model is a kinetic motif, which we call a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), and which reflects known interactions between IRF4, Blimp-1, and Bcl-6. The different activity levels of the PD-1 DIFFL components, as a function of the cognate antigen levels and the given inflammation context, manifest themselves in phenotypically distinct outcomes. Collectively, the model allowed us to put forward a few working hypotheses as follows: (i) the melanoma-specific CD8+ T cells re-circulating with the blood flow enter the lung where they express high levels of inflammation-induced cell-activation blocker PD-1 in the presence of infection; (ii) when PD-1 receptors interact with abundant PD-L1, constitutively expressed in the lung, T cells loose motility; (iii) at the same time, virus-specific cells adapt to strong stimulation by their cognate antigen by lowering the transiently-elevated expression of PD-1, remaining functional and mobile in the inflamed lung, while the infection is cleared. The role that T cell receptor (TCR) activation and feedback loops play in the underlying processes are also highlighted and discussed. We hope that the results reported in our study could potentially contribute to the advancement of immunological approaches to cancer treatment and, as well, to a better understanding of a broader complexity of fundamental interactions between pathogens and tumors

Genome-wide Predictive Simulation on the Effect of Perturbation and the Cause of Phenotypic variations with Network Biology Approach

Thanks to modern high-throughput technologies such as microarray-based gene expression profiling, a large amount of molecular profile data have been generated in several disease related contexts. Despite the fact that these data likely contain systems-level information about disease regulation, revealing the underlying dynamics between genes and mechanisms of gene regulation in genome wide way remains a major challenge. Understanding these mechanisms in genome-wide fashion and the resulting dynamical behavior is a key goal of the nascent field of systems biology. One approach to dissect the logic of the cell, is to use reverse engineering algorithms that infer regulatory interactions form molecular profile data. In this context, use of information theoretic approaches has been very successful: for instance, the ARACNe algorithm has been able to successfully infer transcriptional interactions between transcription factors and their target genes; similarly, the MINDy algorithm has identified post-translational modulators of transcription factor activity by multivariate analysis of large gene expression profile datasets. Many methods have been proposed to improve ARACNe both from a computational efficiency perspective and in terms of increasing the accuracy of the predicted interactions. Yet, the main core of ARACNe, i.e., the data processing inequality (DPI), has remained virtually unaffected even though modern information theory has extended the DPI theorem into higher-order interactions. First, we introduce an improvement of ARACNe, hARACNe, which recursively applies a higher-order DPI analysis. We show that the new algorithm successfully detects false positive feed-forward loops involving more than three genes. Second, we extend the MINDy algorithm using co-information as a novel metric, thus replacing the conditional mutual information and significantly improving the algorithm"™s predictions. Largely, two ultimate goals of systems perturbation studies are to reveal how human diseases are connected with the genes, and to find regulatory mechanism that determine disease cell behavior. However, these goals remain daunting: even the most talented researchers still have to rely on laborious genetic screens and very simplified hypotheses about effects of given perturbation have been experimentally validated and roughly analyzed with very limited regulatory sub-network such as pathway. To overcome these limitations, use of gene regulatory network is explored in this thesis research. Specifically, we propose creation of a new algorithm that can accurately predict cell state in genome-wide fashion following perturbation of individual genes, such as from silencing or ectopic expression experiments. Furthermore, experimentally validated methods to predict genome-wide changes in a cellular system following a genetic perturbation (e.g., gene silencing or ectopic expression) are still unavailable, and even though phenotypic variations are experimentally profiled and gene signatures are selected by being statistically tested, finding the exact regulator which systematically causes significant variations of gene signature is still quite challenging. In this research, I introduce and experimentally validate a probabilistic Bayesian method to simulate the propagation of genetic perturbations on integrated gene regulatory networks inferred by the hARACNe and coMINDy algorithms from human B cell data. With the same predictive framework, we also computationally predict the master driver (regulator) that is most likely to have produced the observed variations in gene expression levels; these studies as a systematized pre-screening process before genetic manipulation. I predict in silico the effect of silencing of several genes as well as the cause of phenotypic variations. Performance analysis, tested by Gene Set Enrichment Analysis (GSEA), shows that the new methods are highly predictive, thus providing an initial step toward building predictive probabilistic regulatory models, which may be applicable as pre-screening steps in perturbation studies

A systems engineering approach to model, tune and test synthetic gene circuits

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 comprLa 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]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/112725TESI