Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria

<p>Abstract</p> <p>Background</p> <p>Fluorescent and luminescent reporter genes have become popular tools for the real-time monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data.</p> <p>Results</p> <p>We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in <it>Escherichia coli</it>. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the half-lives of reporter and host gene products. The results show that large differences in protein half-lives, more than mRNA half-lives, may be critical for the interpretation of reporter gene data in the analysis of the dynamics of regulatory systems.</p> <p>Conclusions</p> <p>The paper contributes to the development of sound methods for the interpretation of reporter gene data, notably in the context of the reconstruction and validation of models of regulatory networks. The results have wide applicability for the analysis of gene expression in bacteria and may be extended to higher organisms.</p

Characterization of Intrinsic Properties of Promoters.

Accurate characterization of promoter behavior is essential for the rational design of functional synthetic transcription networks such as logic gates and oscillators. However, transcription rates observed from promoters can vary significantly depending on the growth rate of host cells and the experimental and genetic contexts of the measurement. Furthermore, in vivo measurement methods must accommodate variation in translation, protein folding, and maturation rates of reporter proteins, as well as metabolic load. The external factors affecting transcription activity may be considered to be extrinsic, and the goal of characterization should be to obtain quantitative measures of the intrinsic characteristics of promoters. We have developed a promoter characterization method that is based on a mathematical model for cell growth and reporter gene expression and exploits multiple in vivo measurements to compensate for variation due to extrinsic factors. First, we used optical density and fluorescent reporter gene measurements to account for the effect of differing cell growth rates. Second, we compared the output of reporter genes to that of a control promoter using concurrent dual-channel fluorescence measurements. This allowed us to derive a quantitative promoter characteristic (ρ) that provides a robust measure of the intrinsic properties of a promoter, relative to the control. We imposed different extrinsic factors on growing cells, altering carbon source and adding bacteriostatic agents, and demonstrated that the use of ρ values reduced the fraction of variance due to extrinsic factors from 78% to less than 4%. This is a simple and reliable method to quantitatively describe promoter properties.TJR was supported by a Microsoft Research studentship and EC FP7 Project No. 612146 (PLASWIRES) awarded to JH, JRB by a Microsoft Research studentship and internship, and FF by CONICYT-PAI/Concurso Nacional de Apoyo al Retorno de Investigadores/as desde el Extranjero Folio 8213002 7, and EPSRC grant EP/H019162/1 awarded to JH. JWA acknowledges the EPSRC and the Wellcome Trust for support.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acssynbio.5b0011

Inference of the statistics of a modulated promoter process from population snapshot gene expression data

International audienc

On observability and reconstruction of promoter activity statistics from reporter protein mean and variance profiles

International audienceReporter protein systems are widely used in biology for the indirect quantitative monitoring of gene expression activity over time. Atthe level of population averages, the relationship between the observed reporter concentration profile and gene promoter activity is established,and effective methods have been introduced to reconstruct this information from the data. At single-cell level, the relationship between population distribution time profiles and the statistics of promoter activation is still not fully investigated, and adequate reconstruction methods are lacking.This paper develops new results for the reconstruction of promoter activity statistics from mean and variance profiles of a reporter protein. Based on stochastic modelling of gene expression dynamics, it discusses the observability of mean and autocovariance function of an arbitrary random binary promoter activity process. Mathematical relationships developed are explicit and nonparametric, i.e. free of a priori assumptions on the laws governing the promoter process, thus allowing for the decoupled analysis of the switching dynamics in a subsequent step. The results of this work constitute the essential tools for the development of promoter statistics and regulatory mechanism inference algorithms

Invalidation of the structure of genetic network dynamics: a geometric approach

International audienceThis work concerns the identification of the structure of a genetic network model from measurements of gene product concentrations and synthesis rates. In earlier work, we developed a data preprocessing algorithm that is able to reject many hypotheses on the network structure by testing certain monotonicity properties for a wide family of network models. Here, we develop a geometric interpretation of the method. Then, for a relevant subclass of genetic network models, we extend our approach to the combined testing of monotonicity and convexity-like properties associated with the network structures. The theoretical aspects and practical performance of the enhanced methods are illustrated by way of numerical results

Quantitative Modeling and Estimation in Systems Biology using Fluorescent Reporter Systems

Building quantitative models of biological systems is a challenging task as these models can consist of a very large number of components with complex interactions between them and the experimental data available for model validation is often sparse and noisy. The focus in this work is on modeling and parameter estimation of biological systems that are monitored using fluorescent reporter systems. Fluorescent reporter systems are widely used for various applications such as monitoring gene expression, protein localization and protein-protein interactions. This dissertation presents various techniques to facilitate modeling of biological systems containing fluorescent reporters with special attention given to challenges arising due to limited experimental data, simultaneous monitoring of multiple events and variability in the observed response due to phenotypic differences. First, an inverse problem is formulated to estimate the dynamics of transcription factors, a crucial molecule that initiates the transcription process, using data of fluorescence intensity profiles obtained from a fluorescent reporter system. The resulting inverse problem is ill-conditioned and it is solved with the aid of regularization techniques. The main contribution is that, with the presented technique, any complex dynamics of transcription factors can be estimated using limited data of fluorescence measurements. The technique has been evaluated using simulated data as well as experimental data of a GFP reporter system of STAT3. Second, an experimental design formulation is developed to facilitate the use of multiple fluorescent reporters, with overlapping emission spectra, in the same experiment. This work develops a criterion to select the fluorescent proteins for simultaneous use such that the accuracy in the estimated contributions of individual proteins to the overall observed intensity is maximized. This technique has been validated using mixtures of different E. coli strains which express different fluorescent proteins. Finally, a population balance model of a cell population containing a fluorescence reporter system is developed to describe the variability in the observed fluorescence in cells. Factors such as rate of fluorescent protein formation as well as partitioning of the fluorescent protein on cell division have been taken into account to describe the dynamics of fluorescence intensity distributions in the cell populations. The model has been used to obtain preliminary hypotheses to explain the difference in response of HeLa cells containing the Tet-on expression system on stimulation by different levels doxycycline. Thus, this work describes techniques for building robust predictive models of biological systems such as regularization for solving ill-posed estimation problems, experimental design techniques as well as using population balance modeling to model complex multi-scale dynamics. Moreover, while the examples discussed here are motivated for fluorescent reporter systems, the developed techniques can be used for different kinds of linear or non-linear dynamic biological systems

Computational Simulation of Gene Regulatory Networks Implementing an Extendable Synchronous Single-Input Delay Flip-Flop and State Machine

We present a detailed and extendable design of the first synchronous single-input delay flip-flop implemented as a gene regulatory network in Escherichia coli (E. coli). The device, which we call the BioD, has one data input (trans-acting RNA), one clock input (far-red light) and an output that reports the state of the device using green fluorescent protein (GFP). The proposed design builds on Gardner’s toggle switch, to provide a more sophisticated device that can be synchronized with other devices within or without the same cell, and which requires only one data input. We provide a mathematical model of the system and simulation results. The results show that the device behaves in line with desired functionality. Further, we discuss the constraints of the design, which pertain to ranges of parameter values. The BioD is extended via the addition of an update function and input and output interfaces. The result is the BioFSM, which constitutes a synchronous and modular finite state machine, which uses an update function to change its state, stored in the BioD. The BioFSM uses its input and output interfaces for inter-cellular communications. This opens the door to the design of a circular cellular automata (the BioCell), which is envisioned as a number of communicating E. coli colonies, each made of clones of one BioFSM

Molecular and Phenotypic Analysis of Salmonella Biofilm Formation: Exploring the Links Between Survival, Virulence, and Transmission

Pathogenic Salmonella strains are responsible for millions of human and livestock infections each year. The mechanisms of Salmonella pathogenesis are of great interest, along with the capacity of strains to survive in the environment and complete the transmission cycle. This survival is predicted to be related to a specific physiology called a biofilm. Biofilms are communities of cells within a self-produced extracellular matrix that are often associated with a physical surface. For Salmonella, the biofilm phenotype is activated by the transcriptional regulator CsgD and is associated with the production of an extracellular matrix consisting of protein polymers and exopolysaccharides. Salmonella biofilm formation is induced during growth at low temperatures and in conditions of nutrient limitation and low osmolarity. The biofilm phenotype is highly conserved across nontyphoidal Salmonella strains that briefly colonize the host and cause gastroenteritis. It is hypothesized that biofilm formation is important for increasing the transmission success of nontyphoidal Salmonella by enhancing their persistence in non-host environments. Salmonella biofilms have traditionally been studied as a population-level phenotype associated with colony formation, known as the red, dry, and rough (rdar) morphotype. However, Salmonella grown in liquid broth cultures under biofilm-inducing conditions form clonal subpopulations of multicellular aggregates and planktonic cells. This phenomenon is attributed to bistable expression of CsgD, where aggregated cells exist in a CsgD-ON state and planktonic cells are associated with a CsgD-OFF state. We performed comparative transcriptomic sequencing (RNA-seq), which revealed 1856 genes that were differentially expressed between these two S. Typhimurium cell subpopulations. Multicellular aggregates were associated with increased gene expression typical of Salmonella biofilm formation, including nutrient scavenging, reactive oxygen species defenses, and osmoprotection. In contrast, planktonic cells were associated with higher expression of multiple virulence pathways associated with the SPI-1 and SPI-2 type three secretion systems, cell motility, and chemotaxis. Increased synthesis of the SPI-1 type three secretion system in planktonic cells correlated with enhanced invasion of polarized Caco-2 human intestinal cells. We modified an existing Tn7-based transposition system to generate chromosomally marked strains of Salmonella to facilitate tracking of multicellular aggregates and planktonic cells in competitive fitness assays. Planktonic cells were associated with increased virulence in mice compared to multicellular aggregates. However, when these same cell subpopulations were exposed to desiccation, multicellular aggregates were associated with greater cell survival and the virulence advantage of planktonic cells was lost. We hypothesize that bistable CsgD expression and the generation of specialized cell types may represent a form of bet hedging, where planktonic cells are adapted for direct host-to-host transmission, and multicellular aggregates can survive long-term in the environment to cause infections later. This strategy would prepare nontyphoidal Salmonella for the unpredictable nature of the fecal-oral transmission process and improve their potential to cause future infections. Salmonella serovars that cause systemic disease within a restricted range of hosts have been shown to be biofilm-negative. In sub-Saharan Africa, a phylogenetically distinct group of nontyphoidal Salmonella has recently been identified for its role in an emerging epidemic of invasive extraintestinal infections. These invasive nontyphoidal Salmonella are associated with chronic persistence within the human host and do not have an identified environmental reservoir. We compared the biofilm phenotype of two invasive nontyphoidal Salmonella strains (S. Typhimurium D23580 and S. Enteritidis D7795) to a panel of strains consisting of ‘typical’ gastroenteritis-causing, nontyphoidal Salmonella and Salmonella strains that cause systemic typhoid fever. Both strains of invasive nontyphoidal Salmonella demonstrated an impaired biofilm phenotype, which we attributed to strain-specific genetic polymorphisms. We predict that the impaired biofilm phenotype of invasive nontyphoidal Salmonella correlates with their occupation of the systemic niche within the host and a reduced capacity to survive in the environment. My research has brought insight into how pathogenic Salmonella strains are able to navigate through unpredictable areas of their lifecycle and increased our understanding of their potential transmission mechanisms

Optimal Experimental Design Applied to Models of Microbial Gene Regulation

Microbial gene expression is a comparatively well understood process, but regulatory interactions between genes can give rise to complicated behaviours. Regulatory networks can exhibit strong context dependence, time-varying interactions and multiple equilibrium. The qualitative diagrammatic models often used in biology are not well suited to reasoning about such intricate dynamics. Fortunately, mathematics offers a natural language to model gene regulation because it can quantify the various system inter-dependencies with much greater clarity and precision. This added clarity makes models of microbial gene regulation a valuable tool for studying both natural and synthetic gene regulatory systems. However models are only as good as the knowledge and assumptions they are built on. Specifically, all models depend on unknown parameters -- constant that quantify specific rates and interaction strengths within the regulatory system. In systems biology parameters are generally fit, rather than measured directly, because their values are contextually dependent on state of the microbial host. This fitting requires collecting observations of the modeled system. Exactly what is measured, how many times and under what experimental conditions defines an experimental design. The experimental design is intimately linked to the accuracy of any resulting parameter estimates for a model, but determining what experimental design will be useful for fitting can be difficult. Optimal experimental design (OED) provides a set of statistical techniques that can be used make design choices that improve parameter estimation accuracy. In this thesis I examine the use of OED methods applied to models of microbial gene regulation. I have specifically focused on optimal design methods that combine asymptotic parametric accuracy objectives, based on the Fisher information matrix, with relaxed formulations of the design optimization problem. I have applied these OED methods to three biological case studies. (1) I have used these methods to implement a multiple-shooting optimal control algorithm for optimal design of dynamic experiments. This algorithm was applied to a novel model of transcriptional regulation that accounts for the microbial host's physiological context. Optimal experiments were derived for estimating sequence-specific regulatory parameters and host-specific physiological parameters. (2) I have used OED methods to formulate an optimal sample scheduling algorithm for dynamic induction experiments. This algorithm was applied to a model of an optogenetic induction system -- an important tool for dynamic gene expression studies. The value of sampling schedules within dynamic experiments was examined by comparing optimal and naive schedules. (3) I derived an optimal experimental procedure for fitting a steady-state model of single-cell observations from a bistable regulatory motif. This system included a stochastic model of gene expression and the OED methods made use of the linear noise approximation to derive a tractable design algorithm. In addition to these case-studies, I also introduce the NLOED software package. The package can perform optimal design and a number of other fitting and diagnostic procedures on both static and dynamic multi-input multi-output models. The package makes use automatic differentiation for efficient computation, offers a flexible modeling interface, and will make OED more accessible to the wider biological community. Overall, the main contributions of this thesis include: developing novel OED methods for a variety of gene regulatory scenarios, studying optimal experimental design properties for these scenarios, and implementing open-source numerical software for a variety of OED problems in systems biology

Dynamique de la réponse physiologique d'Escherichia coli à des perturbations maîtrisées de son environnement (vers le développement de nouveaux outils de changement d'échelle)

Les bioréacteurs de grandes dimensions, en raison de phénomènes de transfert limitant, sont le siège d hétérogénéités se traduisant par des gradients locaux de concentration et température. Les microorganismes circulant au sein de ces bioréacteurs subissent donc des fluctuations environnementales qui peuvent affecter leur comportement aux niveaux métaboliques et/ou moléculaires. La réponse microbienne est fonction de la nature, de l intensité, de la fréquence et de la durée de la perturbation. L objectif de ce travail est l étude quantitative de l impact de l intensité, la fréquence et l amplitude d un stress nutritionnel sur le comportement dynamique d Escherichia coli, à savoir des ajouts pulsés de glucose lors de cultures continues en régime permanent. Un effort particulier est consacré au développement et à la validation des outils expérimentaux indispensables pour une caractérisation rigoureuse des dynamiques de réponses transitoires sur des échelles de temps allant de secondes à quelques minutes. Pour permettre le suivi in situ et en temps réel des changements métaboliques et moléculaires, une souche bioluminescente est mise en œuvre. Les réponses transitoires sont caractérisées par les vitesses spécifiques, les rendements, les profils d induction transcriptionnelle, les temps caractéristiques. Selon les différents scenarii réalisés, l ajustement du métabolisme face aux hétérogénéités de substrat est quantifié selon des échelles de temps aux niveaux macroscopiques et/ou moléculaires ; ces résultats originaux contribuent ainsi à l implémentation des connaissances sur les interactions dynamiques entre les phénomènes biologiques et les phénomènes physiques ; l enjeu réside à terme en l amélioration des processus d optimisation et d extrapolation des bioprocédés par l identification et la quantification des dynamiques des phénomènes limitantsIneffective mixing entailing heterogeneity issues within industrial bioreactors have been reported to affect microbial metabolisms at cellular and/or molecular levels. Substrate gradients inside large-scale bioreactors are common environmental fluctuations that microorganisms would have to encouter along with the bioprocess. Depending on intensity, frequency and duration of those fluctuations, microorganisms may respond in a different manner. The objective of this work is to study the impact of intensity, frequency and amplitude of glucose perturbations on the dynamics of Escherichia coli responses. An E. coli bioluminescent strain is used for in situ and real-time monitoring of both metabolic and transcriptional changes. For this purpose, short-term glucose excess was simulated, using pulse-based experiments into glucose-limited chemostat cultures. In addition, an important effort is devoted to the development and validation of technical and mathematical tools in order to acquire quantitative and kinetic data on time scales from seconds to minutes. The transient responses are characterized, using specific rates, yields, transcriptional induction profiles and characteristic response times, and are compared in the different defined perturbation scenarios. The results reflected the fact that short-term heterogeneities of substrate affect both cell metabolism and regulation at macroscopic and/or molecular levels. Quantitative understandings of the dynamics during transient responses to environmental perturbations can thus shed light on the bioprocess optimizationTOULOUSE-INSA-Bib. electronique (315559905) / SudocSudocFranceF