Model-based design of experiments in the presence of structural model uncertainty: an extended information matrix approach

The identification of a parametric model, once a suitable model structure is proposed, requires the estimation of its non-measurable parameters. Model-based design of experiment (MBDoE) methods have been proposed in the literature for maximising the collection of information whenever there is a limited amount of resources available for conducting the experiments. Conventional MBDoE methods do not take into account the structural uncertainty on the model equations and this may lead to a substantial miscalculation of the information in the experimental design stage. In this work, an extended formulation of the Fisher information matrix is proposed as a metric of information accounting for model misspecification. The properties of the extended Fisher information matrix are presented and discussed with the support of two simulated case studies

Robust Model Selection: Flatness-Based Optimal Experimental Design for a Biocatalytic Reaction

Considering the competitive and strongly regulated pharmaceutical industry, mathematical modeling and process systems engineering might be useful tools for implementing quality by design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However, a crucial task in modeling (bio)pharmaceutical manufacturing processes is the reliable identification of model candidates from a set of various model hypotheses. To identify the best experimental design suitable for a reliable model selection and system identification is challenging for nonlinear (bio)pharmaceutical process models in general. This paper is the first to exploit differential flatness for model selection problems under uncertainty, and thus translates the model selection problem to advanced concepts of systems theory and controllability aspects, respectively. Here, the optimal controls for improved model selection trajectories are expressed analytically with low computational costs. We further demonstrate the impact of parameter uncertainties on the differential flatness-based method and provide an effective robustification strategy with the point estimate method for uncertainty quantification. In a simulation study, we consider a biocatalytic reaction step simulating the carboligation of aldehydes, where we successfully derive optimal controls for improved model selection trajectories under uncertainty

Development and Application of Fluxomics Tools for Analyzing Metabolisms in Non-Model Microorganisms

Decoding microbial metabolism is of great importance in revealing the mechanisms governing the physiology of microbes and rewiring the cellular functions in metabolic engineering. Complementing the genomics, transcriptomics, proteinomics and metabolomics analysis of microbial metabolism, fluxomics tools can measure and simulate the in vivo enzymatic reactions as direct readouts of microbial metabolism. This dissertation develops and applies broad-scope tools in metabolic flux analysis to investigate metabolic insights of non-model environmental microorganisms. 13C-based pathway analysis has been applied to analyze specific carbon metabolic routes by tracing and analyzing isotopomer labeling patterns of different metabolites after growing cells with 13C-labeled substrates. Novel pathways, including Re-type citrate synthase in tricarboxylic acid cycle and citramalate pathways as an alternate route for isoleucine biosynthesis, have been identified in Thermoanaerobacter X514 and other environmental microorganisms. Via the same approach, the utilizations of diverse carbon/nitrogen substrates and productions of hydrogen during mixotrophic metabolism in Cyanothece 51142 have been characterized, and the medium for a slow-growing bacterium, Dehalococcoides ethenogenes 195, has been optimized. In addition, 13C-based metabolic flux analysis has been developed to quantitatively profile flux distributions in central metabolisms in a green sulfur bacterium, Chlorobaculum tepidum, and thermophilic ethanol-producing Thermoanaerobacter X514. The impact of isotope discrimination on 13C-based metabolic flux analysis has also been estimated. A constraint-based flux analysis approach was newly developed to integrate the bioprocess model into genome-scale flux balance analysis to decipher the dynamic metabolisms of Shewanella oneidensis MR-1. The sub-optimal metabolism and the time-dependent metabolic fluxes were profiled in a genome-scale metabolic network. A web-based platform was constructed for high-throughput metabolic model drafting to bridge the gap between fast-paced genome-sequencing and slow-paced metabolic model reconstruction. The platform provides over 1,000 sequenced genomes for model drafting and diverse customized tools for model reconstruction. The in silico simulation of flux distributions in both metabolic steady state and dynamic state can be achieved via flux balance analysis and dynamic flux balance analysis embedded in this platform. Cutting-edge fluxomics tools for functional characterization and metabolic prediction continue to be developed in the future. Broad-scope systems biology tools with integration of transcriptomics, proteinomics and fluxomics can reveal cell-wide regulations and speed up the metabolic engineering of non-model microorganisms for diverse bioenergy and environmental applications

Computational design and designability of gene regulatory networks

Nuestro conocimiento de las interacciones moleculares nos ha conducido hoy hacia una perspectiva ingenieril, donde diseños e implementaciones de sistemas artificiales de regulación intentan proporcionar instrucciones fundamentales para la reprogramación celular. Nosotros aquí abordamos el diseño de redes de genes como una forma de profundizar en la comprensión de las regulaciones naturales. También abordamos el problema de la diseñabilidad dada una genoteca de elementos compatibles. Con este fin, aplicamos métodos heuríticos de optimización que implementan rutinas para resolver problemas inversos, así como herramientas de análisis matemático para estudiar la dinámica de la expresión genética. Debido a que la ingeniería de redes de transcripción se ha basado principalmente en el ensamblaje de unos pocos elementos regulatorios usando principios de diseño racional, desarrollamos un marco de diseño computacional para explotar este enfoque. Modelos asociados a genotecas fueron examinados para descubrir el espacio genotípico asociado a un cierto fenotipo. Además, desarrollamos un procedimiento completamente automatizado para diseñar moleculas de ARN no codificante con capacidad regulatoria, basándonos en un modelo fisicoquímico y aprovechando la regulación alostérica. Los circuitos de ARN resultantes implementaban un mecanismo de control post-transcripcional para la expresión de proteínas que podía ser combinado con elementos transcripcionales. También aplicamos los métodos heurísticos para analizar la diseñabilidad de rutas metabólicas. Ciertamente, los métodos de diseño computacional pueden al mismo tiempo aprender de los mecanismos naturales con el fin de explotar sus principios fundamentales. Así, los estudios de estos sistemas nos permiten profundizar en la ingeniería genética. De relevancia, el control integral y las regulaciones incoherentes son estrategias generales que los organismos emplean y que aquí analizamos.Rodrigo Tarrega, G. (2011). Computational design and designability of gene regulatory networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1417

Flow cytometry in plant pathology: a case study on Pseudomonas cichorii

Flow cytometry (FCM) is a powerful and very versatile technique to measure cells in suspension. It is an indispensible method for routine diagnostics in the medical sector, but also for research purposes in very diverse fields of study. Despite its multiple and still increasing use in other sectors, FCM is scarcely used in plant pathology. In this thesis, we explored the possibilities of flow cytometry in plant pathology, focussing on viability and specific detection of the waterborne lettuce pathogen Pseudomonas cichorii. In a first phase we tested different fluorescent dyes and optimal instrument settings to stain, detect and count bacteria with FCM and determine their viability. In a next step, we wanted to develop a specific detection method for P. cichorii in irrigation water. As this pathogen can cause midrib rot on greenhouse-grown lettuce after a single overhead irrigation with water containing only 100 CFU ml-1, very sensitive detection was necessary. Moreover, P. cichorii is most found in rainwater, and this water often contains high bacterial backgrounds, as well as other organic and inorganic pollutants. Therefore we chose to develop a detection system based on immunomagnetic beads, which would allow specific capture and concentration of the target cells out of the water. We wanted to combine specific detection with viability assessment, in order to have a method that is also useful to research in vivo survival of P. cichorii and gain more insight into the epidemiology of the bacteria. The combination of immunomagnetic separation (IMS) and live/dead staining is not easy and has seldom been tried. We tested different bead systems and defined the most important factors influencing IMS and nonspecific staining. We obtained best results with the relatively large (2.6 µm) non-fluorescent Compel beads. After optimizing this bead system, we came to a method in which beads were identified based on scatter properties and bacteria based on fluorescence properties. Bead-bacteria complexes had both the large scatter of the beads and the high fluorescence of the live/dead stained bacteria. Combining those two conditions in a logical combination of gates allowed the exclusion of most noise and resulted in the sensitive enumeration of bead-bacteria complexes. This method was further evaluated on mixed cultures and larger volumes and finally tested on different irrigation waters from commercial lettuce greenhouses. Irrigation water proved to be a difficult and very variable matrix. Despite the extra sample pretreatment steps, we could not reliably detect P. cichorii cells below the infection threshold of 100 cells ml-1, except in one water type. The major problems we encountered were a too low recovery of P. cichorii, combined with a too high background remaining in the final samples. Besides the IMS method itself, of which the binding percentage and binding strength should be improved, both the bacteria and their matrix complicated detection. Their was a significant difference between some of the tested sampling dates and the analysis date had a significant effect on P. cichorii recovery: higher recovery was obtained in the same waters sampled in March, compared to the February samplings. Furthermore, recovery improved when a water sample was spiked and analysed after storage for at least a week. Also PCR recovery may be influenced by sampling date, but here recovery tended to be lower in spring samplings. The combination of low recovery and an unknown influence of water constitution on recovery, made that our IMS method is not (yet) suited as an alternative for the existing PCR detection of P. cichorii. However, when comparing the conventional real-time PCR detection of P. cichorii with our IMS-FCM method, or with IMS pretreatment followed by PCR analysis, conventional RT-PCR is by far the most expensive method. Not the PCR analysis itself, but the sample pretreatment and DNA extraction before PCR is laborious and has, besides very high labour cost, also high material costs. Although PCR will remain the most specific method, IMS and/or FCM could be brought to a comparable sensitivity and have the potential to become a more cost-effective alternative for sample pretreatment and/or PCR analysis. The fact that P. cichorii is a difficult bacterium to detect is not only due to the IMS/FCM methodology, its low infection threshold, or to the complexity of its natural environment. Also the extremely high sensitivity of these bacteria to mechanical stress complicated detection. Mechanical stress seems to induce rapid apoptosis and autolysis, making P. cichorii cells disappear for both FCM or PCR detection. Medium constitution, especially salt concentration and the presence of nutrients, has a big influence on survival. In the absence of H2O2 and presence of 1% LB, recovery percentages of more than 90% could be obtained, while in saline solution, less than 10% was recovered after centrifugation. Although the enigmatic behaviour of P. cichorii complicated our research, such a far-reaching effect of common lab practices on bacterial viability has never been reported before and may be of considerable importance for microbiological practices

Raman spectroscopy for point of care urinary tract infection diagnosis

Urinary tract infections (UTIs) are one of the most common bacterial infections experience by humans, with 150 million people suffering one or more UTIs each year. The massive scale at which UTIs occurs translates to a tremendous health burden comprising of patient morbidity and mortality, massive societal costs and a recognised contribution to expanding antimicrobial resistance. The considerable disease burden caused by UTIs is severely exacerbated by an outdated diagnostic paradigm characterised by inaccuracy and delay. Poor accuracy of screening tests, such as urinalysis, lead to misdiagnosis which in turn result in delayed recognition or overtreatment. Additionally, these screening tests fail to identify the causative pathogen, causing an overreliance on broad-spectrum antimicrobials which exacerbate burgeoning antimicrobial resistance. While diagnosis may be accurately confirmed though culture and sensitivity testing, the prolonged delay incurred negates the value of the information provided doing so. A novel diagnostic paradigm is required that that targets rapid and accurate diagnosis of UTIs, while providing real-time identification of the causative pathogen. Achieving this precision management is contingent on the development of novel diagnostic technologies that bring accurate diagnosis and pathogen classification to the point of care. The purpose of this thesis is to develop a technology that may form the core of a point-of-care diagnostic capable of delivering rapid and accurate pathogen identification direct from urine sample. Raman spectroscopy is identified as a technology with the potential to fulfil this role, primarily mediated though its ability to provide rapid biochemical phenotyping without requiring prior biomass expansion. Raman spectroscopy has demonstrated an ability to achieve pathogen classification through the analysis of inelastically scattered light arising from pathogens. The central challenge to developing a Raman-based diagnostic for UTIs is enhancing the weak bacterial Raman signal while limiting the substantial background noise. Developing a technology using Raman spectroscopy able to provide UTI diagnosis with uropathogen classification is contingent on developing a robust experimental methodology that harnesses the multitude of experimental and analytical parameters. The refined methodology is applied in a series of experimental works that demonstrate the unique Raman spectra of pathogens has the potential for accurate classification. Achieving this at a clinically relevant pathogen load and in a clinically relevant timeframe is, however, dependent on overcoming weak bacterial signal to improve signal-to-noise ratio. Surface-enhanced Raman spectroscopy (SERS) provides massive Raman signal enhancement of pathogens held in close apposition to noble metal nanostructures. Additionally, vacuum filtration is identified as a means of rapidly capturing pathogens directly from urine. SERS-active filters are developed by applying a gold nanolayer to commercially available membrane filters through physical vapour deposition. These SERS-active membrane filter perform multiple roles of capturing pathogens, separating them from urine, while providing Raman signal enhancement through SERS. The diagnostic and classification performance of SERS-active filters for UTIs is demonstrated to achieve rapid and accurate diagnosis of infected samples, with real-time uropathogen classification, using phantom urine samples, before piloting the technology using clinical urine samples. The Raman technology developed in this thesis will be further developed toward a clinically implementable technology capable of ameliorating the substantial burden of disease caused by UTIs.Open Acces

Metabolic profiling of environmental stress in Scleractinian corals

Ben Gordon studied the effects of environmental stress on the metabolome of reef-building corals. His research produced the first extraction and analysis protocol for coral metabolomics and identified machine learning methods to classify the functional state of corals under a variety of conditions. His work has provided novel biomarkers of coral health and directions for implementing metabolomics biomonitoring