Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Modeling of Tumor Growth and Optimization of Therapeutic Protocol Design

Ph.DDOCTOR OF PHILOSOPH

The metabolization of drugs as a factor in the development of adverse drug reactions

When medicines cause side effects, we often assume that the original active substance is responsible for this. However, after ingestion, the active substance is processed (metabolized) by enzymes or attached to polar compounds in order to accelerate its excretion from the body. The original active substance undergoes chemical changes and drug metabolites are formed. These drug metabolites have their own characteristics and can also cause side effects. Recognizing, predicting and preventing these side effects requires knowledge of the metabolism of the parent drug, the formation of metabolites and their effects in the body.In this thesis we investigated the knowledge about the role of drug metabolization and metabolites on side effects. Various data sources have been explored and used for this purpose. Phenoconversion has also been investigated as a method to intervene in drug metabolization and prevent the formation of unwanted metabolites.It appears that the role of drug metabolism and the formation of drug metabolites in side effects is well recognized, but the available information is not yet fully exploited. For example, knowledge about certain chemical structures in medicines can be included more often in the recognition and prediction of known and unknown side effects.Once the role of drug metabolism and the formation of the drug metabolites in the development of the side effect is recognized, action can be taken to prevent side effects. Phenoconversion appears to be a suitable method to prevent the formation of unwanted metabolites and could be investigated and possibly be applied more often

METABOLIC MODELING AND OMICS-INTEGRATIVE ANALYSIS OF SINGLE AND MULTI-ORGANISM SYSTEMS: DISCOVERY AND REDESIGN

Computations and modeling have emerged as indispensable tools that drive the process of understanding, discovery, and redesign of biological systems. With the accelerating pace of genome sequencing and annotation information generation, the development of computational pipelines for the rapid reconstruction of high-quality genome-scale metabolic networks has received significant attention. These models provide a rich tapestry for computational tools to quantitatively assess the metabolic phenotypes for various systems-level studies and to develop engineering interventions at the DNA, RNA, or enzymatic level by careful tuning in the biophysical modeling frameworks. in silico genome-scale metabolic modeling algorithms based on the concept of optimization, along with the incorporation of multi-level omics information, provides a diverse array of toolboxes for new discovery in the metabolism of living organisms (which includes single-cell microbes, plants, animals, and microbial ecosystems) and allows for the reprogramming of metabolism for desired output(s). Throughout my doctoral research, I used genome-scale metabolic models and omics-integrative analysis tools to study how microbes, plants, animal, and microbial ecosystems respond or adapt to diverse environmental cues, and how to leverage the knowledge gleaned from that to answer important biological questions. Each chapter in this dissertation will provide a detailed description of the methodology, results, and conclusions from one specific research project. The research works presented in this dissertation represent important foundational advance in Systems Biology and are crucial for sustainable development in food, pharmaceuticals and bioproduction of the future. Advisor: Rajib Sah

Development of computational tools for the analysis of 2D-nuclear magnetic resonance data

Dissertação de mestrado em BioinformaticsMetabolomics is one of the omics’ sciences that has been gaining a lot of interest due to its potential on correlating an organism’s biochemical activity and its phenotype. The applications of metabolomics are being extended as new techniques reveal new information on metabolic profiles and molecules, thus elucidating biological, chemical and functional knowledge. The main techniques that collect data are based on mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy. The last one has the advantage of analyzing a sample in vivo without damaging it and while its sensitivity is pointed out as a disadvantage, multidimensional NMR delivers a solution to this issue. It adds layers of information, generating new data that requires advanced bioinformatics methods in order to extract biological meaning. Since multidimensional NMR has different approaches within itself, the need to estab lish an integrated framework that allows a researcher to load its data and extract relevant knowledge has become more imperative over the years. Also, establishing common data analysis pipelines on one-dimensional and multidimensional NMR remains a challenge in current scientific research hindering reproducibility across research groups. In recent work from the host group, specmine, an R package for metabolomics and spectral data analysis/mining, has been developed to wrap and deliver key metabolomic methods that allow a researcher to perform a complete analysis. In this dissertation, tools integrated in specmine were developed to read, visualize and analyze two-dimensional (2D) NMR. A new specmine structure was created for this type of data, easing interpretation and data visualization. In terms of visualization a novel approach towards three-dimensional environments enables users to interact with their data allowing peak hovering or identification of rich resonance regions. The selection of which samples to plot, when the user does not specify an input, is based on a signal-to-noise ratio scale which plots samples with opposite signal-to-noise ratios. A method to perform peak detection on 2D NMR based on local maximum search was implemented to obtain a data structure that best benefits from specmine’s functionalities. These include preprocessing, univariate and multivariate analysis as well as machine learning and feature selection methods. The 2D NMR functions were validated using experimental data from two scientific papers, available on metabolomic databases and applying the necessary preprocessing steps to compare spectra and results. These data originated two case studies from different NMR sources, Bruker and Varian, which reinforces specmine’s flexibility. The case studies were carried out using mainly specmine and other packages for specific processing steps, such as, probabilistic quotient normalization. A pipeline to analyze 2D NMR was added to specmine, in a form of a vignette, to provide a guideline for the newly developed functionalities.A metabolómica é uma das ciências ómicas que tem vindo a ganhar muito interesse devido ao seu potencial para correlacionar a atividade bioquímica de um organismo com o seu fenótipo. As aplicações da metabolómica estão em constante crescimento à medida que novas técnicas revelam nova informação sobre perfis metabólicos e moleculares, elucidando conhecimento biológico, químico e funcional. As principais técnicas para recolher este tipo de dados são baseadas em espectrometria de massa e em ressonância magnética nuclear (RMN). Esta última tem a vantagem de analisar uma amostra in vivo sem a danificar e enquanto a sensibilidade da mesma tem sido apontada como uma desvantagem, surge a abordagem de RMN multidimensional melhorando a versão tradicional. Através da medição de outros núcleos adiciona camadas de informação, gerando um novo tipo de dados que requere métodos bioinformáticos avançados para se extrair significado biológico. A existência de várias abordagens para realizar RMN multidimensional leva à crescente necessidade da existência de uma ferramenta que integre este tipo de dados, de forma a permitir ao investigador executar a sua análise de forma eficaz. Adicionalmente, a consolidação de pipelines comuns para analisar dados de RMN uni- e multidimensional permanece um desafio a investigação científica, dificultando a reprodutibilidade de resultados por diferentes grupos de investigação. Em trabalhos recentes do grupo de acolhimento foi desenvolvido um package para o programa R focado na metabolómica e na análise/mineração de dados. Este package, specmine, tem sido melhorado desde o seu desenvolvimento funcionando como uma ferramenta que engloba diferentes métodos permitindo uma análise total a um determinado conjunto de dados. Baseado neste package, mais recentemente foi desenvolvida uma plataforma web integrada, WebSpecmine, com o mesmo propósito que providencia ao utilizador uma interface de utilizador mais fácil e amigável. Nesta dissertação, ferramentas que permitem a leitura, visualização e análise de NMR bidimensional (2D) foram desenvolvidas tendo em conta a sua integração no specmine. Uma nova estrutura foi adicionada ao package, facilitando a interpretação e esquemetazição dos dados. Quanto a visualização, uma abordagem inovadora para ambientes tridimensionais permite ao utilizador interagir com os seus dados através da identificação de regiões espectrais de interesse ou reconhecimento de picos. A visualização de espectros 2D, sem especificação por parte do utilizador, tem por base uma escala de relação sinal/ruído que permite numa primeira instância visualizar as amostras com uma maior e menor diferença entre sinal e ruído. Foi também implementado um método para realizar a deteção de picos em RMN 2D baseado na procura por valores máximos locais. Esta operação tem por objectivo obter uma estrutura de dados simplificada que melhor beneficia das funcionalidades do specmine. Estas incluem operações de pré-processamento, análises uni- e multivariada, métodos de seleção de variáveis e aprendizagem máquina. As funções desenvolvidas para RMN 2D foram validadas com dados experimentais recolhidos de dois artigos científicos, disponíveis em bases de dados de metabolómica e sobre os quais foram aplicados os passos de pré-processamento que permitissem a comparação de resultados. Estes dados originaram dois casos de estudos que abordavam diferentes instrumentos utilizados em RMN, Bruker e Varian, reforçando desta forma a flexibilidade do specmine relativamente as tipologias de dados capazes de serem lidas. Estes casos foram realizados utilizando principalmente o specmine, no entanto, a utilização de packages externos foi necessária para passos de processamento específicos, como por exemplo, a normalização por quociente probabilístico. Uma pipeline para analise de dados RMN 2D foi adicionada ao specmine, sob a forma de vignette, um formato de documentação longa adequado a packages implementados no programa R. Desta forma e proporcionado ao utilizador um conjunto de procedimentos, orientados a utilização correta das funcionalidades implementadas

Contribution to the development of methods and systems for the automatization during the early stages of bioprocess development

This thesis is framed within the field of red biotechnology and more specifically in the development of bioprocesses for cell species that feature some therapeutical interest, either for the production of vaccines and monoclonal antibodies or stem cell experimental research. The main objective was the development and application of different instrumental techniques for the control and online monitorization of cell cultures. Oxygen consumption OUR (Oxygen Uptake Rate) was chosen as the central theme since this parameter has often been referenced as the most straighforward indicator of metabolic activity in animal cell culture. This thesis was carried out in the context of a Spin-Off project (Hexascreen Culture Technologies) whose objective was the development of disposable Minibioreactors intended for biopharmaceutical research. Obviously, this has led to a number of important trade-offs, as well as the proposal of several imaginative solutions to solve various technological challenges. For this reason and in order to offer a better idea of the work's scope, it was decided to include in the thesis not only the description of the method and results related to the OUR estimation but a detailed description of the systems developed. Results demonstrate the feasibility of a simplified procedure for estimating the oxygen consumption. This is a review of the Stationary liquid phase mass balance method which allows reducing the implementation cost and unlike the Dynamic method (The most usual thechnique) prevents changes on the oxygen tension that could affect the cell's normal arctivity. The proposed method is based on the accurate control of the oxygen concentration by means of PWM driven electrovalves and using the control loop internal signals to estimate the OUR.Aquesta Tesi doctoral està enquadrada en l'àmbit de la Biotecnologia vermella i més concretament en el desenvolupament de Bioprocessos relacionats amb espècies cel·lulars d’interès terapèutic, bé sigui per a la producció de vacunes, anticossos monoclonals o bé per a la recerca experimental amb cèl·lules mare. L'objectiu general ha estat el desenvolupament i aplicació de diferents tècniques instrumentals per al control i monitorització en línia de cultius cel·lulars, tant mateix d'entre les diferents tècniques emprades es va escollir la monitorització de la demanda d'oxigen O.U.R. (Oxygen Uptake Rate) com a tema central de la tesi degut a que aquest paràmetre ha estat referenciat sovint com un dels millors indicadors de l'activitat metabòlica en cultius de cèl·lules animals. Cal mencionar que la Tesi ha estat duta a terme en el context d'un projecte empresarial (HexaScreen Culture Technologies) l'objectiu del qual ha estat el desenvolupament de Minibioreactors d'un sol ús orientats al mon de la recerca Biofarmacèutica. Òbviament això ha comportant un número important de compromisos a l'hora d'abordar les diferents tasques, així com el plantejament de solucions imaginatives per a la resolució dels diferents reptes tecnològic. Per aquest motiu i per tal de transmetre una millor idea de l'abast del treball realitzat, es va decidir incloure en la tesi no només la descripció del mètode i resultats relacionats amb l'estimació de la O.U.R. sinó amés una descripció prou detallada dels sistemes desenvolupats. Pel que fa al tema central de la tesi, es demostra la viabilitat d'un procediment simplificat per a l'estimació de la demanda d’oxigen. Es tracta d'una revisió del procediment d'estimació de la OUR en condicions de concentració estacionària en la fase líquida que permet reduir-ne el cost de implementació tot prescindint de l'ús de cabalímetres màssics, així com a diferència del mètode dinàmic (Tècnica més habitual) evitar cap mena de canvi en la tensió d’oxigen que pogués afectar l’activitat normal de les cèl·lules. El mètode proposat, es basa en el control de la concentració d’oxigen mitjançant actuació PWM de les vàlvules d'aereació i l’ús dels propis senyals del llaç de control per tal d'estimar la O.U.R.Postprint (published version

Systematizing FAIR research data management in biomedical research projects: a data life cycle approach

Biomedical researchers are facing data management challenges brought by a new generation of data driven by the advent of translational medicine research. These challenges are further complicated by the recent calls for data re-use and long-term stewardship spearheaded by the FAIR principles initiative. As a result, there is an increasingly wide-spread recognition that advancing biomedical science is becoming dependent on the application of data science to manage and utilize highly diverse and complex data in ways that give it context, meaning, and longevity beyond its initial purpose. However, current methods and practices in biomedical informatics remain to adopt a traditional linear view of the informatics process (collect, store and analyse); focusing primarily on the challenges in data integration and analysis, which are challenges only pertaining to a part of the overall life cycle of research data. The aim of this research is to facilitate the adoption and integration of data management practices into the research life cycle of biomedical projects, thus improving their capabilities into solving data management-related challenges that they face throughout the course of their research work. To achieve this aim, this thesis takes a data life cycle approach to define and develop a systematic methodology and framework towards the systematization of FAIR data management in biomedical research projects. The overarching contribution of this research is the provision of a data-state life cycle model for research data management in Biomedical Translational Research Projects. This model provides insight into the dynamics between 1) the purpose of a research-driven data use case, 2) the data requirements that renders data in a state fit for purpose, 3) the data management functions that prepare and act upon data and 4) the resulting state of data that is _t to serve the use case. This insight led to the development of a FAIR data management framework, which is another contribution of this thesis. This framework provides data managers the groundwork, including the data models, resources and capabilities, needed to build a FAIR data management environment to manage data during the operational stages of a biomedical research project. An exemplary implementation of this architecture (PlatformTM) was developed and validated by real-world research datasets produced by collaborative research programs funded by the Innovative Medicine Initiative (IMI) BioVacSafe 1 , eTRIKS 2 and FAIRplus 3.Open Acces