Overview of Network Analysis in Systems Medicine

Systems Medicine (SM) is an interdisciplinary research paradigm, that heavily relieson complex systems theory, and emphasizes on the studies the human body in termsof systems and the interactions among them, incorporating biochemical,physiological, and environment interactions. The article presents developments in SMresearch, focusing specifically on the network analysis approaches. Network analysisis fundamental for the study of interactions among systems at different levels withinthe human body. The background knowledge is established: the basic concepts ofnodes and edges, and network metrics as well as existing computational tools aredescribed. Different applications in health research are discussed, includingdescriptive and predictive approaches. The use of network analysis in temporal dataand data coming from digital health technologies is further highlighted. Finally, thecurrent challenges are discussed and the foreseen development

Knowledge Discovery Through Large-Scale Literature-Mining of Biological Text-Data

The aim of this study is to develop scalable and efficient literature-mining framework for knowledge discovery in the field of medical and biological sciences. Using this scalable framework, customized disease-disease interaction network can be constructed. Features of the proposed network that differentiate it from existing networks are its 1) flexibility in the level of abstraction, 2) broad coverage, and 3) domain specificity. Empirical results for two neurological diseases have shown the utility of the proposed framework. The second goal of this study is to design and implement a bottom-up information retrieval approach to facilitate literature-mining in the specialized field of medical genetics. Experimental results are being corroborated at the moment

Recent advances in smart biotechnology: Hydrogels and nanocarriers for tailored bioactive molecules depot

Over the past ten years, the global biopharmaceutical market has remarkably grown, with ten over the top twenty worldwide high performance medical treatment sales being biologics. Thus, biotech R&D (research and development) sector is becoming a key leading branch, with expanding revenues. Biotechnology offers considerable advantages compared to traditional therapeutic approaches, such as reducing side effects, specific treatments, higher patient compliance and therefore more effective treatments leading to lower healthcare costs. Within this sector, smart nanotechnology and colloidal self-assembling systems represent pivotal tools able to modulate the delivery of therapeutics. A comprehensive understanding of the processes involved in the self assembly of the colloidal structures discussed therein is essential for the development of relevant biomedical applications. In this review we report the most promising and best performing platforms for specific classes of bioactive molecules and related target, spanning from siRNAs, gene/plasmids, proteins/growth factors, small synthetic therapeutics and bioimaging probes.Istituto Italiano di Tecnologia (IIT)COST Action [CA 15107]People Program (Marie Curie Actions) of the European Union's Seventh Framework Program under REA [606713 BIBAFOODS]Portuguese Foundation for Science and Technology (FCT) [PTDC/AGR-TEC/4814/2014, IF/01005/2014]Fundacao para a Ciencia e Tecnologia [SFRH/BPD/99982/2014]Danish National Research Foundation [DNRF 122]Villum Foundation [9301]Italian Ministry of Instruction, University and Research (MIUR), PRIN [20109PLMH2]"Fondazione Beneficentia Stiftung" VaduzFondo di Ateneo FRAFRAinfo:eu-repo/semantics/publishedVersio

Metabonomics-based omics study and atherosclerosis

Atherosclerosis results from dyslipidemia and systemic inflammation, associated with the strong metabolism and interaction between diet and disease. Strategies based on the global profiling of metabolism would be important to define the mechanisms involved in pathological alterations. Metabonomics is the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. Metabonomics has been used in combination with proteomics and transcriptomics as the part of a systems biology description to understand the genome interaction with the development of atherosclerosis. The present review describes the application of metabonomics to explore the potential role of metabolic disturbances and inflammation in the initiation and development of atherosclerosis. Metabonomics-based omics study offers a new potential for biomarker discovery by disentangling the impacts of diet, environment and lifestyle

An insight towards food-related microbial sets through metabolic modelling and functional analysis

The dietary food digestion depends on the human gastrointestinal tract, where host cells and gut microbes mutually interact. This interplay may also mediate host metabolism, as shown by microbial-derived secondary bile acids, needed for receptor signalling. Microbes are also crucial in the production of fermented foods, such as wine and dairy. Kefir is fermented milk processed by the symbiotic community of bacteria and yeasts. One such species is a yeast Kluyveromyces marxianus. Its thermotolerance is a desired trait in biotechnology since it may reduce the cooling demands during cultivation.The systems biology tools allow analysing various size microbial communities under the different functional scope. For example, the homology prediction tools can give detailed functional insights when working with metagenomics data. The whole-cell metabolic processes can be summarised in genome-scale metabolic models (GEMs), which enable to predict the metabolic capabilities and allow for the integration of omics data.The work shown in this thesis includes i) in silico analysis of food-related microbes; ii) the development of GEMs and RAVEN. With a focus on bile acid metabolism, hundreds of human gut microbes were annotated based on metagenomics data, thereby suggesting the differences in the potential for bile acid processing between healthy and diseased subjects. These findings may be exploitable once aiming to restore the bile acid metabolism for the patients having inflammatory bowel disease. Also, the metabolism of yeast K. marxianus was characterised in genome-scale. Two K. marxianus strains from kefir grains were isolated, sequenced, assembled, and functionally annotated. They were compared with the other ten strains, providing the core and dispensable physiological features for K. marxianus. Furthermore, the first GEM for K. marxianus, namely iSM996, was reconstructed. It was integrated with transcriptomics data to predict its metabolic capabilities in rich medium and high-temperature conditions. The results might be useful to optimise strain-specific medium for high-temperature applications. The final paper comprises the efforts to improve the usability for RAVEN, a toolbox for GEM reconstruction and analysis. Altogether the outcomes of this thesis suggest the potential applications for medicine and industrial biotechnology, which may be facilitated by the newly upgraded RAVEN toolbox

The Bronchoalveolar Lavage Proteome- Phenotypic associations to smoking and divergence towards development of COPD

Proteomic analysis of bronchoalveolar lavage (BAL) fluid from smokers at risk of developing chronic obstructive pulmonary disease (COPD) and never smokers is described. COPD is currently the world's fourth leading cause of death and its prevalence is increasing. The leading cause of COPD is smoking and an estimated 600 million people in the world suffer from COPD which makes it the world's most common chronic disease. The aim of this thesis was to explore and characterize the BAL proteome of never smokers and smokers. The hypotheses were that the BAL proteome reflect smoking habits in subjects, and that smokers susceptible to COPD development have a specific proteome. In order to relate the measurement of protein expression with clinical phenotypes we have developed and utilized an interdisciplinary toolbox that includes protein separation (two-dimensional gel electrophoresis and liquid chromatography), mass spectrometry identification and statistical methods for multivariate analysis. The study material used in this thesis consisted of age matched men all born in 1933, living in one city differing by lifelong smoking history. These were compared by clinical function measurements and histological assessment at the same relative time points. A follow up study after 6-7 years identified a group of subjects who had progressed to COPD GOLD stage 2. Those with COPD shared a distinct protein expression profile in the baseline BAL sample which could be identified using multivariate analysis. This pattern was not observed in BAL samples of asymptomatic smokers free of COPD at the 6-7 year follow-up. The results suggest that specific patterns of protein expression occur in the airways of smokers susceptible to COPD disease progression, before the disease is clinically measurable

Why we should use topological data analysis in ageing: Towards defining the “topological shape of ageing”

Living systems are subject to the arrow of time; from birth, they undergo complex transformations (self-organization) in a constant battle for survival, but inevitably ageing and disease trap them to death. Can ageing be understood and eventually reversed? What tools can be employed to further our understanding of ageing? The present article is an invitation for biologists and clinicians to consider key conceptual ideas and computational tools (known to mathematicians and physicists), which potentially may help dissect some of the underlying processes of ageing and disease. Specifically, we first discuss how to classify and analyse complex systems, as well as highlight critical theoretical difficulties that make complex systems hard to study. Subsequently, we introduce Topological Data Analysis - a novel Big Data tool – which may help in the study of complex systems since it extracts knowledge from data in a holistic approach via topological considerations. These conceptual ideas and tools are discussed in a relatively informal way to pave future discussions and collaborations between mathematicians and biologists studying ageing.Basque Government under the grant “Artificial Intelligence in BCAM number EXP. 2019/00432” Inria associated team "NeuroTransSF

Trajectory Data Mining in Mouse Models of Stroke

Contains fulltext : 273912.pdf (Publisher’s version ) (Open Access)Radboud University, 04 oktober 2022Promotor : Kiliaan, A.J. Co-promotor : Wiesmann, M.167 p

The new science of metagenomics and the challenges of its use in both developed and developing countries

Our view of the microbial world and its impact on human health is changing radically with the ability to sequence uncultured or unculturable microbes sampled directly from their habitats, ability made possible by fast and cheap next generation sequencing technologies. Such recent developments represents a paradigmatic shift in the analysis of habitat biodiversity, be it the human, soil or ocean microbiome. We review here some research examples and results that indicate the importance of the microbiome in our lives and then discus some of the challenges faced by metagenomic experiments and the subsequent analysis of the generated data. We then analyze the economic and social impact on genomic-medicine and research in both developing and developed countries. We support the idea that there are significant benefits in building capacities for developing high-level scientific research in metagenomics in developing countries. Indeed, the notion that developing countries should wait for developed countries to make advances in science and technology that they later import at great cost has recently been challenged