A FRAMEWORK FOR BIOPROFILE ANALYSIS OVER GRID

An important trend in modern medicine is towards individualisation of healthcare to tailor care to the needs of the individual. This makes it possible, for example, to personalise diagnosis and treatment to improve outcome. However, the benefits of this can only be fully realised if healthcare and ICT resources are exploited (e.g. to provide access to relevant data, analysis algorithms, knowledge and expertise). Potentially, grid can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. The integration of grid and the new concept of bioprofile represents a new topic in the healthgrid for individualisation of healthcare. A bioprofile represents a personal dynamic "fingerprint" that fuses together a person's current and past bio-history, biopatterns and prognosis. It combines not just data, but also analysis and predictions of future or likely susceptibility to disease, such as brain diseases and cancer. The creation and use of bioprofile require the support of a number of healthcare and ICT technologies and techniques, such as medical imaging and electrophysiology and related facilities, analysis tools, data storage and computation clusters. The need to share clinical data, storage and computation resources between different bioprofile centres creates not only local problems, but also global problems. Existing ICT technologies are inappropriate for bioprofiling because of the difficulties in the use and management of heterogeneous IT resources at different bioprofile centres. Grid as an emerging resource sharing concept fulfils the needs of bioprofile in several aspects, including discovery, access, monitoring and allocation of distributed bioprofile databases, computation resoiuces, bioprofile knowledge bases, etc. However, the challenge of how to integrate the grid and bioprofile technologies together in order to offer an advanced distributed bioprofile environment to support individualized healthcare remains. The aim of this project is to develop a framework for one of the key meta-level bioprofile applications: bioprofile analysis over grid to support individualised healthcare. Bioprofile analysis is a critical part of bioprofiling (i.e. the creation, use and update of bioprofiles). Analysis makes it possible, for example, to extract markers from data for diagnosis and to assess individual's health status. The framework provides a basis for a "grid-based" solution to the challenge of "distributed bioprofile analysis" in bioprofiling. The main contributions of the thesis are fourfold: A. An architecture for bioprofile analysis over grid. The design of a suitable aichitecture is fundamental to the development of any ICT systems. The architecture creates a meaiis for categorisation, determination and organisation of core grid components to support the development and use of grid for bioprofile analysis; B. A service model for bioprofile analysis over grid. The service model proposes a service design principle, a service architecture for bioprofile analysis over grid, and a distributed EEG analysis service model. The service design principle addresses the main service design considerations behind the service model, in the aspects of usability, flexibility, extensibility, reusability, etc. The service architecture identifies the main categories of services and outlines an approach in organising services to realise certain functionalities required by distributed bioprofile analysis applications. The EEG analysis service model demonstrates the utilisation and development of services to enable bioprofile analysis over grid; C. Two grid test-beds and a practical implementation of EEG analysis over grid. The two grid test-beds: the BIOPATTERN grid and PlymGRID are built based on existing grid middleware tools. They provide essential experimental platforms for research in bioprofiling over grid. The work here demonstrates how resources, grid middleware and services can be utilised, organised and implemented to support distributed EEG analysis for early detection of dementia. The distributed Electroencephalography (EEG) analysis environment can be used to support a variety of research activities in EEG analysis; D. A scheme for organising multiple (heterogeneous) descriptions of individual grid entities for knowledge representation of grid. The scheme solves the compatibility and adaptability problems in managing heterogeneous descriptions (i.e. descriptions using different languages and schemas/ontologies) for collaborated representation of a grid environment in different scales. It underpins the concept of bioprofile analysis over grid in the aspect of knowledge-based global coordination between components of bioprofile analysis over grid

Bioprofile Analysis:A New Approach for the Analysis of Biomedical Data in Alzheimer's Disease

This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a "Bioprofile" analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal "Bioindices" that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an "AD pattern" in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.</p

Towards Data Governance for International Dementia Care Mapping (DCM). A Study Proposing DCM Data Management through a Data Warehousing Approach.

Information Technology (IT) plays a vital role in improving health care systems by enhancing the quality, efficiency, safety, security, collaboration and informing decision making. Dementia, a decline in mental ability which affects memory, concentration and perception, is a key issue in health and social care, given the current context of an aging population. The quality of dementia care is noted as an international area of concern. Dementia Care Mapping (DCM) is a systematic observational framework for assessing and improving dementia care quality. DCM has been used as both a research and practice development tool internationally. However, despite the success of DCM and the annual generation of a huge amount of data on dementia care quality, it lacks a governance framework, based on modern IT solutions for data management, such a framework would provide the organisations using DCM a systematic way of storing, retrieving and comparing data over time, to monitor progress or trends in care quality. Data Governance (DG) refers to the implications of policies and accountabilities to data management in an organisation. The data management procedure includes availability, usability, quality, integrity, and security of the organisation data according to their users and requirements. This novel multidisciplinary study proposes a comprehensive solution for governing the DCM data by introducing a data management framework based on a data warehousing approach. Original contributions have been made through the design and development of a data management framework, describing the DCM international database design and DCM data warehouse architecture. These data repositories will provide the acquisition and storage solutions for DCM data. The designed DCM data warehouse facilitates various analytical applications to be applied for multidimensional analysis. Different queries are applied to demonstrate the DCM data warehouse functionality. A case study is also presented to explain the clustering technique applied to the DCM data. The performance of the DCM data governance framework is demonstrated in this case study related to data clustering results. Results are encouraging and open up discussion for further analysis

Growth behavior of human adipose tissue-derived stromal/stem cells at small scale : numerical and experimental investigations

Human adipose tissue-derived stromal/stem cells (hASCs) are a valuable source of cells for clinical applications, especially in the field of regenerative medicine. Therefore, it comes as no surprise that the interest in hASCs has greatly increased over the last decade. However, in order to use hASCs in clinically relevant numbers, in vitro expansion is required. Single-use stirred bioreactors in combination with microcarriers (MCs) have shown themselves to be suitable systems for this task. However, hASCs tend to be less robust, and thus, more shear sensitive than conventional production cell lines for therapeutic antibodies and vaccines (e.g., Chinese Hamster Ovary cells CHO, Baby Hamster Kidney cells BHK), for which these bioreactors were originally designed. Hence, the goal of this study was to investigate the influence of different shear stress levels on the growth of humane telomerase reversed transcriptase immortalized hASCs (hTERT-ASC) and aggregate formation in stirred single-use systems at the mL scale: the 125 mL (= SP100) and the 500 mL (= SP300) disposable Corning® spinner flask. Computational fluid dynamics (CFD) simulations based on an Euler⁻Euler and Euler⁻Lagrange approach were performed to predict the hydrodynamic stresses (0.06⁻0.87 Pa), the residence times (0.4⁻7.3 s), and the circulation times (1.6⁻16.6 s) of the MCs in different shear zones for different impeller speeds and the suspension criteria (Ns1u, Ns1). The numerical findings were linked to experimental data from cultivations studies to develop, for the first time, an unstructured, segregated mathematical growth model for hTERT-ASCs. While the 125 mL spinner flask with 100 mL working volume (SP100) provided up to 1.68.10⁵ hTERT-ASC/cm² (= 0.63 × 10⁶ living hTERT-ASCs/mL, EF 56) within eight days, the peak living cell density of the 500 mL spinner flask with 300 mL working volume (SP300) was 2.46 × 10⁵ hTERT-ASC/cm² (= 0.88 × 10⁶ hTERT-ASCs/mL, EF 81) and was achieved on day eight. Optimal cultivation conditions were found for Ns1u < N < Ns1, which corresponded to specific power inputs of 0.3⁻1.1 W/m³. The established growth model delivered reliable predictions for cell growth on the MCs with an accuracy of 76⁻96% for both investigated spinner flask types

Development of a bio-inspired in silico-in vitro platform: towards personalised healthcare through optimisation of a bone-marrow mimicry bioreactor

Human red blood cell production, or erythropoiesis, occurs within bone marrow. Living animal and human cadaver models have demonstrated the marrow production of red blood cells is a spatially-complex process, where cells replicate, mature, and migrate between distinct niches defined by biochemical nutrient access, supportive neighboring cells, and environmental structure. Unfortunately, current research in understanding normal and abnormal human production of blood takes place in petri dishes and t-flasks as 2D liquid suspension cultures, neglecting the role of the marrow environment for blood production. The culture of blood on marrow-mimetic 3D biomaterials has been used as a laboratory model of physiological blood production, but lacks characterization. In this work, a 3D biomaterial platform is developed and to capture the in vivo blood production process and manufacture red blood cells from human umbilical cord blood. First ceramic hollow fibres were designed and tested to be incorporated and perfused in a 3D porous scaffold bioreactor to mimic marrow structure, provide a better expansion of cell numbers, a better diffusion of nutrients, and allow for the continuous, non-invasive harvest of small cells in comparison to static, unperfused biomaterials. Quantitative 3D image analysis tools were developed to spatially assess bioreactor distributions and associations of and between different cell types. Using these tools, the bioreactor distribution of red blood cell production were characterized within niches in collaboration with supportive, non-blood cell types and designed miniaturised, parallelised mini-bioreactors to further explore bioreactor capabilities. This thesis presents a hollow fibre bioreactor able to produce blood cells alongside supportive cells at 1,000-fold higher cell densities with 10-fold fewer supplemented factor than flask cultures, without serum, with one cell source, and continuously harvest enucleate red blood cell product to provide a physiologically-relevant model for cell expansion protocols.Open Acces

Novel in vivo biosensors for monitoring of mammalian cell cultures

Mammalian cell cultures are used for production of biopharmaceuticals, e.g. monoclonal antibodies. Only mammalian hybridoma cells contain the pathways for antibody production, but due to their multicellular origin the cells have complex nutrient requirements. Cell growth and antibody production are limited by supply of essential nutrients such as glutamine and accumulation of toxic waste products such as lactate. Many attempts have been made at tackling these challenges, e.g. by optimising growth media to keep metabolite concentrations at optimal levels. These approaches have been hampered by our ability to monitor relevant cell culture parameters such as metabolite concentration dynamics in real time. The aim of this study is to develop a solution to this problem using a synthetic biology approach. Whole-cell bacterial biosensors for important culture parameters, glutamine, leucine, alanine and lactate, were designed, built and characterised. The biosensors were designed from natural metabolite-sensing systems, specifically the Escherichia coli Ntr regulon, Lrp regulon and lldPRD operon and the Bacillus subtilis GlnK-GlnL system. Characterisation of the biosensors in defined medium using known lactate concentrations was followed by validation in mammalian cell culture media and using cell culture samples. A lactate sensor based on the lldPRD operon showed a reliable lactate-response during initial characterisation and was chosen to determine lactate concentrations in cell culture samples in parallel with lactate analysis using a bioprofiler. Generally, the lactate concentrations from the two methods showed a good match. Data points where the results differed showed that there are some sources of error in the usage of the biosensor that could be addressed in future. The results of this study also highlight the many challenges of applying synthetic biology constructs to complex industrial contexts. The biosensors presented in this study are more generally applicable in any experimental context that requires sensing of metabolites.Open Acces

Towards continuous biomanufacturing a computational approach for the intensification of monoclonal antibody production

Current industrial trends encourage the development of sustainable, environmentally friendly processes with reduced energy and raw material consumption. Meanwhile, the increasing market demand as well as the tight regulations in product quality, necessitate efficient operating procedures that guarantee products of high purity. In this direction, process intensification via continuous operation paves the way for the development of novel, eco-friendly processes, characterized by higher productivity compared to batch (Nicoud, 2014). The shift towards continuous operation could advance the market of high value biologics, such as monoclonal antibodies (mAbs), as it would lead to shorter production times, decreased costs, as well as significantly less energy consumption (Konstantinov and Cooney, 2015, Xenopoulos, 2015). In particular, mAb production comprises two main steps: the culturing of the cells (upstream) and the purification of the targeted product (downstream). Both processes are highly complex and their performance depends on various parameters. In particular, the efficiency of the upstream depends highly on cell growth and the longevity of the culture, while product quality can be jeopardized in case the culture is not terminated timely. Similarly, downstream processing, whose main step is the chromatographic separation, relies highly on the setup configuration, as well as on the composition of the upstream mixture. Therefore, it is necessary to understand and optimize both processes prior to their integration. In this direction, the design of intelligent computational tools becomes eminent. Such tools can form a solid basis for the: (i) execution of cost-free comparisons of various operating strategies, (ii) design of optimal operation profiles and (iii) development of advanced, intelligent control systems that can maintain the process under optimal operation, rejecting disturbances. In this context, this work focuses on the development of advanced computational tools for the improvement of the performance of: (a) chromatographic separation processes and (b) cell culture systems, following the systematic PAROC framework and software platform (Pistikopoulos et al., 2015). In particular we develop model-based controllers for single- and multi-column chromatographic setups based on the operating principles of an industrially relevant separation process. The presented strategies are immunized against variations in the feed stream and can successfully compensate for time delays caused due to the column residence time. Issues regarding the points of integration in multi-column systems are also discussed. Moreover, we design and test in silico model-based control strategies for a cell culture system, aiming to increase the culture productivity and drive the system towards continuous operation. Challenges and potential solutions for the seamless integration of the examined bioprocess are also investigated at the end of this thesis.Open Acces