946 research outputs found

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin

    Clinical, immunological and genetic features of histiocytic disorders

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    Clinical, immunological and genetic features of histiocytic disorders

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    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Integrating Experimental and Computational Approaches to Optimize 3D Bioprinting of Cancer Cells

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    A key feature distinguishing 3D bioprinting from other 3D cell culture techniques is its precise control over created structures. This property allows for the high-resolution fabrication of biomimetic structures with controlled structural and mechanical properties such as porosity, permeability, and stiffness. However, for bioprinting to be successful, a comprehensive understanding of cell behavior is essential, yet challenging. This includes the survivability of cells throughout the printing process, their interactions with the printed structures, and their responses to environmental cues after printing. There are numerous variables in bioprinting which influence the cell behavior, so bioprinting quality during and after the procedure. Thus, to achieve desirable results, it is necessary to consider and optimize these influential variables. So far, these optimizations have been accomplished primarily through trial and error and replicating several experiments, a procedure that is not only time-consuming but also costly. This issue motivated the development of computational techniques in the bioprinting process to more precisely predict and elucidate cells’ function within 3D printed structures during and after printing. During printing, we developed predictive machine learning models to determine the effect of different variables such as cell type, bioink formulation, printing settings parameters, and crosslinking condition on cell viability in extrusion-based bioprinting. To do this, we first created a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed regression and classification neural networks to predict cell viability based on these bioprinting variables. Compared to models that have been developed so far, the performance of our models was superior and showed great prediction results. The study further demonstrated that among the variables investigated in bioprinting, cell type, printing pressure, and crosslinker concentration, respectively, had the most significant impact on the survival of cells. Additionally, we introduced a new optimization strategy that employs the Bayesian optimization model based on the developed regression neural network to determine the optimal combination of the selected bioprinting parameters for maximizing cell viability and eliminating trial-and-error experiments. In our study, this strategy enabled us to identify the optimal crosslinking parameters, within a specified range, including those not previously explored, resulting in optimum cell viability. Finally, we experimentally validated the optimization model's performance. After printing, we developed a cellular automata model for the first time to predict and elucidate the post-printing cell behavior within the 3D bioprinted construct. To improve our model, we bioprinted a 3D construct using cell-laden hydrogel and evaluated cellular functions, including viability and proliferation, in 11 days. The results showed that our model successfully simulated the 3D bioprinted structure and captured in-vitro observations. The proposed model is beneficial for demonstrating complex cellular systems, including cellular proliferation, movement, cell interactions with the environment (e.g., extracellular microenvironment and neighboring cells), and cell aggregation within the scaffold. We also demonstrated that this computational model could predict post-printing biological functions for different initial cell numbers in bioink and different bioink formulations with gelatin and alginate without replicating several in-vitro measurements. Taken all together, this thesis introduces novel bioprinting process design strategies by presenting mathematical and computational frameworks for both during and after bioprinting. We believe such frameworks will substantially impact 3D bioprinting's future application and inspire researchers to further realize how computational methods might be utilized to advance in-vitro 3D bioprinting research

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes

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    The blood–brain barrier (BBB) is a selectively permeable boundary that separates the circulating blood from the extracellular fluid of the brain and is an essential component for brain homeostasis. In glioblastoma (GBM), the BBB of peritumoral vessels is often disrupted. Pericytes, being important to maintaining BBB integrity, can be functionally modified by GBM cells which induce proliferation and cell motility via the TGF-β-mediated induction of central epithelial to mesenchymal transition (EMT) factors. We demonstrate that pericytes strengthen the integrity of the BBB in primary endothelial cell/pericyte co-cultures as an in vitro BBB model, using TEER measurement of the barrier integrity. In contrast, this effect was abrogated by TGF-β or conditioned medium from TGF-β secreting GBM cells, leading to the disruption of a so far intact and tight BBB. TGF-β notably changed the metabolic behavior of pericytes, by shutting down the TCA cycle, driving energy generation from oxidative phosphorylation towards glycolysis, and by modulating pathways that are necessary for the biosynthesis of molecules used for proliferation and cell division. Combined metabolomic and transcriptomic analyses further underscored that the observed functional and metabolic changes of TGF-β-treated pericytes are closely connected with their role as important supporting cells during angiogenic processes

    The Creation of a Biophysical Modeling Universe: The UNIfied and VERSatile bio response Engine

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    Radiotherapy is a crucial pillar of cancer therapy and ion beams promise superior dose conformity and potentially enhanced biological effectiveness in comparison to conventional radiation modalities. However, several factors are known to modify the biological effect of radiation. The capability to model their impact within a unified description of radiation action in conventional and ion beam fields would greatly enhance the ability to prescribe the optimal treatment and improve the knowledge of underlying mechanisms. To this end, the initial developments of the mechanistic UNIfied and VERSatile bio response Engine (UNIVERSE) are presented in this work. The effects of radiosensitizing drugs and mutations as well as DNA repair kinetics were modeled for each radiation quality. For sparsely ionizing radiation, the sparing effects at ultra-high dose-rates (uHDR) applied in FLASH radiotherapy were introduced based on oxygen depletion rates approaching measured values. Benchmarks against own or literature data are presented for each development. Challenges concerning the transition of oxygen and uHDR effects to ion beams as well as the vision of personalized biomarker-based patient plan adaptation based on UNIVERSE are discussed. UNIVERSE offers clinically relevant insights into radiobiological interdependencies and its versatility will allow it to follow future trends in radiotherapy
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