Refine
Has Fulltext
- yes (26)
Is part of the Bibliography
- yes (26)
Year of publication
Document Type
- Doctoral Thesis (23)
- Journal article (2)
- Master Thesis (1)
Keywords
- Bioinformatik (26) (remove)
Institute
Sonstige beteiligte Institutionen
In this work models for molecular networks consisting of ordinary differential equations are extended by terms that include the interaction of the corresponding molecular network with the environment that the molecular network is embedded in. These terms model the effects of the external stimuli on the molecular network. The usability of this extension is demonstrated with a model of a circadian clock that is extended with certain terms and reproduces data from several experiments at the same time.
Once the model including external stimuli is set up, a framework is developed in order to calculate external stimuli that have a predefined desired effect on the molecular network. For this purpose the task of finding appropriate external stimuli is formulated as a mathematical optimal control problem for which in order to solve it a lot of mathematical methods are available. Several methods are discussed and worked out in order to calculate a solution for the corresponding optimal control problem. The application of the framework to find pharmacological intervention points or effective drug combinations is pointed out and discussed. Furthermore the framework is related to existing network analysis tools and their combination for network analysis in order to find dedicated external stimuli is discussed.
The total framework is verified with biological examples by comparing the calculated results with data from literature. For this purpose platelet aggregation is investigated based on a corresponding gene regulatory network and associated receptors are detected. Furthermore a transition from one to another type of T-helper cell is analyzed in a tumor setting where missing agents are calculated to induce the corresponding switch in vitro. Next a gene regulatory network of a myocardiocyte is investigated where it is shown how the presented framework can be used to compare different treatment strategies with respect to their beneficial effects and side effects quantitatively. Moreover a constitutively activated signaling pathway, which thus causes maleficent effects, is modeled and intervention points with corresponding treatment strategies are determined that steer the gene regulatory network from a pathological expression pattern to physiological one again.
Development and application of computational tools for RNA-Seq based transcriptome annotations
(2019)
In order to understand the regulation of gene expression in organisms, precise genome annotation is essential. In recent years, RNA-Seq has become a potent method for generating and improving genome annotations. However, this Approach is time consuming and often inconsistently performed when done manually. In particular, the discovery of non-coding RNAs benefits strongly from the application of RNA-Seq data but requires significant amounts of expert knowledge and is labor-intensive. As a part of my doctoral study, I developed a modular tool called ANNOgesic that can detect numerous transcribed genomic features, including non-coding RNAs, based on RNA-Seq data in a precise and automatic fashion with a focus on bacterial and achaeal species. The software performs numerous analyses and generates several visualizations. It can generate annotations of high-Resolution that are hard to produce using traditional annotation tools that are based only on genome sequences. ANNOgesic can detect numerous novel genomic Features like UTR-derived small non-coding RNAs for which no other tool has been developed before. ANNOgesic is available under an open source license (ISCL) at https://github.com/Sung-Huan/ANNOgesic.
My doctoral work not only includes the development of ANNOgesic but also its application to annotate the transcriptome of Staphylococcus aureus HG003 - a strain which has been a insightful model in infection biology. Despite its potential as a model, a complete genome sequence and annotations have been lacking for HG003. In order to fill this gap, the annotations of this strain, including sRNAs and their functions, were generated using ANNOgesic by analyzing differential RNA-Seq data from 14 different samples (two media conditions with seven time points), as well as RNA-Seq data generated after transcript fragmentation. ANNOgesic was
also applied to annotate several bacterial and archaeal genomes, and as part of this its high performance was demonstrated. In summary, ANNOgesic is a powerful computational tool for RNA-Seq based annotations and has been successfully applied to several species.
Neurobiology is widely supported by bioinformatics. Due to the big amount of data generated from the biological side a computational approach is required. This thesis presents four different cases of bioinformatic tools applied to the service of Neurobiology.
The first two tools presented belong to the field of image processing. In the first case, we make use of an algorithm based on the wavelet transformation to assess calcium activity events in cultured neurons. We designed an open source tool to assist neurobiology researchers in the analysis of calcium imaging videos. Such analysis is usually done manually which is time consuming and highly subjective. Our tool speeds up the work and offers the possibility of an unbiased detection of the calcium events. Even more important is that our algorithm not only detects the neuron spiking activity but also local spontaneous activity which is normally discarded because it is considered irrelevant. We showed that this activity is determinant in the calcium dynamics in neurons and it is involved in important functions like signal modulation and memory and learning.
The second project is a segmentation task. In our case we are interested in segmenting the neuron nuclei in electron microscopy images of c.elegans. Marking these structures is necessary in order to reconstruct the connectome of the organism. C.elegans is a great study case due to the simplicity of its nervous system (only 502 neurons). This worm, despite its simplicity has taught us a lot about neuronal mechanisms. There is still a lot of information we can extract from the c.elegans, therein lies the importance of reconstructing its connectome. There is a current version of the c.elegans connectome but it was done by hand and on a single subject which leaves a big room for errors. By automatizing the segmentation of the electron microscopy images we guarantee an unbiased approach and we will be able to verify the connectome on several subjects.
For the third project we moved from image processing applications to biological modeling. Because of the high complexity of even small biological systems it is necessary to analyze them with the help of computational tools. The term in silico was coined to refer to such computational models of biological systems. We designed an in silico model of the TNF (Tumor necrosis factor) ligand and its two principal receptors. This biological system is of high relevance because it is involved in the inflammation process. Inflammation is of most importance as protection mechanism but it can also lead to complicated diseases (e.g. cancer). Chronic inflammation processes can be particularly dangerous in the brain. In order to better understand the dynamics that govern the TNF system we created a model using the BioNetGen language. This is a rule based language that allows one to simulate systems where multiple agents are governed by a single rule. Using our model we characterized the TNF system and hypothesized about the relation of the ligand with each of the two receptors. Our hypotheses can be later used to define drug targets in the system or possible treatments for chronic inflammation or lack of the inflammatory response.
The final project deals with the protein folding problem. In our organism proteins are folded all the time, because only in their folded conformation are proteins capable of doing their job (with some very few exceptions). This folding process presents a great challenge for science because it has been shown to be an NP problem. NP means non deterministic Polynomial time problem. This basically means that this kind of problems cannot be efficiently solved. Nevertheless, somehow the body is capable of folding a protein in just milliseconds. This phenomenon puzzles not only biologists but also mathematicians. In mathematics NP problems have been studied for a long time and it is known that given the solution to one NP problem we could solve many of them (i.e. NP-complete problems). If we manage to understand how nature solves the protein folding problem then we might be able to apply this solution to many other problems. Our research intends to contribute to this discussion. Unfortunately, not to explain how nature solves the protein folding problem, but to explain that it does not solve the problem at all. This seems contradictory since I just mentioned that the body folds proteins all the time, but our hypothesis is that the organisms have learned to solve a simplified version of the NP problem. Nature does not solve the protein folding problem in its full complexity. It simply solves a small instance of the problem. An instance which is as simple as a convex optimization problem. We formulate the protein folding problem as an optimization problem to illustrate our claim and present some toy examples to illustrate the formulation. If our hypothesis is true, it means that protein folding is a simple problem. So we just need to understand and model the conditions of the vicinity inside the cell at the moment the folding process occurs. Once we understand this starting conformation and its influence in the folding process we will be able to design treatments for amyloid diseases such as Alzheimer's and Parkinson's.
In summary this thesis project contributes to the neurobiology research field from four different fronts. Two are practical contributions with immediate benefits, such as the calcium imaging video analysis tool and the TNF in silico model. The neuron nuclei segmentation is a contribution for the near future. A step towards the full annotation of the c.elegans connectome and later for the reconstruction of the connectome of other species. And finally, the protein folding project is a first impulse to change the way we conceive the protein folding process in nature. We try to point future research in a novel direction, where the amino code is not the most relevant characteristic of the process but the conditions within the cell.
The field of genetics faces a lot of challenges and opportunities in both research and diagnostics due to the rise of next generation sequencing (NGS), a technology that allows to sequence DNA increasingly fast and cheap.
NGS is not only used to analyze DNA, but also RNA, which is a very similar molecule also present in the cell, in both cases producing large amounts of data.
The big amount of data raises both infrastructure and usability problems, as powerful computing infrastructures are required and there are many manual steps in the data analysis which are complicated to execute.
Both of those problems limit the use of NGS in the clinic and research, by producing a bottleneck both computationally and in terms of manpower, as for many analyses geneticists lack the required computing skills.
Over the course of this thesis we investigated how computer science can help to improve this situation to reduce the complexity of this type of analysis.
We looked at how to make the analysis more accessible to increase the number of people that can perform OMICS data analysis (OMICS groups various genomics data-sources).
To approach this problem, we developed a graphical NGS data analysis pipeline aimed at a diagnostics environment while still being useful in research in close collaboration with the Human Genetics Department at the University of Würzburg.
The pipeline has been used in various research papers on covering subjects, including works with direct author participation in genomics, transcriptomics as well as epigenomics.
To further validate the graphical pipeline, a user survey was carried out which confirmed that it lowers the complexity of OMICS data analysis.
We also studied how the data analysis can be improved in terms of computing infrastructure by improving the performance of certain analysis steps.
We did this both in terms of speed improvements on a single computer (with notably variant calling being faster by up to 18 times), as well as with distributed computing to better use an existing infrastructure.
The improvements were integrated into the previously described graphical pipeline, which itself also was focused on low resource usage.
As a major contribution and to help with future development of parallel and distributed applications, for the usage in genetics or otherwise, we also looked at how to make it easier to develop such applications.
Based on the parallel object programming model (POP), we created a Java language extension called POP-Java, which allows for easy and transparent distribution of objects.
Through this development, we brought the POP model to the cloud, Hadoop clusters and present a new collaborative distributed computing model called FriendComputing.
The advances made in the different domains of this thesis have been published in various works specified in this document.
Biological systems such as cells or whole organisms are governed by complex regulatory networks of transcription factors, hormones and other regulators which determine the behavior of the system depending on internal and external stimuli. In mathematical models of these networks, genes are represented by interacting “nodes” whose “value” represents the activity of the gene.
Control processes in these regulatory networks are challenging to elucidate and quantify. Previous control centrality metrics, which aim to mathematically capture the ability of individual nodes to control biological systems, have been found to suffer from problems regarding biological plausibility.
This thesis presents a new approach to control centrality in biological networks. Three types of network control are distinguished: Total control centrality quantifies the impact of gene mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signaling cascades (e.g control in mouse colon stem cells). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Well-defined network manipulations define all three centralities not only for nodes, but also for the interactions between them, enabling detailed insights into network pathways.
The calculation of the new metrics is made possible by substantial computational improvements in the simulation algorithms for several widely used mathematical modeling paradigms for genetic regulatory networks, which are implemented in the regulatory network simulation framework Jimena created for this thesis.
Applying the new metrics to biological networks and artificial random networks shows how these mathematical concepts correspond to experimentally verified gene functions and signaling pathways in immunity and cell differentiation. In contrast to controversial previous results even from the Barabási group, all results indicate that the ability to control biological networks resides in only few driver nodes characterized by a high number of connections to the rest of the network. Autoregulatory loops strongly increase the controllability of the network, i.e. its ability to control itself, and biological networks are characterized by high controllability in conjunction with high robustness against mutations, a combination that can be achieved best in sparsely connected networks with densities (i.e. connections to nodes ratios) around 2.0 - 3.0.
The new concepts are thus considerably narrowing the gap between network science and biology and can be used in various areas such as system modeling, plausibility trials and system analyses.
Medical applications discussed in this thesis include the search for oncogenes and pharmacological targets, as well their functional characterization.
Die Sequenzierungstechnologien entwickeln sich stetig weiter, dies ermöglicht eine zuvor nicht erreichte Ausbeute an experimentellen Daten und auch an Neuentwicklungen von zuvor nicht realisierbaren Experimenten. Zugleich werden spezifische Datenbanken, Algorithmen und Softwareprogramme entwickelt, um die neu entstandenen Daten zu analysieren. Während der Untersuchung bioinformatischer Methoden für die Identifizierung und Klassifizierung somatischer Mutationen in hämatologischen Erkrankungen, zeigte sich eine hohe Vielfalt an alternativen Softwaretools die für die jeweiligen Analyseschritte genutzt werden können. Derzeit existiert noch kein Standard zur effizienten Analyse von Mutationen aus Next-Generation-Sequencing (NGS)-Daten. Die unterschiedlichen Methoden und Pipelines generieren Kandidaten, die zum größten Anteil in allen Ansätzen identifiziert werden können, jedoch werden Software spezifische Kandidaten nicht einheitlich detektiert.
Um eine einheitliche und effiziente Analyse von NGS-Daten durchzuführen war im Rahmen dieser Arbeit die Entwicklung einer benutzerfreundlichen und einheitlichen Pipeline vorgesehen. Hierfür wurden zunächst die essentiellen Analysen wie die Identifizierung der Basen, die Alignierung und die Identifizierung der Mutationen untersucht. Des Weiteren wurden unter Berücksichtigung von Effizienz und Performance diverse verfügbare Softwaretools getestet, ausgewertet und sowohl mögliche Verbesserungen als auch Erleichterungen der bisherigen Analysen vorgestellt und diskutiert. Durch Mitwirken in Konsortien wie der klinischen Forschergruppe 216 (KFO 216) und International Cancer Genome Consortium (ICGC) oder auch bei Haus-internen Projekten wurden Datensätze zu den Entitäten Multiples Myelom (MM), Burkitt Lymphom (BL) und Follikuläres Lymphom (FL) erstellt und analysiert. Die Selektion geeigneter Softwaretools und die Generierung der Pipeline basieren auf komparativen Analysen dieser Daten, sowie auf geteilte Ergebnisse und Erfahrungen in der Literatur und auch in Foren. Durch die gezielte Entwicklung von Skripten konnten biologische und klinische Fragestellungen bearbeitet werden. Hierzu zählten eine einheitliche Annotation der Gennamen, sowie die Erstellung von Genmutations-Heatmaps mit nicht Variant-Calling-File (VCF)-Syntax konformen Dateien. Des Weiteren konnten nicht abgedeckte Regionen des Genoms in den NGS-Daten identifiziert und analysiert werden. Neue Projekte zur detaillierten Untersuchung der Verteilung von wiederkehrender Mutationen und Funktionsassays zu einzelnen Mutationskandidaten konnten basierend auf den Ergebnissen initiiert werden.
Durch eigens erstellte Python-Skripte konnte somit die Funktionalität der Pipeline erweitert werden und zu wichtigen Erkenntnissen bei der biologischen Interpretation der Sequenzierungsdaten führen, wie beispielsweise zu der Detektion von drei neuen molekularen Subgruppen im MM. Die Erweiterungen, der in dieser Arbeit entwickelten Pipeline verbesserte somit die Effizienz der Analyse und die Vergleichbarkeit unserer Daten. Des Weiteren konnte durch die Erstellung eines eigenen Skripts die Analyse von unbeachteten Regionen in den NGS-Daten erfolgen.
Localization microscopy is a class of super-resolution fluorescence microscopy techniques. Localization microscopy methods are characterized by stochastic temporal isolation of fluorophore emission, i.e., making the fluorophores blink so rapidly that no two are
likely to be photoactive at the same time close to each other. Well-known localization microscopy methods include dSTORM}, STORM, PALM, FPALM, or GSDIM. The biological community has taken great interest in localization microscopy, since it can enhance the resolution of common fluorescence microscopy by an order of magnitude at little experimental cost.
However, localization microscopy has considerable computational cost since millions of individual stochastic emissions must be located with nanometer precision. The computational cost of this evaluation, and the organizational cost of implementing the complex algorithms, has impeded adoption of super-resolution microscopy for a long time.
In this work, I describe my algorithmic framework for evaluating localization microscopy data.
I demonstrate how my novel open-source software achieves real-time data evaluation, i.e., can evaluate data faster than the common experimental setups can capture them.
I show how this speed is attained on standard consumer-grade CPUs, removing the need for computing on expensive clusters or deploying graphics processing units.
The evaluation is performed with the widely accepted Gaussian PSF model and a Poissonian maximum-likelihood noise model.
I extend the computational model to show how robust, optimal two-color evaluation is realized, allowing correlative microscopy between multiple proteins or structures. By employing cubic B-splines, I show how the evaluation of three-dimensional samples can be made simple and robust, taking an important step towards precise imaging of micrometer-thick samples.
I uncover the behavior and limits of localization algorithms in the face of increasing emission densities.
Finally, I show up algorithms to extend localization microscopy to common biological problems.
I investigate cellular movement and motility by considering the in vitro movement of myosin-actin filaments. I show how SNAP-tag fusion proteins enable imaging with bright and stable organic fluorophores in live cells. By analyzing the internal structure of protein clusters, I show how localization microscopy can provide new quantitative approaches beyond pure imaging.
Zentrales Ziel dieser Arbeit war es, Methoden der Mikroskopie, Bildverarbeitung und Bilderkennung für die Charakterisierungen verschiedener Phyotplankter zu nutzen, um deren Analyse zu verbessern und zu vereinfachen.
Der erste Schwerpunkt der Arbeit lag auf der Analyse von Phytoplanktongemeinschaften, die im Rahmen der Überprüfung der Süßwasserqualität als Marker dienen. Die konventionelle Analyse ist dabei sehr aufwendig, da diese noch immer vollständig von Hand durchgeführt wird und hierfür speziell ausgebildetes Personal eingesetzt werden muss. Ziel war es, ein System zur automatischen Erkennung aufzubauen, um die Analyse vereinfachen zu können. Mit Hilfe von automatischer Mikroskopie war es möglich Plankter unterschiedlicher Ausdehnung durch die Integration mehrerer Schärfeebenen besser in einem Bild aufzunehmen. Weiterhin wurden verschiedene Fluoreszenzeigenschaften in die Analyse integriert. Mit einem für ImageJ erstellten Plugin können Organismen vom Hintergrund der Aufnahmen abgetrennt und eine Vielzahl von Merkmalen berechnet werden. Über das Training von neuralen Netzen wird die Unterscheidung von verschieden Gruppen von Planktontaxa möglich. Zudem können weitere Taxa einfach in die Analyse integriert und die Erkennung erweitert werden. Die erste Analyse von Mischproben, bestehend aus 10 verschiedenen Taxa, zeigte dabei eine durchschnittliche Erkennungsrate von 94.7% und eine durchschnittliche Falsch-Positiv Rate von 5.5%. Im Vergleich mit bestehenden Systemen konnte die Erkennungsrate verbessert und die Falsch Positiv Rate deutlich gesenkt werde. Bei einer Erweiterung des Datensatzes auf 22 Taxa wurde darauf geachtet, Arten zu verwenden, die verschiedene Stadien in ihrem Wachstum durchlaufen oder höhere Ähnlichkeiten zu den bereits vorhandenen Arten aufweisen, um evtl. Schwachstellen des Systemes erkennen zu können. Hier ergab sich eine gute Erkennungsrate (86.8%), bei der der Ausschluss von nicht-planktonischen Partikeln (11.9%) weiterhin verbessert war. Der Vergleich mit weiteren Klassifikationsverfahren zeigte, dass neuronale Netze anderen Verfahren bei dieser Problemstellung überlegen sind. Ähnlich gute Klassifikationsraten konnten durch Support Vektor Maschinen erzielt werden. Allerdings waren diese bei der Unterscheidung von unbekannten Partikeln dem neuralen Netz deutlich unterlegen.
Der zweite Abschnitt stellt die Entwicklung einer einfachen Methode zur Viabilitätsanalyse von Cyanobakterien, bei der keine weitere Behandlung der Proben notwendig ist, dar. Dabei wird die rote Chlorophyll - Autofluoreszenz als Marker für lebende Zellen und eine grüne unspezifische Fluoreszenz als Marker für tote Zellen genutzt. Der Assay wurde mit dem Modellorganismus Synechocystis sp. PCC 6803 etabliert und validiert. Die Auswahl eines geeigeneten Filtersets ermöglicht es beide Signale gleichzeitig anzuregen und zu beobachten und somit direkt zwischen lebendenden und toten Zellen zu unterscheiden. Die Ergebnisse zur Etablierung des Assays konnten durch Ausplattieren, Chlorophyllbestimmung und Bestimmung des Absorbtionsspektrums bestätigt werden. Durch den Einsatz von automatisierter Mikroskopie und einem neu erstellten ImageJ Plugin wurde eine sehr genaue und schnelle Analyse der Proben möglich. Der Einsatz beim Monitoring einer mutagenisierten Kultur zur Erhöhung der Temperaturtoleranz ermöglichte genaue und zeitnahe Einblicke in den Zustand der Kultur. Weitere Ergebnisse weisen darauf hin, dass die Kombination mit Absorptionsspektren es ermöglichen können bessere Einblicke in die Vitalität der Kultur zu erhalten.
Dynamic interactions and their changes are at the forefront of current research in bioinformatics and systems biology. This thesis focusses on two particular dynamic aspects of cellular adaptation: miRNA and metabolites.
miRNAs have an established role in hematopoiesis and megakaryocytopoiesis, and platelet miRNAs have potential as tools for understanding basic mechanisms of platelet function. The thesis highlights the possible role of miRNAs in regulating protein translation in platelet lifespan with relevance to platelet apoptosis and identifying involved pathways and potential key regulatory molecules. Furthermore, corresponding miRNA/target mRNAs in murine platelets are identified. Moreover, key miRNAs involved in aortic aneurysm are predicted by similar techniques. The clinical relevance of miRNAs as biomarkers, targets, resulting later translational therapeutics, and tissue specific restrictors of genes expression in cardiovascular diseases is also discussed.
In a second part of thesis we highlight the importance of scientific software solution development in metabolic modelling and how it can be helpful in bioinformatics tool development along with software feature analysis such as performed on metabolic flux analysis applications. We proposed the “Butterfly” approach to implement efficiently scientific software programming. Using this approach, software applications were developed for quantitative Metabolic Flux Analysis and efficient Mass Isotopomer Distribution Analysis (MIDA) in metabolic modelling as well as for data management. “LS-MIDA” allows easy and efficient MIDA analysis and, with a more powerful algorithm and database, the software “Isotopo” allows efficient analysis of metabolic flows, for instance in pathogenic bacteria (Salmonella, Listeria). All three approaches have been published (see Appendices).
An essential topic for synthetic biologists is to understand the structure and function of biological processes and involved proteins and plan experiments accordingly. Remarkable progress has been made in recent years towards this goal. However, efforts to collect and present all information on processes and functions are still cumbersome. The database tool GoSynthetic provides a new, simple and fast way to analyse biological processes applying a hierarchical database. Four different search modes are implemented. Furthermore, protein interaction data, cross-links to organism-specific databases (17 organisms including six model organisms and their interactions), COG/KOG, GO and IntAct are warehoused. The built in connection to technical and engineering terms enables a simple switching between biological concepts and concepts from engineering, electronics and synthetic biology. The current version of GoSynthetic covers more than one million processes, proteins, COGs and GOs. It is illustrated by various application examples probing process differences and designing modifications.