Probabilistic reasoning with a bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes? Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro

Probes, hardware and software for next-generation super-resolution microscopy

Super-resolution microscopy enables optical imaging using fluorescence probes below the diffraction limit. In stochastic super-resolution microscopy, molecules are „switched“ between non-fluorescent dark-state (OFF-state) and fluorescent bright-state (ON-state) in order to pinpoint their position with sub-diffraction precision. The most prominent techniques of localization-based super-resolution microscopy are photo-activated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM). Here, the switching between dark- and bright-state is accomplished using photophysical or photochemical processes. A recently introduced super-resolution microscopy method called DNA-PAINT (deoxyribonucleic acid - point accumulation for imaging in nanoscale topography) is based on DNA-DNA interaction. In contrast to STORM or PALM, the fluorescence molecules do not switch between dark and bright states. The so-called „blinking“ in DNA-PAINT is created by transient hybridization of short fluorescent DNA strands (imagers) to their targets. The work in this dissertation focuses on three different advancements in the technological aspect of super-resolution microscopy. Probes In the first project of this thesis, I demonstrate the combination of single-molecule Förster resonance energy transfer (FRET) with DNA-PAINT imaging to overcome some current limitations of the DNA-based super-resolution microscopy. I evaluate the novel probe design with in vitro experiments using DNA nanostructures and prove the performance of the FRET-based probes in a cellular context. Hardware In the second project, I describe a cost-efficient single-molecule microscope platform, which is an order of magnitude more affordable, while still yielding high-performance imaging capacity. Using two-dimensional (2D) and three-dimensional (3D) super-resolution in vitro experiments using DNA nanostructures, I asses the performance of the microscopy platform. Finally, I present exemplary experiments for multiplexed cellular imaging. Software In the last project, I present a software package that is developed to assist during super-resolution data analysis. It is based on the deep learning concept of the artificial neural network (ANN) and designed to automate the classification of nano-scaled patterns found in super-resolution images. I evaluate the performance of the software package using super-resolution in vitro experiments of DNA nanostructures as well as targets in cellular samples.Die superauflösende Mikroskopie ermöglicht die optische Abbildung mittels Fluoreszenzsonden unterhalb der Beugungsgrenze. In stochastischen Superauflösungsmikroskopie werden Moleküle zwischen dem nicht-fluoreszierenden Zustand (OFF-Zustand) und dem fluoreszierenden Zustand (ON-Zusstand) “geschaltet“, um ihre Position präziser als die Beugungsgrenze zu bestimmen. Die bekanntesten Mikroskopietechniken der lokalisationsbasierten Superauflösungsmikroskopie sind photo-activated localization microscopy (PALM) und stochastic optical reconstruction microscopy (STORM). Hier wird die Umschaltung zwischen Dunkel- und Hellzustand mithilfe photophysikalischer oder photochemischer Prozesse durchgeführt. Eine kürzlich eingeführte Methode der Superauflösungsmikroskopie namens DNA-PAINT (deoxyribonucleic acid - point accumulation for imaging in nanoscale topography) basiert auf der DNA-DNA Wechselwirkung. Im Vergleich zu STORM oder PALM wechseln die Fluoreszenzmoleküle nicht zwischen dem dunklen und dem hellen Zustand. Das sogenannte “Blinken“ in DNA-PAINT wird durch transiente Hybridisierung kurzer fluoreszierender DNA Stränge (Imager) an ihre Ziele erzeugt. Die Arbeiten in dieser Dissertation konzentriert sich auf drei unterschiedliche Fortschritte im technologischen Aspekt der Superauflösungsmikroskopie. Sonden Im ersten Projekt dieser Arbeit zeige ich die Kombination von Einzelmolekül-Förster-Resonanzenergietransfer (englisch Förster resonance energy transfer (FRET)) mit DNA-PAINT Mikroskopie, um einige aktuelle Einschränkungen der DNA basierten Superauflösungsmikroskopie zu überwinden. Ich evaluiere das neuartige Sondendesign mithilfe von in vitro Experimenten mit DNA nanostructure und zeige die Leistungsfähigkeit der FRET-basierten Sonden im zellulären Kontext. Hardware Im zweiten Projekt beschreibe ich eine kosteneffiziente Mikroskop-Plattform für Einzelmolekülstudien, die um eine Größenordnung erschwinglicher ist und dennoch eine leistungsstarke Abbildungsfähigkeit bietet. Unter Verwendung von zweidimensionalen (2D) und dreidimensionalen (3D) in vitro Superauflösungsexperimenten von DNA Nanostrukturen bewerte ich die Leistung der Mikroskopie-Plattform. Schließlich zeige ich exemplarische Experimente für die zelluläre Bildgebung in mehreren Farben. Software Im letzten Projekt stelle ich ein Softwarepaket vor, das zur Unterstützung der Analyse von Daten in Superauflösungsmikroskopie entwickelt wurde. Es basiert auf dem Konzept des tiefen Lernens (englisch deep learning) mithilfe von künstlichen neuronalen Netzen und wurde entwickelt, um die Klassifikation von nanoskaligen Mustern zu automatisieren, die in superaufgelösten Bildern zu finden sind. Ich evaluiere die Leistung des Softwarepakets anhand von in vitro Superauflösungsexperimenten von DNA Nanostrukturen sowie von in Zellproben

Probes, hardware and software for next-generation super-resolution microscopy

Super-resolution microscopy enables optical imaging using fluorescence probes below the diffraction limit. In stochastic super-resolution microscopy, molecules are „switched“ between non-fluorescent dark-state (OFF-state) and fluorescent bright-state (ON-state) in order to pinpoint their position with sub-diffraction precision. The most prominent techniques of localization-based super-resolution microscopy are photo-activated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM). Here, the switching between dark- and bright-state is accomplished using photophysical or photochemical processes. A recently introduced super-resolution microscopy method called DNA-PAINT (deoxyribonucleic acid - point accumulation for imaging in nanoscale topography) is based on DNA-DNA interaction. In contrast to STORM or PALM, the fluorescence molecules do not switch between dark and bright states. The so-called „blinking“ in DNA-PAINT is created by transient hybridization of short fluorescent DNA strands (imagers) to their targets. The work in this dissertation focuses on three different advancements in the technological aspect of super-resolution microscopy. Probes In the first project of this thesis, I demonstrate the combination of single-molecule Förster resonance energy transfer (FRET) with DNA-PAINT imaging to overcome some current limitations of the DNA-based super-resolution microscopy. I evaluate the novel probe design with in vitro experiments using DNA nanostructures and prove the performance of the FRET-based probes in a cellular context. Hardware In the second project, I describe a cost-efficient single-molecule microscope platform, which is an order of magnitude more affordable, while still yielding high-performance imaging capacity. Using two-dimensional (2D) and three-dimensional (3D) super-resolution in vitro experiments using DNA nanostructures, I asses the performance of the microscopy platform. Finally, I present exemplary experiments for multiplexed cellular imaging. Software In the last project, I present a software package that is developed to assist during super-resolution data analysis. It is based on the deep learning concept of the artificial neural network (ANN) and designed to automate the classification of nano-scaled patterns found in super-resolution images. I evaluate the performance of the software package using super-resolution in vitro experiments of DNA nanostructures as well as targets in cellular samples.Die superauflösende Mikroskopie ermöglicht die optische Abbildung mittels Fluoreszenzsonden unterhalb der Beugungsgrenze. In stochastischen Superauflösungsmikroskopie werden Moleküle zwischen dem nicht-fluoreszierenden Zustand (OFF-Zustand) und dem fluoreszierenden Zustand (ON-Zusstand) “geschaltet“, um ihre Position präziser als die Beugungsgrenze zu bestimmen. Die bekanntesten Mikroskopietechniken der lokalisationsbasierten Superauflösungsmikroskopie sind photo-activated localization microscopy (PALM) und stochastic optical reconstruction microscopy (STORM). Hier wird die Umschaltung zwischen Dunkel- und Hellzustand mithilfe photophysikalischer oder photochemischer Prozesse durchgeführt. Eine kürzlich eingeführte Methode der Superauflösungsmikroskopie namens DNA-PAINT (deoxyribonucleic acid - point accumulation for imaging in nanoscale topography) basiert auf der DNA-DNA Wechselwirkung. Im Vergleich zu STORM oder PALM wechseln die Fluoreszenzmoleküle nicht zwischen dem dunklen und dem hellen Zustand. Das sogenannte “Blinken“ in DNA-PAINT wird durch transiente Hybridisierung kurzer fluoreszierender DNA Stränge (Imager) an ihre Ziele erzeugt. Die Arbeiten in dieser Dissertation konzentriert sich auf drei unterschiedliche Fortschritte im technologischen Aspekt der Superauflösungsmikroskopie. Sonden Im ersten Projekt dieser Arbeit zeige ich die Kombination von Einzelmolekül-Förster-Resonanzenergietransfer (englisch Förster resonance energy transfer (FRET)) mit DNA-PAINT Mikroskopie, um einige aktuelle Einschränkungen der DNA basierten Superauflösungsmikroskopie zu überwinden. Ich evaluiere das neuartige Sondendesign mithilfe von in vitro Experimenten mit DNA nanostructure und zeige die Leistungsfähigkeit der FRET-basierten Sonden im zellulären Kontext. Hardware Im zweiten Projekt beschreibe ich eine kosteneffiziente Mikroskop-Plattform für Einzelmolekülstudien, die um eine Größenordnung erschwinglicher ist und dennoch eine leistungsstarke Abbildungsfähigkeit bietet. Unter Verwendung von zweidimensionalen (2D) und dreidimensionalen (3D) in vitro Superauflösungsexperimenten von DNA Nanostrukturen bewerte ich die Leistung der Mikroskopie-Plattform. Schließlich zeige ich exemplarische Experimente für die zelluläre Bildgebung in mehreren Farben. Software Im letzten Projekt stelle ich ein Softwarepaket vor, das zur Unterstützung der Analyse von Daten in Superauflösungsmikroskopie entwickelt wurde. Es basiert auf dem Konzept des tiefen Lernens (englisch deep learning) mithilfe von künstlichen neuronalen Netzen und wurde entwickelt, um die Klassifikation von nanoskaligen Mustern zu automatisieren, die in superaufgelösten Bildern zu finden sind. Ich evaluiere die Leistung des Softwarepakets anhand von in vitro Superauflösungsexperimenten von DNA Nanostrukturen sowie von in Zellproben

Multi-input distributed classifiers for synthetic genetic circuits

For practical construction of complex synthetic genetic networks able to perform elaborate functions it is important to have a pool of relatively simple "bio-bricks" with different functionality which can be compounded together. To complement engineering of very different existing synthetic genetic devices such as switches, oscillators or logical gates, we propose and develop here a design of synthetic multiple input distributed classifier with learning ability. Proposed classifier will be able to separate multi-input data, which are inseparable for single input classifiers. Additionally, the data classes could potentially occupy the area of any shape in the space of inputs. We study two approaches to classification, including hard and soft classification and confirm the schemes of genetic networks by analytical and numerical results

Laser Based Mid-Infrared Spectroscopic Imaging – Exploring a Novel Method for Application in Cancer Diagnosis

A number of biomedical studies have shown that mid-infrared spectroscopic images can provide both morphological and biochemical information that can be used for the diagnosis of cancer. Whilst this technique has shown great potential it has yet to be employed by the medical profession. By replacing the conventional broadband thermal source employed in modern FTIR spectrometers with high-brightness, broadly tuneable laser based sources (QCLs and OPGs) we aim to solve one of the main obstacles to the transfer of this technology to the medical arena; namely poor signal to noise ratios at high spatial resolutions and short image acquisition times. In this thesis we take the first steps towards developing the optimum experimental configuration, the data processing algorithms and the spectroscopic image contrast and enhancement methods needed to utilise these high intensity laser based sources. We show that a QCL system is better suited to providing numerical absorbance values (biochemical information) than an OPG system primarily due to the QCL pulse stability. We also discuss practical protocols for the application of spectroscopic imaging to cancer diagnosis and present our spectroscopic imaging results from our laser based spectroscopic imaging experiments of oesophageal cancer tissue

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics

Systematic discovery of linear binding motifs targeting an ancient protein interaction surface on MAP kinases

Mitogen-activated protein kinases (MAPK) are broadly used regulators of cellular signaling. However, how these enzymes can be involved in such a broad spectrum of physiological functions is not understood. Systematic discovery of MAPK networks both experimentally and in silico has been hindered because MAPKs bind to other proteins with low affinity and mostly in less-characterized disordered regions. We used a structurally consistent model on kinase-docking motif interactions to facilitate the discovery of short functional sites in the structurally flexible and functionally under-explored part of the human proteome and applied experimental tools specifically tailored to detect low-affinity protein-protein interactions for their validation in vitro and in cell-based assays. The combined computational and experimental approach enabled the identification of many novel MAPK-docking motifs that were elusive for other large-scale protein-protein interaction screens. The analysis produced an extensive list of independently evolved linear binding motifs from a functionally diverse set of proteins. These all target, with characteristic binding specificity, an ancient protein interaction surface on evolutionarily related but physiologically clearly distinct three MAPKs (JNK, ERK, and p38). This inventory of human protein kinase binding sites was compared with that of other organisms to examine how kinase-mediated partnerships evolved over time. The analysis suggests that most human MAPK-binding motifs are surprisingly new evolutionarily inventions and newly found links highlight (previously hidden) roles of MAPKs. We propose that short MAPK-binding stretches are created in disordered protein segments through a variety of ways and they represent a major resource for ancient signaling enzymes to acquire new regulatory roles