Search

Auf die Entwicklung der Hochdurchsatz-Sequenzierung (HTS) folgte eine Reihe von speziellen Erweiterungen, die erlauben verschiedene zellbiologischer Aspekte wie Genexpression, DNA-Methylierung, etc. zu messen. Die Analyse dieser Daten erfordert die Entwicklung von Algorithmen, die einzelne Experimenteberücksichtigen oder mehrere Datenquellen gleichzeitig in betracht nehmen. Der letztere Ansatz bietet besondere Vorteile bei Analyse von einzelligen RNA-Sequenzierung (scRNA-seq) Experimenten welche von besonders hohem technischen Rauschen, etwa durch den Verlust an Molekülen durch die Behandlung geringer Ausgangsmengen, gekennzeichnet sind. Um diese experimentellen Defizite auszugleichen, habe ich eine Methode namens netSmooth entwickelt, welche die scRNA-seq-Daten entrascht und fehlende Werte mittels Netzwerkdiffusion über ein Gennetzwerk imputiert. Das Gennetzwerk reflektiert dabei erwartete Koexpressionsmuster von Genen. Unter Verwendung eines Gennetzwerks, das aus Protein-Protein-Interaktionen aufgebaut ist, zeige ich, dass netSmooth anderen hochmodernen scRNA-Seq-Imputationsmethoden bei der Identifizierung von Blutzelltypen in der Hämatopoese, zur Aufklärung von Zeitreihendaten unter Verwendung eines embryonalen Entwicklungsdatensatzes und für die Identifizierung von Tumoren der Herkunft für scRNA-Seq von Glioblastomen überlegen ist. netSmooth hat einen freien Parameter, die Diffusionsdistanz, welche durch datengesteuerte Metriken optimiert werden kann. So kann netSmooth auch dann eingesetzt werden, wenn der optimale Diffusionsabstand nicht explizit mit Hilfe von externen Referenzdaten optimiert werden kann. Eine integrierte Analyse ist auch relevant wenn multi-omics Daten von mehrerer Omics-Protokolle auf den gleichen biologischen Proben erhoben wurden. Hierbei erklärt jeder einzelne dieser Datensätze nur einen Teil des zellulären Systems, während die gemeinsame Analyse ein vollständigeres Bild ergibt. Ich entwickelte eine Methode namens maui, um eine latente Faktordarstellungen von multiomics Daten zu finden., The development of high throughput sequencing (HTS) was followed by a swarm of protocols utilizing HTS to measure different molecular aspects such as gene expression (transcriptome), DNA methylation (methylome) and more. This opened opportunities for developments of data analysis algorithms and procedures that consider data produced by different experiments. Considering data from seemingly unrelated experiments is particularly beneficial for Single cell RNA sequencing (scRNA-seq). scRNA-seq produces particularly noisy data, due to loss of nucleic acids when handling the small amounts in single cells, and various technical biases. To address these challenges, I developed a method called netSmooth, which de-noises and imputes scRNA-seq data by applying network diffusion over a gene network which encodes expectations of co-expression patterns. The gene network is constructed from other experimental data. Using a gene network constructed from protein-protein interactions, I show that netSmooth outperforms other state-of-the-art scRNA-seq imputation methods at the identification of blood cell types in hematopoiesis, as well as elucidation of time series data in an embryonic development dataset, and identification of tumor of origin for scRNA-seq of glioblastomas. netSmooth has a free parameter, the diffusion distance, which I show can be selected using data-driven metrics. Thus, netSmooth may be used even in cases when the diffusion distance cannot be optimized explicitly using ground-truth labels. Another task which requires in-tandem analysis of data from different experiments arises when different omics protocols are applied to the same biological samples. Analyzing such multiomics data in an integrated fashion, rather than each data type (RNA-seq, DNA-seq, etc.) on its own, is benefitial, as each omics experiment only elucidates part of an integrated cellular system. The simultaneous analysis may reveal a comprehensive view.

A deep learning approach refines the state-of-the-art subtypes of colorectal cancer and evaluates the fidelity of cell lines that model cancer., Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. To maximize the translatability and clinical relevance of in vitro studies, the selection of optimal cancer models is imperative. We have developed a deep learning–based method to measure the similarity between CRC tumors and disease models such as cancer cell lines. Our method efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions. These latent factors represent the patterns that are clinically relevant and explain the variability of molecular profiles across tumors and cell lines. Using these, we propose refined CRC subtypes and provide best-matching cell lines to different subtypes. These findings are relevant to patient stratification and selection of cell lines for early-stage drug discovery pipelines, biomarker discovery, and target identification.

This thesis was set to tackle the gas coning problem in oil-rim reservoirs with horizontal wells. The focus was short term production planning in the sub-critical phase only. Different controllers were developed and assessed based on the objective of net-present-value (NPV) of oil produced in the sub-critical phase. The reservoir model is a 1-D partial differential equation describing the dynamics of the gas-oil contact (GOC), for a homogeneous reservoir. Gas coning is considered to be the deformation of the GOC towards the well. Several controllers were developed and assessed alongside control laws from previous research: (i) the Backstepping method was used to develop a state-feedback controller, along with an observer. Coupled they make the Backstepping output-feedback controller. (ii) an output-feedback controller based on the structure proposed by previous research. (iii) linear-quadratic optimal control. An extended Kalman filter was also considered as a state observer, alongside the Backstepping observer. The backstepping controller did not deliver an increase of sub-critical payout which warrants the complicated structure of an observer-controller pair. It was even outperformed by simpler, output-feedback control laws. The optimal linear-quadratic controller achieved the best NPV of sub-critical production by far. This makes it the most attractive control strategy presented, even when considering that a real-world implementation will need to be paired with a state observer.

Epstein-Barr virus (EBV) is a B cell transforming virus that causes B cell malignancies under conditions of immune suppression. EBV orchestrates B cell transformation through its latent membrane proteins (LMPs) and Epstein-Barr nuclear antigens (EBNAs). We here identify secondary mutations in mouse B cell lymphomas induced by LMP1, to predict and identify key functions of other EBV genes during transformation. We find aberrant activation of early B cell factor 1 (EBF1) to promote transformation of LMP1-expressing B cells by inhibiting their differentiation to plasma cells. EBV EBNA3A phenocopies EBF1 activities in LMP1-expressing B cells, promoting transformation while inhibiting differentiation. In cells expressing LMP1 together with LMP2A, EBNA3A only promotes lymphomagenesis when the EBNA2 target Myc is also overexpressed. Collectively, our data support a model where proproliferative activities of LMP1, LMP2A, and EBNA2 in combination with EBNA3A-mediated inhibition of terminal plasma cell differentiation critically control EBV-mediated B cell lymphomagenesis.