Automatic Classification of the Bronchus Structures using a Rule-Based Approach

Brutenis Gliwa

Abstract: Knowledge of a patient’s bronchus structure is essential for surgery, for example, where lung segments have to be removed. However, the deep bronchus structures are hard for surgeons to identify by only viewing CT image slices. This thesis presents a pipeline, which automatically classifies the bronchus down to the tertiary bronchi using a rule-based approach. The bronchus classification with the lowest cost is quickly found by using Dijkstra’s algorithm to search over all possible classifications matching the rule set. The algorithm is applied to a data set of 76 segmented High Resolution Computed Tomography (HRCT) images provided by the Department of Thoracic Surgery at University Medicine Rostock. Furthermore, to analyse variations in the bronchus, the secondary bronchi were clustered into groups sharing the same deep structure. Finally, the pipeline was used to manually create classification ground truths for all patients up to the tertiary bronchi. The algorithm was evaluated on the created ground truth data, achieving a classification accuracy of 98.4% for the secondary bronchi and 84.2% for the tertiary bronchi. The annotated 3D models provided by the pipeline greatly enhance a surgeon’s ability for intuitive visualisation of the bronchus structure.

(Bachelors Thesis)



Functional Characterization of Long Non-Coding RNAs from Stem Cell Derived Cardiomyocyte Cell Types

Brauer DL

Bachelor thesis within the study degree of Medical Biotechnology

PhD theses

Improved imbalanced classification through convex space learning

Saptarshi Bej

Imbalanced datasets for classification problems, characterised by unequal distribution of samples, are abundant in practical scenarios. Oversampling algorithms generate synthetic data to enrich classification performance for such datasets. In this thesis, I discuss two algorithms LoRAS & ProWRAS, improving on the state-of-the-art as shown through rigorous benchmarking on publicly available datasets. A biological application for detection of rare cell-types from single-cell transcriptomics data is also discussed. The thesis also provides a better theoretical understanding behind oversampling.

Defense: 16 Dec. 2021

Customized Workflow Development and Omics Data Integration Concepts in Systems Medicine

Wolfien M

The ever-increasing amount and diversity of biological and medical data is a major challenge in computational analyses. Computational methods have to be combined into analysis workflows for seamless, swift, and transparent computation. In this work, numerous workflows were developed for the general processing of bulk RNA sequencing (RNA-Seq), single-cell sequencing experiments, and non-coding RNA identification. Mathematical concepts of machine learning and univariate meta-analyses were successfully implemented to independently investigate the role of cell therapies in cardiac regeneration.

Defense: 30. November 2020

Improving Reproducibility and Reuse of Modelling Results in the Life Sciences

Scharm M

Research results are complex and include a variety of heterogeneous data. This entails major computational challenges to (i) to manage simulation studies, (ii) to ensure model exchangeability, stability and validity, and (iii) to foster communication between partners. I describe techniques to improve the reproducibility and reuse of modelling results. First, I introduce a method to characterise differences in computational models. Second, I present approaches to obtain shareable and reproducible research results. Altogether, my methods and tools foster exchange and reuse of modelling results.

Defense: 30 August 2018

An investigation of microRNA target regulation mechanisms using an integrative approach

Schmitz U


Transcriptional Responses to Radiation Exposure Facilitate the Discovery of Biomarkers Functioning as Radiation Biodosimeters

Strunz S

The development of new methods for a retrospective quantification of the radiation dose of exposed individuals is of widespread interest. To this end, I developed a computational framework for biomarker discovery and radiation dose prediction and successfully identified gene signatures with which low and medium to high radiation doses can be accurately quantified. To enhance our understanding of the radiation-induced transcriptional response, I additionally analyzed microarray data of human PBLs after ex vivo gamma-irradiation and characterized affected functional processes and pathways.

Defense: 13 May 2014

Compartmental Modelling of the Wnt pathway

Schmitz Y

The Wnt pathway plays a critical role in development and disease. The key player of the pathway is b-cat. In the nucleus, the complex formation of b-cat and TCF initiates target gene expression. Its activity is mainly regulated by retention and degradation by its antagonists APC, Axin and GSK3. Based on experimental findings, I develop and investigate compartmental models in order to analyse of the role of nucleo-cytoplasmic shuttling of these proteins in Wnt signalling. I show that the compartmental separation of b-cat and its antagonists yields an increase of the b-cat/TCF concentration.

Defense: 19 December 2013

Characterisation of Signalling Pathways of pancreatic epithelial and cancer tissue using systems biology

Lange F


A Systems Biology Approach to Understand the Influence of SORLA on Amyloidogenic Processing in Alzheimer's Disease

Lao A

In this dissertation APP processing is modelled and the influence of SORLA in Alzheimer’s disease is evaluated. A model with both monomeric and dimeric APP in a single compartment best fitted the experimental data suggesting that SORLA prevents APP oligomerization and affects β-secretase indirectly. Including compartmental details into the combined model, the multi-compartmental model determines the relative contribution of SORLA to each APP-cleavage step showing that SORLA specifically impairs the processing of APP dimer and alters the dynamical behavior of beta-secretase.

Defense: 29 January 2013

Regulation of the general stress response of Bacillus subtilis

Liebal U

The bacterium Bacillus subtilis lives in the environmentally diverse soil habitat. Various environmental signals are sensed by the stressosome protein complex. Stimulation of the stressosome modifies its phosphorylation patterns and activates a signalling cascade that lead to protein expression changes. The thesis introduces models to study structural processes and signalling principles of the stressosome. Overall, the thesis enhances our understanding of the SigmaB-mediated general stress response and raises our awareness of the environmental integration of B. subtilis.

Defense: 4 February 2013

A systems biology approach to unravel the cellular function of microRNAs

Lai X


A systems biology approach to dynamic modelling of the AMP-activated kinase pathway

Frey S


Stochastic Modelling of Subcellular Biochemical Systems

Ullah M


Random set theory applied to the forecasting of stochastic point processes

Nuñez J


Master theses

Machine-Learning-Assisted Construction of a Disease-Specific Molecular Interaction Network from Human Blood RNA-Seq Data

Brauer DL

Master thesis within the study degree of Medical Biotechnology

Comparison of Deep Learning Approaches for Cardiomyocyte Evaluation

Maximilian Hillemanns


Arrhythmias are severe cardiac diseases and lethal if untreated. To serve as an in vitro drug testing option for anti-arrhythmic agents, cardiomyocytes and especially pacemaker cells are being generated in vitro from induced pluripotent stem cells (iPSCs). These generated cardiomyocytes resemble fetal cardiac tissue rather than adult cardiomyocytes. An automated tool for evaluations of cardiomyocytes would help the establishment of new generation protocols. In this work, a novel approach for this task is presented and evaluated.
Different convolutional neural networks (CNNs) including transfer models and native 2D and 3D models were trained on fluorescence images of cardiomyocytes, which were rated based on their sarcomerisation and the orientation of sarcomeres (directionality) beforehand. The CNNs were trained to perform classifications on sarcomerisation and directionality ratings and cell source, as cardiomyocytes from five different sources were used in this work. In this thesis, it could be shown that cellular fluorescence images can be analysed with CNNs. This  classifier can be used to make trustworthy predictions on the quality of a cardiomyocyte which will hopefully benefit the generation of cardiomyocytes from iPSCs. This classifier is currently being

Curation of an immune cell interactome and its analysis

Matti Hoch


Master thesis within the study degree of Medical Biotechnology

The interconnectivity of immune cells has been the subject of research numerously due to its importance in different diseases such as autoimmune defects, (microbial) infections and cancer. Various cell types have already been identified that are regulated by a complex network of cytokines and small molecules, of which many may not have been discovered yet. Therefore, it is of great interest to understand these mechanisms as they form the basis for drug development and therapy design. In this project, methods were described to create and analyze a cell interactome of molecular intra- and intercellular communication processes. Many molecular interaction maps (MIMs) have already been developed to evaluate molecular processes in certain diseases or cells. However, they either lacked essential information necessary for accurate modeling of cell-cell interactions or were poorly clinically assessed. Here, systems biology-based rules were defined to model the molecular pathways of intercellular interactions of cells in detail. By mapping expression data of immune cell samples, individual cellular MIMs were created automatically and validated by comparing the results with the current knowledge in the field of immunology. In addition to analyzing intracellular signaling pathways, intercellular communication processes were investigated by connecting the MIMs. The outcomes of this work improve the system biology modeling of molecular interaction networks and further provide the basis for the efficient development of complex intercellular networks to investigate biological and molecular processes in silico.

A Machine Learning approach to identify White Blood Cells

Mariam Nassar


Design of the TriplexRNA: a database of cooperating microRNAs and their mutual targets

Andrea Bagnacani

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Single miRNAs can repress the expression of many genes, however, experimental evidence suggest that their repressive effect alone is rather mild compared with that carried out by cooperating miRNAs. Although synergistic target regulation has been confirmed as a prevalent pattern in gene regulation, the mechanism behind this phenomena are little known, and it is unclear which miRNA pairs and gene targets are involved in the formation of RNA triplexes. The growing amount of data on miRNA-mediated gene expression is motivated by the role they play in cellular functions, where their presence can be correlated with cancers, and cardiovascular diseases, making them good biomarkers for diagnostic and prognostic purposes. However, existing databases relate disease associations with the duplex model: one miRNA inhibiting the expression of one or many genes, while none of them provide an exploration of the more effective regulation operated by synergistic pairs. To overcome this gap, the present work describes the design and implementation of the TriplexRNA: a database of cooperating miRNAs and their mutual targets, which we realised to enable researchers explore novel patterns in gene regulation. The database can be accessed at

Next Generation Sequencing Data Analysis of Stem Cell Derived Cardiomyocyte Cell Types

Markus Wolfien


M.Sc. within the study degree of Medical Biotechnology

Analysis of the carbon flow in response to iron supply in Clostridium acetobutylicum

Berger C


Effect of enzyme-complexes on the efficiency of metabolic pathways

Krey T


Cloud based analysis of Health Data with RESTful Web Services

Daniar R


Computational identification of ADAR editing sites in microRNA precursors

Blitti A


Adaptation in networks of heterogeneous mitochondrial populations

Arumugam U


Detection of potential drug targets in cancer signaling by mathematical modelling and optimization

Khan FM


Investigation of mammalian microRNA biogenesis by use of mathematical modeling

Freiesleben S


Stochastic Modelling and Simulations: An Extended Crossover method for modelling noise in stiff/bistable biochemical systems.

Asim Anees

Electrical Engineering, 2011

Data Mining and Clustering in Model Calibration for Systems Biology

Shah AF


Mathematical modelling and mathematical optimisation of biochemical systems: a method of analysis in biotechnology and biomedicine

Lai X


Development of a SBToolbox module to analyse Power-Law Models

Sun C


Significance Analysis for Microarray Data

Kutalik Z



Identification of activated signaling pathways in cardiac stem cell types by using network analysis

Matti Hoch


Bachelor thesis within the study degree of Medical Biotechnology

Visualising sub-networks in SBML models

Tom Gebhardt

further information at the SEMS Webpage.

Classifying reactions of computational models

Lambusch F

Further information at the SEMS webpage.

Downloads & more:

The thesis is available online (PDF).

Bestimmung von Distanzen zwischen Versionen XML - kodierter biologischer Modelle

Alm R


The impact of iron limitation on growth and product spectrum of Clostridium acetobutylicum

Berger C


Ansäuerungsleistung und Sauerstoffverbrauch von Säugetierzellen im extrazellulären Medium

Pauleweit S


Bifurkation analysis of the Hes1 oscillator

Oertel Y


Molecular Dynamics Simulation

Raasch P


Konzipierung und Erstellung eines Interfaces zum Export von Matlab SBmodel Modellen nach SBML

Drews G


Detektion charakteristischer Muster in Proteininteraktionsnetzwerken

Bittig A



Versioning Concepts and Technologies for Biochemical Simulation Models

Hälke R


Biological Aging, Negligible Senescence and the Concept of Renewal: A Complex Systems Approach by Memory Evolutive Systems

Haufschild M


Optimierung von Stimulusprofilen zellbiologischer Experimente durch mathematische Modellierung

Krause M


Simulation der Messung eines Cell Monitoring Systems (Modellbildung und Parameterschätzung)

Pauleweit S


A JAVA framework to integrate ncRNA detection methods

Raasch P


Erstellung eines Parameter Estimation Frameworks für die Systems Biology Toolbox for Matlab

Drews G


Combined Visualisation of Pathway and Protein-Protein Interaction Data

Bittig A


Zur Bestimmung sequenzabhängiger Zusammenhänge zwischen Transkriptom und Proteom

Weber I