Seminars

Seminars

Current Research in Systems Biology and Bioinformatics

Seminars are normally presented on TUESDAYs at 13:00 in Ulmenstr. 69, Building 3 (Haus 3), Room 410 (SR410). Be aware that we choose fitting dates for our guest speakers. Seminars are a mix of presentations from PhD students and academic visitors. If you are willing to attend the research seminar from outside Rostock, please contact Julia Scheel or Saptarshi Bej (Tel.: 0381-498-7575; Email: julia.scheel@uni-rostock.de, saptarshi.bej@uni-rostock.de) prior to the announced seminar. The seminar is co-organised with the group of Prof. Georg Fuellen, University Medicine Rostock.

Seminars in our group are structured into two types of talks:

  • Research Presentation

Members and students of our research group present their work and the talk is strictly limited to 20 minute presentation plus 10 minute discussion. A typical structure for the presentation should follow the outline: What's the context? What's the question asked? Why is it exciting? What has been done? What are the results, conclusions and achievements.

  • Research Discussion

The aim of this kind of talk is to stir up discussions about a topic of interest, not necessarily limited to a certain project. The talk is limited to 10 minutes only with up to 30 minutes for discussion and debate.
For more information concerned by the speakers, please see here.

* t.b.a. = to be announced

Winter semester 2019/2020

, Ulmencampus - Building 3 - Room 410

Olaf Wolkenhauer - Successful writing of grant proposals

In this seminar I would like to share my experiences as an applicant and reviewer for DFG, BMBF and EU applications.
 
My experience is necessarily limited and subjective. I would not even claim to be particularly successful myself. The spirit of this seminar is to share what “I wish someone would have told me that.”
 
The target audience are postdocs, young professors and PhD students in the natural sciences. The format of the seminar is not a lecture, but a discussion with hopefully many questions from the participants.
 
We are trained to generate scientific results but we are not told how to communicate our work effectively. To help myself and others, I analysed grant proposals as a particular form of communication shares elements with other forms, including publications, posters, websites etc. Once we realise this, we can identify guiding principles that help us not only to structure and write a grant proposal but, in the process, also improve our actual research through the writing of a grant proposal.
 
Why do I offer this seminar? (i) Because I find it difficult to write successful applications. (ii) Because I was elected to the DFG review panel Fundamentals of Medicine and Biology, and I can reward the trust I have received in this way. (iii) Because as a young scientist I would have liked to get help for this important part of my work.

, Ulmencampus - Building 3 - Room 410

Krithika Sayar Chand - Advanced Image Analysis with Deep Learning

The risk of chronic diseases is directly related to one’s diet. There has been a growing need for a smart dietary assessment system that can help people to keep track of their
food consumption. The first step towards development of such a system is the recognition of the food item and estimation of its volume of from its images. The state-ofthe-art techniques to find the volume of food item from its images requires the presence of a reference calibration object to be placed next to the food item. Other techniques
involve creating a 3D reconstruction of the food item from multiview images to find its volume and in some approaches complex camera systems such as multiple cameras, stereo cameras or depth cameras are used. In this thesis, we adopt a different approach where the presence of a calibration object is not required, and the 3D reconstruction of the food item is not done explicitly. The factors that influences the 3D reconstruction such as multi-view images and the change in position of the camera are taken as inputs to a supervised deep learning model. The sequence of images is taken using a simple smartphone camera and the change in the position of the smart phone while capturing the sequence of images is obtained from the IMU sensors of the smartphone. Objects in the images are classified using state-of-the-art deep neural networks for image classification. An end-to-end deep neural network model is implemented that takes in the multi-view images and the IMU sensor measurements as inputs, fuses them together to estimate the volume. LSTMs are used to learn the sequential nature of the inputs.

Experimental results demonstrate that the accuracy of image classification achieved is 99.23%. The error analysis of volume predictions show that image features and the
quantity of dataset played an important role in the performance of the models. The volume prediction results improved when the image features were better represented in
the deep learning model.

, Ulmencampus - Building 3 - Room 410

Aurelia Bustos - Learning Eligibility in Cancer Clinical Trials Using Deep Neural Networks

Interventional cancer clinical trials are generally too restrictive, and some patients are often excluded on the basis of comorbidity, past or concomitant treatments, or the fact that they are over a certain age. The efficacy and safety of new treatments for patients with these characteristics are, therefore, not defined. In this work, we built a model to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. We used protocols from cancer clinical trials that were available in public registries from the last 18 years to train word-embeddings, and we constructed a dataset of 6M short free-texts labeled as eligible or not eligible. A text classifier was trained using deep neural networks, with pre-trained word-embeddings as inputs, to predict whether or not short free-text statements describing clinical information were considered eligible. We additionally analyzed the semantic reasoning of the word-embedding representations obtained and were able to identify equivalent treatments for a type of tumor analogous with the drugs used to treat other tumors. We show that representation learning using deep neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols for potentially assisting practitioners when prescribing treatments.

, Ulmencampus - Building 3 - Room 410

Andres Torrubia Sáez - Kaggle Molecular Competiton Abstract

Kaggle recently hosted a competition organized by the members of the CHemistry and Mathematics in Phase Space (CHAMPS) at the University of Bristol, Cardiff University, Imperial College and the University of Leeds to predict the magnetic interaction between two atoms in a molecule (i.e., the scalar coupling constant).  While it is possible to accurately calculate scalar coupling constants given only a 3D molecular structure as input using quantum mechanics calculations, these calculations are extremely expensive (days or weeks per molecule), and therefore have limited applicability in day-to-day workflows. We developed a state-of-the-art solution using deep neural networks which achieved second place amongst 2700+ competitors worldwide with a score of -3.22 (LMAE averaged amongst 8 j-coupling types) using an ensemble of 8 models, and the best single model performance across all teams with a score of -3.16 (LMAE), running inference in less than 20 milliseconds per molecule.

Summer semester 2019

, Ulmencampus - Building 3 - Room 410

Sarah Fischer: Lung-SQUAD - First steps into Deep learning with Lung cancer

"From the 9th till 13-09-2019 September I participated with my team „LUNG-SQUAD“ on the Deep Learning Hackathon in Dresden. We started to work on different ideas about the development of machine learning especially deep learning approaches for an integrative omics analysis and I would like to present to you the preliminary results and discuss the future path of the project."

, Ulmencampus - Building 3 - Room 410

Shailendra Gupta: Atlas of Inflammation Resolution (AIR): a comprehensive resource connecting inflammation community

Acute inflammation is the first protective response by the host tissue against invading pathogens and/or injury. It is a highly coordinated, active, nonlinear spatio-temporal process for the removal of invading pathogens and the repair of damaged tissues to reestablish homeostasis. The whole acute inflammatory landscape can be broadly divided into 4 phases, namely, inflammation initiation; transition, resolution and finally homeostasis. The boundaries of all these phases are fuzzy and contain large number of regulators (molecular switches) in the form of feedback and feedforward loops, making the whole system extremely complex and dynamic.

Currently, there is no common web platform for the community to share their viewpoints on the emergence of acute inflammatory phenotypes. We conceptualized Atlas of Inflammation Resolution (AIR) as a community resource to connect clinicians, research scientists, pharmaceutical companies working in the area of acute inflammation and inflammation resolution. This can be realized only when AIR is able to support 1) clinicians in decision making (e.g. patient stratification; therapy response; therapy personalization), 2) research scientists in the development of hypotheses for experimental design (e.g. identification of molecular switches, mapping of high-throughput experimental data), and 3) pharmaceutical companies in the identification of new therapeutic checkpoints.

In the research seminar, I will present the current status and the live demonstration of various features available with the AIR to support inflammation community.

, Ulmencampus - Building 3 - Room 410

Skye Chan - INFLAWAT Project Network Analysis Results

Adipose tissue inflammation may be closely related to diseases such as type II diabetes. Its mechanism behind is worth investigating. In this project, through various network analysis methods, which are ClueGO analysis, centrality analysis and network analysis, experimental data were analysed in terms of genetic ontology and interaction networks. The latter yields particularly promising results, with the proteins Rps27a and Tnfrfs1b, the latter of which interacting with Plaur, being worth further investigation.

, Ulmencampus - Building 3 - Room 410

Stefan Siegmund: Applied Mathematics and Beyond

In this overview talk we present research results on bone remodeling and transient dynamical behavior. These problems are also used as examples for a discussion of the role of consciousness in research.

, Ulmencampus - Building 3 - Room 410

Matti Hoch: Curation of an Immune Cell Interactome and its Analysis

The interconnectivity of immune cells is the subject of extensive research for its importance in many different diseases like autoimmune defects, (microbial) infections and cancer. Many cell types have already been identified which are regulated through a complex network of cytokines and small molecules from which many may have not been discovered yet. It is therefore of great interest to understand these mechanisms as they provide the basis for targeted drug development. This project investigates the methodology for creating and analyzing immune system interactomes using cell type clustered networks based on transcriptomics data.

, Ulmencampus - Building 3 - Room 410

Dr Adrian E. Granada: Single-cells dynamics - from the circadian clock to proliferation and cell fate under chemotherapy

The circadian clock coordinates daily physiological, metabolic and behavioral rhythms. In mammals, the suprachiasmatic nucleus (SCN) has been identified as the master circadian clock. The SCN orchestrates circadian rhythms in peripheral tissues and, thereby, generate a synchronized circadian output in physiology and behavior. Little is known about the mechanisms that determine this hierarchy and the central role played by the SCN. In the first part of this talk, I will present our collaborative endeavors to integrate mathematical modeling with single-cell live recordings to gain new insight and quantitatively predictive understanding of the properties that determine the observed hierarchy. In the second part of my talk I will focus on the heterogeneous responses of cancer cells to chemotherapy. In particular, I will show how non-genetic sources of heterogeneity can lead to large cell-to-cell response variability. Our results show how two intertwined cellular features present in every cell have distinct control of cell fate choices in response to the chemotherapeutic drug cisplatin. Contrary to tumor observations, we find that in dividing cells higher proliferation activity increases resistance and that cell cycle state determines the arrest likelihood of the surviving population. These findings show how cellular states shape the individual cell choices that collectively determine the overall response to therapy.

, Ulmencampus - Building 3 - Room 410

Clarisse de Vries - Aberdeen Biomedical Imaging Centre and Braing Ageing

Clarisse de Vries is a PhD student at the Aberdeen Biomedical Imaging Centre in Aberdeen, Scotland. She is part of the Brain Ageing research group, which focusses on the relationship between life-course variables and brain health later in life. She will discuss the rich Aberdonian datasets (the Aberdeen Birth Cohorts of 1921 and 1936, and the Aberdeen Children of the 1950s), which contain late-life brain MRI scans, childhood and adult cognition, and various other early and late life health-related and socioeconomic variables. In addition, she will give an overview of her group's research directions, and some of the computational techniques employed to investigate healthy brain ageing. Finally, she will discuss her work on the klotho (or longevity) gene, and her research on brain entropy, a possible measure of brain complexity extracted from MRI derived brain activations.

, Ulmencampus - Building 3 - Room 410

Maximilian Hillemanns: tbd

Our research student Max Hillemanns will present his work with the SBI group.

, Ulmencampus - Building 3 - Room 410

Prof. Jan Hasenauer - tbd

Prof. Jan Hasenauer from the University Bonn is visiting us and will present during our research seminar. 

, Ulmencampus - Building 3 - Room 410

Dr. Mohamed Elhadidi: tbd

, Ulmencampus - Building 3 - Room 410

Faiz Muhammad Khan: An integrative workflow to study large-scale biochemical networks

Abstract:

The recent development in quantitative measurements and access to interaction databases facilitate the construction of detailed molecular interaction maps of cellular processes. Such networks serve as a knowledge-base and being machine readable are amenable to computational analysis. Studying biochemical networks as a non-linear dynamical system is challenging due to a large number of components and complex network structures including feedback/feedforward loops.


In this thesis, I proposed an integrative workflow based on multi-objective optimization function to study large-scale biochemical networks by combining techniques from bioinformatics and systems biology. It integrates heterogeneous sources of biological information with network structure and dynamical systems analysis to unravel mechanisms underlying diseases. To understand the functional role of the transcription factor E2F1 in different traits of cancer, such as drug resistance and epithelial-mesenchymal transition (EMT), we constructed a comprehensive molecular interaction map. The network contains 1,015 nodes and 4,180 interactions. Further, the network topological analysis revealed a large number of feedback and feedforward loops. To make such large, complex network suitable for dynamical systems analysis, the proposed workflow combined its topological properties with high-throughput and biomedical data to identify disease phenotype specific modules, which I refer to as “core-regulatory” networks. Such core networks are smaller compared to parent (large) networks and are amenable for analysis with dynamical systems theory. Using the proposed workflow, I identified core-regulatory networks from the E2F1 map underlying EMT in bladder and breast cancer. I carried out dynamical analyses of the core networks using logic-based models. Using in silico stimulus-response and perturbation experiments, molecular signatures and potential targets were detected for each cancer type. The in silico predictions were validated with patient data and through in vitro experiments.

Moreover, I developed a hybrid modeling framework that combines ordinary differential equation models with logic-based models as a strategy to analyze the dynamics of large-scale non-linear biological systems. Using the proposed hybrid modeling strategy, I simulated the known dynamical features of the E2F1-p73/DNp73-miR205 network in drug resistance for different concentrations of the transcription factor E2F1 and receptor molecules. Further, the results of my model analyses suggest that cancer cells might become independent of growth factors when E2F1 is highly expressed.

This thesis is a contribution to interdisciplinary cancer research, providing a methodology for the analysis of large-scale networks in molecular and cell biology.

, Ulmencampus - Building 3 - Room 410

Andrea Bagnacani - Rethinking Galaxy workflows for self-training of Life Science data scientists

Abstract: Shared Galaxy workflows facilitate the dissemination of best-practices for the computational analysis of scientific
experiments. Manually-curated interactive tours explaining each tool of a workflow support the self-training of Life Science data scientists. However, workflow tools are usually pre-selected, and little to no information is conveyed about alternative tools. In this seminar I'll showcase a Galaxy-based system whose design overcomes the aforementioned limitations, by offering and explaining alternative approaches for the selected analysis. This system allows to set up more comprehensive and custom-tailored workflows, and significantly reduces the curatorial effort to maintain interactive tours.

, Ulmencampus - Building 3 - Room 410

Michael Crusoe - About the Common Workflow Language and its workflows

Michael Crusoe was invited as a guest speaker by IBIMA.

, Ulmencampus - Building 3 - Room 410

Julia Scheel - How to Prepare a Course

Our PhD student Julia presented what she has learned about education and course preparation with Open Foster and the Carpentries. 

Winter semester 2018/2019

Reverse Docking

Steffen Möller

PRI-mel groundwork and shadow-sampling

Saptarshi Bej

Machine learning (ML) and deep learning techniques can guide therapeutic decision making by learning pattern from existing patient data. However, to train such models reliably we need a large pool of data, which is often unavailable in light of the patient numbers, effort and the costs to generate such data. The lack of training data does not allow us to reliably validate the model which makes it unsafe to be used practically in clinics . We developed a new method of data augmentation called Random Affine Combination Shadow-sampling (RACoS). From a small amount of data, we can create a large number of RACoS samples that capture the pattern present in the data. We then train our machine learning models with the RACoS data. We observed from our initial studies that our approach provides the ML models with a better experience of the data, compared to training the models with small amounts of data. We also used RCoS sampling to quantify the overfitting tendency of the model even if there is not enough data for validation. Since the RACoS samples provide the ML models with a better experience of the data, we are now developing a new learning methodology, based on one-shot learning.

How Quercetin may affect health and disease.

Towards the Draft Genome Sequence and Annotation of Sander lucioperca (Pike-perch)

Julien A. Nguinkal from the Leibniz institute for farm animals will speak about:

The Percidae family is a diverse and economically relevant group of mostly freshwater
fishes that comprises 11 genera and about 266 identified species. Many of these species play
key roles in aquatic ecosystems and provide valuable resources for acquafarming. Pike-perch
(Sander lucioperca, Linnaeus, 1758) is considered as one of the high emerging species for
intensive aquaculture production in Europe, because of its delicate meat, mild taste and diversification
potential. Therefore, considerable efforts are being made to increase and optimize
its industrial production. However, there is a tremendous lack of genomic resources indispensable
to study genetic traits and variations, that are of high relevance in domestication and
adaptation of Sander spp. to aquaculture environments. In particular, there is a need of high
quality assembled and annotated draft genome as reference basis for addressing questions in
Percidae research in general, and specifically, to understand and enhance aquaculture production
and performance traits of Sander. The pike-perch draft reference genome will be an
essential material for investigating and answering specific genomics questions such as:
 
  • Characterization of fitness traits associated with genomic variability in captive animals
  •  Identification of biomarkers for welfare, health and growth in pike-perch aquaculture
  • How can genomic selection be optimized for pike-perch breeding programs
 
In this presentation, we are outlining approaches that are currently being used to sequence
and annotate Sander lucioperca genome. These include for instance, hybrid de novo assembly
approaches by combining accurate short-read data from Illumina sequencing technology
with the erroneous long-read data produced by third generation technologies like PacBio

, Ulmencampus, Haus 3, Raum 410

A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer

The talk will be given by Sarah Fischer from the Research Group Medical Bioinformatics of the Universitätsmedizin Rostock.


Background

Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels and Gleason score are not sufficiently accurate for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa stages.

Results

We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for PCa. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ±0.75) with aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage.

Conclusion

The integrated regulatory analysis of coding and non-coding RNAs helps to unravel the molecular mechanisms behind PCa upstaging and to expand our knowledgebase of potential stage-specific diagnostic factors, that are correlated with PCa aggressiveness- related imaging features. Our prediction model based on the combined set of clinical parameters, and of molecular features of genes and miRNAs has the potential to yield clinically relevant results for characterizing PCa aggressiveness.

Summer semester 2018

, Ulmencampus, Haus 3, Raum 410

Manatee invariants: definitions and applications to functional analysis of signaling pathways

Prof. Dr. Ina Koch - Goehte-Universität Frankfurt a. M.

The modeling and analysis of often highly intertwined signaling networks deliver valuable insights into the function of the biological system. For the analysis of robustness and vulnerability of the system, the determination of all possible signal flows is obligatory. The detection of signal flows, e.g., from signal initiation to cellular response, can be a demanding task for signaling pathways, which often exhibit cyclic regulations.
In the talk, we introduce the concept of Manatee invariants based on transition invariants in the context of Petri nets to enumerate all signal flows in a network including cyclic structures. To illustrate the conceptual ideas, we consider a small Petri net model of a part of the TNFR1-mediated NF-κB signaling pathway. Furthermore, we emphasize the benefit of Manatee invariants as precursors to further analyses like in silico knockouts.

Construction of disease networks and identification of common regulatory molecules in selected acute inflammatory indications.

Introduction to Mendel Medical Genetic Lab

Dr. Ali Ahani from the Mendel Lab in Tehran, Iran.

 

Reconstruction of pEV-tumor-immunity network, data integration and analysis to predict early markers for tumor relapse

From standalone tool to framework tool: porting the TriplexRNA into Galaxy

, Ulmencampus, Haus 3, Raum 410

Models - The third dimension of the sciences - Prof. Bernhard Thalheim

Models have become a universal tool in almost all sciences, in technology and also in normal life. For example, computer science cannot do without models in constructing or in explaining and describing systems or in teaching. Modeling is often done as a craft and seems a long way off to have a scientific basis. Models are instruments that fulfill certain functions in scenarios. Therefore they stand independently between data / observations / situations and theories. In an initiative lasting for almost a decade, a discussion forum about all faculties of the CAU together with partners - such as from Rostock – developed a model concept that does not resort to phenomena, but treats models as an independent research subject. In this talk, I will introduce the developed approach to the model concept, to the art and science of the models, to the modeling as a well-founded technique in both development and also when using models and modeling as systematic work towards a culture. Illustrative examples are models from the field of databases, business processes and archeology.

Disease specific network reconstruction from GWAS data

Protein and Gene interaction Networks are an integral part of Systems Biology today. Techniques like RNA sequencing and GWAS helps us to identify important genes and proteins relevant to a specific disease. The challenge is to extract important protein and gene identifiers from the Human interactome and create a network out of their interactions that is specific to a disease. We will explore how to create such a network using Cytoscape.  

Research on Circadian Rhythms in mRNA Expression: Contributions from Mathematical Modeling and Data Analysis. - Dr. Pål Westermark

In animals, the biological circadian clock generates rhythms with a period of ~24 hours that synchronize to the ambient daily light-dark cycles. Genetic feedback loops in single cells are the primary generator of these rhythms, but they are also observable at the tissue and organ level, as well as in behavior. Thousands of genes in many cell types exhibit circadian rhythms in their expression: this is the clock output. Central questions include which cellular processes are affected by circadian gene expression, as well as whether there are underlying principles for circadian control of cellular pathways. We will give examples from our research activities in the study of the clock output in cells from various tissues in mice with focus on mRNA expression and statistics to investigate clock-controlled processes. We also discuss changes in circadian gene expression due to aging, their consequences and how to detect them. We will further give an example of how mathematical analysis helped us uncover some principles for circadian orchestration of metabolic pathways.

Functional Characterization of lncRNAs - David Brauer

Martin Scharm

t.b.a.

Identifying White Blood Cells using Machine Learning - Mariam Nassar

Identifying the number of different white blood cells (WBC) in human blood is an established clinical routine, where WBC are labeled with fluorescent markers.
A novel approach based on machine learning will be presented, where WBC are identified label-free, i.e., without any markers.
We developed an open-source workflow that seamlessly connects the recorded images from the instruments with machine learning. The goal is a presentation of the results relevant for clinicians.
This enables fast, cheap and highly accurate identification of WBC, without destroying the cells and leaves marker channels free to answer other biological questions.

, Ulmencampus, Haus 3, Raum 410

Computer Simulation of the Metastatic Progression and Treatment Interventions - Bertin Hoffmann

Growth and spreading behaviour of tumours and metastases are still subject of intensive research regarding the most effective treatment intervention in individual cases. It is difficult to evaluate experimental data regarding different treatment strategies and its individual characteristics for their clinical relevance. Our collaboration developed a computer model which allows a quantitative comparison of effects of treatment interventions with clinical and experimental data.

The computer model is based on a discrete event simulation protocol. Analytical functions describe the growth of primary tumour and distant metastases, a rate function models the intravasation events of the primary tumour and its metastases. Events describe the behaviour of the emitted malignant cells until the formation of new metastases.

We analysed data from experiments with untreated groups of mice from human small cell lung cancer lines OH-1 and extracted information about the growing and spreading behaviour. On this basis we modelled experimental data from groups of mice, which were treated with chemotherapy and radiation therapy. Our results reveal that the fractal dimension of the primary tumour vasculature changes during treatment. That indicates that the therapy affects the blood vessels’ geometry. We proved that by quantitative histological analysis showing that the blood vessel density is depleted during treatment.

, Ulmencampus, Haus 3, Raum 410

A SNP calling workflow for the analysis of RNA-Sequencing data

Markus Wolfien will give a brief introduction about the GATK4 Single Nucleotide Variant (SNP) caller and will show a detailed workflow for the analysis of RNA-Sequencing datasets.

Faiz Khan

t.b.a.

, Ulmencampus, Haus 3, Raum 410

Can cells cause behaviors of organisms? - Dr. Beate Krickel

Why philosophers think that interlevel-causation is problematic and how we can solve these problems.

Please read the abstract for more information.

, Ulmencampus, Haus 3, Raum 410

Coupled within- to between-host dynamics indicate vaccination-outbreak conflicts in a viral infectious disease - Dr. Alexis Almocera

The complex processes governing an infectious disease remain an incomplete but significant area in systems medicine. Problems on the emerging dynamics at the interface of viral replication and transmission motivate the analysis of multiscale models, especially from a mathematical standpoint. A coupled system, which comprises ordinary differential equations, links within- and between-host scales by means of disease-induced transmission. Bifurcation analysis captures the dynamical relationship between the two scales by evaluating the predicted epidemic size as a function of viral replication. The analysis further suggests that conflicts can arise between effective vaccination and outbreak prevention.
 

, Ulmencampus, Haus 3, Raum 410

Translating molecular activity into multidimensional waves - Dr. Patrick Schopohl

If you like to prepare yourself for this short notice seminar, please have a look at the this or this documentary.

 

Construction and analysis of Molecular Interaction Map(MIM) for Colorectal Cancer(CRC)

Winter semester 2017/2018

, Ulmencampus, Haus 3, Raum 410

Deep Learning in Biology for Next Generation Sequencing Data - DeepBioSeq

Dr. Holger Henning and Markus Wolfien

DeepBioSeq - Deep Learning in Biology for Next Generation Sequencing Data


Abstract

, Ulmencampus, Haus 3, Raum 410

Argyris Arnellos

Short text

 

Summer semester 2017

Prof. Julio Vera-González (University of Erlangen-Nürnberg)

Surfing oceans of data in Neurobiology with networks and mathematical modelling. The case of oligodendrocytes

Abstract

Dr. Anu Jauhan

Systems Pharmacology for Drug Discovery and Development: Paradigm Shift or Flash in the Pan?

Abstract

Andrea Bagnacani

Towards automating and publishing reusable workflow analyses in Galaxy.

Markus Wolfien

Customized workflow development and omics data integration concepts in Systems Medicine.

Shailendra Gupta

Identification of molecular signatures and therapeutic targets underlying E2F1 mediated epithelial to mesenchymal transition

Ali Salehzadeh-Yazdi

Metabolic Modelling (Applications & Challenges)

Holger Hennig

Deepometry: Workflow for applying deep learning in bioimaging analysis

Matti Hoch

Bachelor thesis - practice session

Etienne Rolland

Internship: Extension of the TriplexRNA, Workflow to construct a circular RNA to upregulate pathways of interest

Dr. Olga Krebs (HITS)

Karrierewege für Informatiker in den Lebenswissenschaften

Prof. Béatrice Desvergne & Prof. Olaf Wolkenhauer

Modelling of human metabolism

t.b.a.

Are you interested in giving a talk at our research seminar? Please contact Tom Gebhardt (tom.gebhardt@uni-rostock.de).

t.b.a.

Are you interested in giving a talk at our research seminar? Please contact Tom Gebhardt (tom.gebhardt@uni-rostock.de).

Winter semester 2016/2017

Tom Theile

spatio-temporal modeling

Markus Wolfien

Customized workflow development with Galaxy

Anton Kulaga

Gene circuits modelling with KAPPA rule based language

Holger Hennig

Causality vs. correlation: how to infer stem cell decision making and gene regulation from time lapse microscopy data

Sherry Freiesleben

t.b.a.

Prof. Julio Vera-González

A comprehensive network on macrophage regulations

Ali Salehzadeh-Yazdi

SYSTERACT: Systematic Rebuilding of Actinomycetes for Natural Product Formation

Björn Anderson (Lübeck)

Safe and Dynamic Networking in Operating Room and Hospital

Summer semester 2016

Andrea Bagnacani

t.b.a.

Vasundra Toure

Practices for drawing biological networks using the SBGN standard

Suchi Smita

Investigation of toxicity profile of various combustion aerosols by integrative network approaches

Tom Gebhardt

Improve the visualization of differences between model versions

Holger Hennig

Label-free analysis for high-throughput imaging flow cytometry using machine learning

Sherry Freiesleben

COLLAR - Current status of microarray gene expression analysis

Birgit Berger

A Summary of the IdaMo Summer School

Markus Wolfien

Next Generation Sequencing Data Analysis to Reveal Structural DNA Variations

Tobias Plewka

Cyber-Physical-Systems - Characteristics Analysis

Andrea Bagnacani

The Semantic Lancet Project: a Linked Open Dataset for Scholarly Publishing

Adrien Barton

Dispositions in the Lifesciences

Shailendra Gupta

Systems Biology of E2F Signalling in Tumor Progression and Metastasis

Fritz Beise

Basics in layouting e-books with Adobe InDesign

Winter semester 2015/2016

Steffen Möller

Title:- "eQTL: intertwining disease decomposition and drug repositioning" Expression QTL (eQTL) further annotate disease-associated genetic loci with co-observed changes in the transcriptome. With drugs selected to compensate the disturbance caused for single loci, for a genotyped patient of a multifactorial disease one may derive a recipe for a drug cocktail. This presentation reviews resources available today and emergent algorithms, exemplified on murine data for experimental autoimmune encephalomyelitis, a mouse model for neuroinflammation.

Steffen Möller

eQTL: intertwining disease decomposition and drug repositioning

Martin Scharm

Cancelled

Sherry Freiesleben

A consensus-based approach to identify networks underlying DNA damage responses using microarrays

Steffen Moller & Mohamed Hamed

Integrative computational approaches for studying complex diseases

Tom Theile

Understanding bone tissue - How do small cells organise large structures?

Daniel Koch

Modeling and simulation of chronic myeloid leukemia using causal Bayesian networks

Ron Henkel

Creating a Habitat for Computational Biological Models

Prof. Olaf Wolkenhauer

Muhammad Ali and the Principle of Life: Modelling Whole-Part Relationships in Living Systems

Felix Winter

Mathematical modelling of brain energy metabolism reveals Alzheimer's-specific shape alterations in the BOLD function

Florian Wendland

Hybrid stochastic simulation for multi-scale and multi-level models

Markus Wolfien

New insights into the regulation of the heart rate at the molecular and meta-level

Summer semester 2015

Prof. Dr. Blagoj Ristevski (St. Kliment Ohridski University - Bitola)

Reverse Engineering of Micro-RNA Mediated and Gene Regulatory Networks and Netwoks Properties.

Suchi Smita

Integration of multi-omics and chemometric data to acess the impact of aerosols exposure

Tom Theile

Modeling Bone-Remodeling. The Influence of Microcracks

Felix Winter

Modelling metabolic markers of dementia

Markus Wolfien

Automated NGS data analysis and evaluation

Prof Dr Ralf Schnabel (Head of the Institute for GeneticsTechnical University Braunschweig)

Phainothea:Creation of form by cell focussing in the C. elegans embryo

Dr. Dagmar Waltemath

Notions of similarity for systems biology models

Muhammad Haseeb

Mathematical Modeling of Crosstalk between Wnt Signaling and Cell Cycle Pathways

Aakash Chavan Ravindranath

Data integration and modelling techniques to study protein and compound activity towards drug discovery

Ulf Schmitz

An investigation of microRNA target regulation mechanisms - rehearsal for PhD defence

Dr. Alberto de la Fuente (From Leibniz Institute for Farm Animal Biology (FBN) Dummersdorf)

Silence on the relevant literature and errors in implementation?

Florian Wendland

Pathway-based Prediction of Dynamics in Cancer Cell Populations

Martin Scharm

M2CAT: Extracting reproducible simulation studies from model repositories using the CombineArchive Toolkit

Winter semester 2014/2015

Dagmar Waltemath

Coming soon: de.NBI and SBGN-ED
Abstract

Dr. Michael Linnenbacher

HRO collection as tools for individual cancer network biology.

Faiz M. Khan and Stephan Marquardt

Unraveling the regulatory core of E2F1-mediated tumor aggressiveness using integrated network analysis methods

Murali Chodisetti

Next-Generation Sequencing in Acute Lymphoblastic Leukemia

Raheleh Amirkhah

Prediction of miRNA targets in colorectal cancer using machine learning method

Dr. Sonja Strunz

Gene Co-Expression Networks Reflecting the Transcriptional Responses to Radiation Exposure

Tom Theile

Modeling Bone Growth Using Agent-based-Simulations - Understanding the role of Osteocytes

Dr. Johannes Wollbold

Formal concept analysis methods for systems biology: representing knowledge, analyzing and validating models

Prof. M.J. Bottema

Spatio-Temporal Structure of Cancellous Bone

Markus Wolfien

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

Summer semester 2014

, room 410

Dr. Johaness Wollbold

Constructing a knowledge base for the Free Radical Theory of Ageing

Prof. Maureen O'Malley (University of Sydney)

What has philosophy to do with systems biology, and what has systems biology to do with philosophy?

Sonja Strunz (PhD Defence Rehearsal)

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

Abstract

Winter semester 2013/2014

Markus Wolfien (Student in SEMS project)

Standardising Karr's Whole Cell Model

Prof. Dr. rer. nat. Robert David (Reference and Translation Center for Cardiac Stem Cell Therapy (RTC) Rostock)

Cardiac Cell Types from Stem Cells.

Siavash Ghavami

Accounting for randomness in measurement and sampling of cancer cell population dynamics

Dr. Katja Rateitschak

Pancreatic cancer reloaded

Sherry Freiesleben

Identification of microRNA networks in multiple sclerosis

Daniel Arend

Long-Term Preservation and Management of Scientific Primary Data
Abstract

Bingquan Bao (Master defence)

Multi-scale hybrid model of biological system used as predictors of anti-caner therapy outcoume

Summer semester 2013

Noman al Hassan (Master defence)

Identification and analysis of putatively cooperating microRNAs

Mustafa Baig Mirza (Master defence)

Extending the multi-valued logic approach for modeling biochemical reaction networks

Tim Kacprowski (Ernst-Moritz-Arndt-Universität Greifswald Interfakultäres Institut für Genetik und Funktionelle Genomforschung)

Ranking Molecules - from Disease Gene Prioritization to Omics Data Analyses

Dr. Georg Homuth (Ernst-Moritz-Arndt-Universität Greifswald Interfakultäres Institut für Genetik und Funktionelle Genomforschung)

Whole-Blood Transcriptome Profiling of Population-based Cohorts Reveals Major Gene Expression Changes Correlated with Body Mass Index

Sherry Freiesleben

Investigation of microRNA expression in multiple sclerosis

, Building 1, Room 126

Peter Nonnenmann (FIAS)

Ellerman's heteromorphic adjoint functors & Wolkenhauer's tissue organization
Abstract

Yang Du

BiomvRCNS: copy number study and segmentation for multivariate biological data