Aging-related processes such as cellular senescence are believed to underlie the accumulation of diseases in time, causing (co-)morbidity, including (pancreatic) cancer and (ischemic) stroke. Intervening into these processes may delay, stop or reverse morbidity. To study the link between (co-)morbidity and aging, by exploring biomarkers and molecular mechanisms of disease-triggered deterioration, we will recruit 100 patients with pancreatic ductal adenocarcinoma and ischemic stroke, and 50 controls, at the clinics of hematology/oncology and neurology of the Rostock University Medical Center. We will subject them to blood routine, clinical scores and patient-reported outcomes at up to 9 time points, including an in-depth transcriptomics & proteomics characterization at two early time points. The definition of outcomes aims for clinically relevant predictive biomarkers of morbidity (quality of life, probable sarcopenia, cognitive decline) and comorbidity (vascular event, cancer). The primary outcome is a composite of probable sarcopenia, clinical performance score and quality of life. The data analysis is comprehensive in that it includes biostatistics & machine learning, both following canonical role models & explorative approaches. Predictive biomarkers for “seno-interventions” may become available if the biomarkers that we find are predominantly related to aging / cellular senescence. Similarly, diagnostic biomarkers will be explored for their relationship to aging / cellular senescence. Our findings will require validation in independent studies, and our dataset shall be useful to validate the findings of other studies. In some of the explorative analyses, we shall include insights from systems-biology modeling as well as insights from animal models.

In collaboration with the Modelling and Simulation Group at the University of Rostock we have built a traffic simulation capable of simulating an intersection, including the traffic lights and various types of cars. The intersection is populated with realistic traffic extracted from data given to us by the Department of Transportation in Rostock. There has been put a large focus on the visualisation part of the simulation, as well as on correct behaviour of the road users. In this talk I want to demonstrate the simulation, how we implemented it and our conclusions.

Neutrophil extracellular trap (NET) formation involves the release of DNA peppered with antimicrobial proteins and histones which can immobilize and kill pathogens. NETs are being increasingly recognized to be playing a key role in a wide range of diseases and conditions, with both beneficial and detrimental outcomes.

A major issue plaguing NET research is a lack of reliable, non-biased, and high-throughput techniques for quantification. I have developed a novel technique to identify and characterize NETs using imaging flow cytometry. This technique is able to discriminate not only NETs, but also cells undergoing nuclear decondensation (a precursor step before NET formation), DNA fragments, and other types of cell death. An image analysis algorithm was developed based on a set of image features which can be used to identify characteristics such as the presence or absence of extracellular DNA, and the loss of membrane permeability. This allows the automated quantification of cell types and removes any potential operator bias. NETs can be measured in vitro, but importantly can also be identified in whole blood samples, giving this approach potential clinical applications.

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.

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.

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.

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.

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

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.

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

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.

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.

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.  

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.

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.

Dr. Holger Henning and Markus Wolfien

DeepBioSeq - Deep Learning in Biology for Next Generation Sequencing Data

Abstract

Towards automating and publishing reusable workflow analyses in Galaxy.

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

Deepometry: Workflow for applying deep learning in bioimaging analysis

Modelling of human metabolism

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

Customized workflow development with Galaxy

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

A comprehensive network on macrophage regulations

Safe and Dynamic Networking in Operating Room and Hospital

HRO collection as tools for individual cancer network biology.

Next-Generation Sequencing in Acute Lymphoblastic Leukemia

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

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

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

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

Standardising Karr's Whole Cell Model

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

Identification of microRNA networks in multiple sclerosis

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

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

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