SBI – Department of Systems Biology and Bioinformatics
Faculty of Computer Science and Electrical Engineering
University of Rostock
Ulmenstrasse 69 | 18057 Rostock
+49 381 498-7571
Making Sense out of Data - Providing Meaning to Models
We are experiencing tremendous technological advances, bringing a steady increase in the generation of data in biology and medicine. How can it be, that these advances do not translate into automated analyses, confident predictions and user-friendly tools? Due to the complexity of living systems, biological and medical data do not speak for themselves. We approach this challenge with the development of data analysis and modelling workflows, in which multiple heterogeneous data sources are integrated and processed by a variety of methodologies.
Not only the generation of biological and medical data has become more complicated, the analysis of data cannot be done without an interdisciplinary effort. To this end, we successfully combine a wide range of computational and mathematical approaches in integrated workflows. Our team is multidisciplinary and so is our approach to address problems in biological and medical research.
We have twenty years of experience in interdisciplinary research and training, which led to a solid track record of successfully completed projects. Our methodologies and tools are supporting the translation of biological and medical research into real world solutions and practical applications.
Our expertise at a glance:
We analyse data and create meaningful models to improve our understanding of health and disease. Our research thereby contributes to better diagnosis, prognosis and therapy. Check our profile at www.sbi.uni-rostock.de and contact us for further information.
The success of our team is largely determined by our vision for interdisciplinary research. Our team assembles a wide range of expertise from computer science, biology, medicine, biochemistry, engineering, physics and mathematics. However, we do not think in terms of disciplines and neither do we care much about project boundaries. Our team is International, and our collaborative network is worldwide. We prefer collaborations over competition. We are fond of transparency; we trust people, not rules.
Above all, we love multidisciplinary challenges to investigate complex systems.
Eine deutschsprachige Kurzfassung unserer Forschung finden Sie auf der Seite www.sbi.uni-rostock.de/datascience
#DataAnalytics #DigitalHealth #SystemsMedicine #DataDrivenMedicine #DataSavesLives #SystemsBiology #DataScience #SystemsScience #BioTechnology #Bioinformatics #DataScience #BioMedicine #SystemsScience
In the evaluation of imaging procedures (X-ray, CT, MRI), many assistance systems already exist to visually prepare the images for diagnosis. Software tools support physicians by segmenting and annotating regions of interest in medical images and diagnosing diseases. In recent years in particular, the analysis of medical image data using artificial intelligence has made great strides.
In order to increase acceptance among physicians, diagnoses should be made comprehensible. However, the greatest advantage of frequently used neural networks is at the same time a great disadvantage: Due to their complexity, they allow classifications to be made for difficult questions, but the underlying decision-making process can hardly be traced. However, the comprehensibility can be increased by novel methods through so-called "Explainable" or "Reasonable" AI. Here, especially for image data, a modified version of the image is usually output, in which specific areas are marked that are of high importance for the network for a certain diagnosis/classification.
At SBI, existing algorithms of "Explainable" AI are applied to medical diagnostic tools, and new methods are developed specifically for this use case.
A benchmark of context-specific models for the assessment of cancer metabolic heterogeneity.
A platform for trimming and extracting genome-scale metabolic models (GEMs)
Clinical trials are a good source for alternative therapies for cancer patients. However, mapping cancer patient profiles to clinical trials is a time consuming task, because the search parameters are generic. Matching the patient's specific tumor parameters to the clinical trials requires reading the eligibility criteria of a huge amount of clinical trials.
In this project, we work on a tool for clinicians to find and rank potentially relevant clinical trials for their cancer patients. This requires a more specialized search for fitting clinical trials by matching cancer patient profiles using methods of information extraction, clinical text processing, clustering and topic extraction, ranking of results and sentence complexity analysis. The goal of this project is to provide a time saving alternative to reduce the number of clinical trials which has to be read by clinicians by filtering and ranking clinical trials eligibility criteria for a given patient profile.
Finding groups of airway tree structures in CT images
The MelAutim project aims to uncover the molecular and cellular mechanisms for the interaction of cancer and autoimmunity. In particular, it aims to identify factors that are involved in the development of new or the exacerbation of existing autoimmune diseases during immunotherapy.
In real world scenarios, datasets are often imbalanced. That is, the datasets meant for supervised learning, divides into classes, where in some classes there are a very large number of instancess, compared to the others. Training machine learning algorithms on such data is challenging. We have developed an algorithm that overcomes problems of widely used algorithms.
Pre-eclampsia (PE) is a complex disorder occurring during pregnancy and the postpartum affecting almost 5-8% of all pregnancies and often occurs with other complications. Its pathology is largely unknown. To improve the diagnosis and treatment outcomes, a deeper understanding of the determinants of pathophysiology is urgently needed.
A European standardization framework for data integration and data-driven in silico models for personalised medicine.
The AIR is to provide an interactive platform connecting scientific and medical communities.
The project addresses the generation and establishment of programmed pacemaker cells for an in vitro drug testing possibility to perform predictive tests. This may lead to an improved treatment of cardiac arrhythmias or an accurate identification of potential drug molecules at an early stage of development. Important benefits will arise in verifying the safety of a wide variety of medicines while reducing animal testing.
The BESTER project will develop new bioprocesses for the production of butyl esters for the bio-based chemicals market. The in situ enzymatic esterification of butyric acid (HBu) and butanol (BuOH), produced by C. acetobutylicum, to butyl butyrate (BuB) with simultaneous extraction of the ester has been successfully shown by TU Delft (NL), using a single bioreactor setup (5g/L BuB yield).
This page is in German, for an English summary/flyer, please click here.
Der technologische Fortschritt in den Lebenswissenschaften ist eng mit der Generierung immer komplexerer Daten verbunden, deren Analyse und Interpretation oftmals nur noch durch computergestützte Werkzeuge der Informations- und Kommunikationstechnik und mit Hilfe ausgefeilter mathematischer Methoden geleistet werden kann. Mit einer stetig wachsenden Vielfalt und Menge an Daten ergeben sich jedoch auch neue Möglichkeiten, die sich im Kontext “Data Science" zunehmend klarer präsentieren. Der Begriff “Data Science” beschreibt hier die Extraktion von Wissen aus Daten.
~ In biology, the exception is the rule. ~
~ With our work, we are not really interested in the unique, but in what is general in the unique.~
With this project, we want to address a biological and a methodological challenge. First, we wish to clarify how the functioning of cells, and the functioning of a tissue relate to each other. Do cells exercise a degree of autonomy, or is their behavior completely determined by the functioning of the tissue? Such questions are important in understanding the emergence and progression of diseases. For example, it remains unclear whether the causative origin of colon cancer is a cell, or a consequence of tissue organization.
Deep learning technologies are making an impact, particularly with image analysis and object detection. Applications to Next Generation Sequencing data are however still at an early stage ...
Regenerative therapies using stem cells for the repair of heart tissue have been at the forefront of preclinical and clinical development during the past 16 years. To build upon this progress, the Phase III clinical trial PERFECT was designed to assess clinical safety and efficacy of intramyocardial CD133+ bone marrow stem cell treatment combined with coronary artery bypass graft for induction of cardiac repair.
Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.
By bringing the community of modelling groups together and contributing to the improvement of the annotation of existing models and the accessibility of related datasets, we will pave the way for the development of a modular, integrative model of cellular processes.
Investigating the gut-brain-axis
The gut–brain axis (GBA) provides a bidirectional homeostatic communication between the gastrointestinal tract and the central nervous system. The interdisciplinary collaboration is going to fully explore a first comprehensive GBA cross-disease map of genetic, expression and regulatory changes associated with ulcerative colitis and schizophrenia disease entities.
KNOWYODA delivers high quality tools to manage and analyse health data for the private user. KNOWYODA is a secure, personal, digital memory focussing on health related data. We develop cutting edge methodologies to support patients and the public visualise and interpret their data.
RNA Sequencing (RNA-Seq) has become a widely used tool to study quantitative and qualitative aspects of the transcriptome. The variety of RNA-Seq protocols, experimental study designs and the characteristic properties of the organisms under investigation greatly affect downstream and comparative analyses. We provide easy access to comprehensive analysis of RNA-Seq experiments as a service. To do so, we leverage on the Galaxy framework, and organise dedicated workshops, training programs, and screencasts to make Life Scientists familiar with computational approaches to biological problems.
The aim of the project is to develop, test and prepare for translation into clinical practice a systems-biology-based diagnostic tool for assessing the probability of tumor relapse in melanoma patients, based on the profiling of pEVs. In the methodology proposed, in vitro and clinical data are integrated using data-driven mathematical modeling. The insights obtained from patient data analysis, reconstruction of biochemical networks, and model simulations are used to a) select a set of microRNAs, long non-coding RNAs, and proteins present in pEVs of patients to be measured in a blood test as surrogates of immune system activity against MRD and b) assess the probability of tumor relapse in the close future.
Data Management node in the German Network for Bioinformatics Infrastructure
The TriplexRNA is a database of cooperating microRNAs and their mutual targets.
The database collects predicted and experimentally validated RNA triplexes, and provides an interactive interface for highligting targets of concerted RNA regulation within known disease pathways.
Research projects we take part in producing research outputs by maintaining close collaborations.
The main objective of the SYSTERACT project is through an integrated and interdisciplinary approach to develop the model actinomycete Streptomyces coelicolor into a "Superhost" for the efficient heterologous production of bioactive compounds, enabling a faster discovery of new antibiotics from environmental microbial resources (microbial strains and metagenomes). Central to this approach will be an iterative Systems Biology process, combining microbiology, genetics, biochemistry, and fermentation technology with modelling.
The School brings together PhD students, postdocs and senior scientists, coming from medicine, biostatistics, bioinformatics, medical informatics, molecular/cell biology, epidemiology, and systems biology. The four day workshop will be structured into six parts, covering these six areas.
Helmholtz Virtual Institute in Aerosol & Health Research is funded by the Helmholtz Association led by the Helmholtz Zentrum München and the University of Rostock in European cooperation with 8 academic partners and 6 associated partners. The project started in January 2012 as a five-year project.
The investigations will deepen our knowledge on the impact of radiation-induced complex DNA lesions with spinoffs for radiation protection and the development of new, advanced tumor therapy strategies.
Promotion of the SBGN standard and extentions to SBGN-ED.
The Coordinating Action Systems Medicine (CASyM) is a multidisciplinary European consortium that joined forces to develop an implementation strategy (road map) for Systems Medicine. The CASyM road map is driven by clinical needs: It aims to identify areas where a systems approach will address clinical questions and solve clinical problems.
The goal of this summer school is to encode the whole-cell model -- a popular model of the whole cell -- in standard formats. The summer school will take place in March 2015 in Rostock, Germany.
This project is to generate and analyze a computational multilevel model of a signaling network that has a key role in tumor progression and metastasis. The project brings together an experienced group of modelers with an internationally renowned group in cancer research with a leading role on E2F1 signaling, investigating for the first time the impact of E2F1 on cancer aggressiveness using a systems approach.
Systems biology is a scientific approach characterised by an iterative cycle of data-driven modelling and model-driven experimentation. The present project is to provide support for this iterative cycle through techniques and tools that improve and ease the working with simulation setups, using standard formats and ensuring result reproducibility.
The emergence of systems biology signaled a shift of focus, away from the molecular characterization of individual components, towards an understanding of functional activity. The field is however also too focused on pathway-centric approaches, mechanistic models and subcellular processes. It is increasingly evident that the behavior of cells is largely dependent upon influences from the environment. New approaches are thus required to integrate processes at the molecular and cell level with those at higher levels of structural and functional organization (e.g. tissues and tissue physiology). We argue that this calls for a rethinking of what systems biology should be about (the search for organizing principles) and that new approaches are required to tackle biological complexity. To this end we promote Mathematical General Systems Theory, multilevel modeling, and theorem proving as a way forward. In this project we are going to discuss the role of mathematical models, the notion of mechanisms and biological complexity in systems biology.
It is widely assumed that mitochondrial dysfunction and subsequent ROS (Reactive Oxygen Species) production are crucial players in ageing. However, the specific role of mitochondrial pathways in the onset and progression of degeneration and ageing is still unclear, as is the degree of interplay among organs in the ageing process. We hypothesise that impairing mitochondrial respiratory function leads to oxidative stress and ROS production. This induces DNA damage, inflammation and, ultimately, cell senescence and ageing.
Clostridium are bacteria which evolved before the earth had an oxygen atmosphere. To them the air we breathe is a poison. To survive they produce a spore resting stage, resistant to physical and chemical agents. Some species cause devastating diseases, such as the superbug Clostridium difficile. On the other hand, most are totally harmless, and make a wide range of chemicals useful to man. The best example is Clostridium acetobutylicum which makes butanol.
The adult tissue of an organism includes stem cells, which through cell division cycles maintain and regenerate the functional tissue through differentiation and maturation. With regard to stem cell dynamics our research focus is on multilevel systems. Multilevelness is a defining characteristic of complex systems. The behavior of the system "as a whole" is considered to emerge from the functioning and interactions of its parts. What we are seeking is a conceptual (mathematical) framework to analyse how the "fate" of the tissue is an emergent property that inherently arises from the complex yet robust underlying biology of stem cells. Without specifying how the emergence takes place, the concept has almost a mystical character, it is an observation rather than a contribution to understanding the phenomenon. For understanding it is necessary to identify how the behavior of the whole changes when the parts and/or interactions between them change. Understanding cross-level relations in complex systems is key to "demystifying" the concept of emergence.
Recent experimental evidences suggest that cancer progression and aging could be linked at the cellular level by the abnormal function of some signalling pathways, cellular systems in charge of identifying and processing the external signals received by the cell. Those mechanisms protect our cells against external or internal insults that damage their genetic material. Leaded by the protein p53, the so-called DNA damage master controller, these processes repair the inflicted DNA damage but also take additional measures to avoid these genetic errors to be transferred to new cells. Depending on the severity of the damage, p53 can trigger a transient/permanent stop of cell replication or the programmed cellular death (apoptosis) in a strategy to kill some damaged cells but save the whole organism. Imbalance between these processes is very often present in different phases of cancer progression, but also is one of the sources of aging in the organism.
It is well known that cells react on chemical or topographical alterations of the surfaces they are attached to. Despite that, the underlying mechanisms causing these interactions are most widely unknown. Thus, the DFG funded project CeMaTif, consisting of four research groups from Rostock and Tübingen, aims at a better understanding of the influences which micro- and nanostructured titanium surfaces exert on adjoining biosystems.
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by amyloid plaques in the brain of affected individuals. This project aims at modelling of neurodegenerative processes in AD. Our study focuses on the interactome of neuronal factors central to the proteolytic processing of amyloid precursor protein (APP) into Aβ, the main constituent of senile plaques. Factors considered in this model include proteases, trafficking adaptors, as well as a novel sorting receptor SORLA.
Aβ aggregation inside the brain is one major aspect of Alzheimer's disease. The appearance of Aβ plaques is highly correlated with the decline of brain activity. It is therefore necessary to understand the mechanisms regulating Aβ production, Aβ degradation and removal from the brain. The aim of the project is to develop an integrated model to understand the dynamics of Aβ aggregation and its transport through the blood brain barrier.
The BaCell project is part of the SysMo transnational network, an initiative focusing on approaches on the application of Systems Biology to microorganisms. In the BaCell project, our interest is to gain a better understanding of the dynamics of the transition from exponential to stationary growth phase in Bacillus subtilis. Obtaining knowledge in this model organism provides twofold advances: First, elucidating the fundamental processes in bacterial growth and stress response. Second, Bacillus subtilis is used in many biotechnological applications, thus our research improves production of medical drugs and consumer goods.
An important aspect of the development of biofunctional implant surfaces is a better understanding of the influence of micro- and nanastructured materials on cells growing on surfaces. The theoretical analysis of the adhesion is limited to adult cells. But it is known that the accretion of the cells happens in two phases. One is the passive phase containing the adhesion and migration of the cell depending to the properties of the surface.
MicroRNAs (miRNAs) are endogenous, small, non-coding ~22 nt RNA molecules that have emerged as a major class of regulatory genes for diverse range of biological functions. In our preliminary studies, we were able to demonstrate that primary melanoma show a distinct miRNA expression pattern compared with benign melanocytic nevi. The aim of the present project is to identify and functionally characterize miRNAs that might play a role during further melanoma progression (metastasis).
The PROMICS project addresses the cellular biology and metabolic integration of photorespiratory processes in the context of carbon metabolism (C3, C4 and cyanobacteria), stress response and productivity. The photorespiration is the result of the oxygenase pathway, see the picture below, of the rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) which leads to the reduced rates of photosynthetic CO2 assimilation, making photorespiration a wasteful process. However, it is just this inefficiency which can significantly decrease the photoinhibition or high temperature stress. The question arises where is the balance between the protection and crop yield or if the photorespiration is essential process at all.
Protection of humans from the adverse effects of ionizing radiation is a major goal of the Radiation Protection field. In the case of accidental exposures to radiation the development of effective counter measures requires information on the actual exposure dose as well as knowledge on the individual radiosensitivity of the exposed group of individuals. The major scientific goal of this project is to identify suitable sub-sets of genes which correspond to the expression patterns and correlate this information with radiation dose and radiation quality. In addition, it will investigate the effects of single nucleotide polymorphisms on the individual radiation susceptibility and their influence on the proposed biodosimetry system.
The ability of regeneration characterizes cell biological systems and is increasingly required for computer science systems as well. The Graduate School "dIEM oSiRiS" is a DFG-funded research training group. It brings together researchers from Medicine, Biology and Computer Science and contributes towards achieving new insights into the functioning of biological cell systems, establishing modelling and simulation as an experimental methodology in Biology, and developing innovative modelling and simulation methods and tools from which the understanding and the design of regenerative systems in general will benefit.
ExCell is a joint project investigating the changed understanding of the living cell in science. The task for the project partners is to investigate the transformation of scientific knowledge in the life sciences in a comprehensive and interdisciplinary way. For the first time, the shift in paradigm, concerning the understanding of cellular processes in the context of the digital revolution in light microscopy and systems biology, is scrutinized.
This project combines theoretical and experimental systems biology to challenge pancreatic cancer (PC), a tumour disease with a very poor prognosis. It is focused on mathematical modelling of chemical kinetics and transport processes in biochemical reaction networks of PC cells. The model properties are analysed with methods of dynamical systems theory and methods of experimental design to understand, predict and improve the cellular response to chemotherapy. This effort is supported by quantitative cell biology, and the development of experimental techniques for systems biology.