Ionizing radiation (IR) leads to DNA double-strand breaks and can therefore be used as cancer treatment. However, some tumors are less responsive to radiation treatment because of their underlying molecular profile. Our ultimate goal is to understand the reaction of lung cancer cells with different phenotypes to IR on a molecular level to then provide the best therapy options for lung cancer patients with IR therapy.

gefördert durch:

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.

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.

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.

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).

 ~ 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.