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  • LIFE

    Leipzig Research Center for Civilization Diseases
    We are concerned with the pre-processing, management and analysis of molecular-genetic data in LIFE-Heart, -Adult and -Child. We are especially interested in metabolic and vascular phenotypes.


    Pneumonia Research Network on Genetic Resistance and Susceptibility for the Evolution of Severe Sepsis
    Our group is concerned with planning, management, data banking and data analysis of the PROGRESS consortium, a collaborative effort to understand the mechanisms of disease progression in pneumococcal lung disease.


    System Medicine of Pneumonia-aggravated Atherosclerosis
    In this project, we collaborate with several partners to understand molecular patho-mechanisms linking pneumonia with atherosclerosis development and aggravation. In the consortium, we are responsible for bioinformatics analyses of molecular data of several human cohorts including PROGRESS, LIFE-Adult and LIFE-Heart. We also perform analyses of data of in vivo animal models including single-cell sequencing based analyses. Finally, we develop stochastic and mechanistic biomathematical models of pneumonia-aggravated atherosclerosis development.


    Epidemic Hospital Resource Demand – Modeling Incidence, Bed-occupancy, Staffing and Supply Chains
    The aim of this BMBF-funded project is to develop powerful epidemiologic models of respiratory tract infections and their impact on different levels of hospital care including occupancies of normal ward, intensive care units and ventilation requirements at different time and geographical scales. Moreover, we aim at predicting the impacts of epidemics and hospital burden on supply chains and human resources. The project is a collaborative effort of five universities, namely Aachen, Augsburg, Münster, Dresden and Leipzig. It is part of the German modelling network of severe infectious diseases MONID.

  • OptimAgent

    Optimal control of the epidemic under heterogeneity conditions – decision making perspective on agent-based modelling
    OptimAgent is a large collaborative effort of different groups in Germany to establish a comprehensive agent-based model of respiratory tract infections in Germany. We contribute by developing and applying approaches for estimating heterogeneity of epidemiologic model parameters between different geographical regions, variants and epidemic periods considering different learning approaches such as Bayesian methods, transfer and reinforcement learning. Major application area is the COVID-19 pandemic but methods are developed in a generic way to strengthen pandemic preparedness. OptimAgent is also part of MONID network.

  • ScaDS.AI

    Center for Scalable Data Analytics and Artificial Intelligence
    We are partners of Scads.AI. Here, we develop new methods of combining natural and artificial intelligence in the framework of biomathematical disease and therapy models. In particular, we analyse the relative importance of prior knowledge regarding establishing patho-mechanistic biomathematical models and how to combine mechanistic and neural network models effectively. We will also study methods of reinforcement learning to improve parametrization of mechanistic models.

  • Collaborative Research Centre 1052 “Obesity Mechanisms”

    Together with partners from Israel, we lead the project “Molecular predictors and causal drivers determining the personal response to dietary interventions – integrated analysis of four long-term, large scale dietary clinical trials and epidemiological data” in the framework of the CRC 1052. We will perform bioinformatics and causal network analyses to identify molecular factors affecting or predicting the outcome and long-term effects of dietary intervention programs. Large-scale clinical cohort data established by our partner will be used for this purpose.

  • ChemoTox-AI

    A Framework and Tool to Combine Artificial Intelligence and Physiology for Personalised Modeling of Chemotherapy Toxicity
    This project is part of the Computational Life Sciences line of funding (CompLS3 “Machine Learning for Cancer Research”). We combine physiological knowledge with artificial neuronal network (ANN) modelling to understand the heterogeneity of haematotoxic response to cytotoxic cancer therapies and to derive strategies for personalised risk management.


    Causal Analysis and Predictive Modelling of Molecular and Clinical Time-Series Data of Pneumonia Patients
    This project is part of the Computational Life Sciences line of funding (CompLS4 “AI-Methods for Infection Research”). We lead this project and cooperate with the Charité. We establish a framework to extract causal molecular relationships from clinical and molecular time series data of pneumonia patients and use them to develop powerful prediction models. We will distinguish and compare non-COVID-19 and COVID-19 induced pneumonia to mechanistically describe and understand differences in their pathologies.


Completed Projects

  • CAPSyS

    Systems Medicine of Community Acquired Pneumonia
    CAPSyS is a consortium with the aim to investigate and better understand the course of pneumonia from infection to resolution. Prof. Scholz is head of subproject 1 "Integrative Genetic Analysis and Biomathematical Modelling of Systemic Inflammation". In addition, our group is involved in work packages on data integration and visualization and supports project management. Our systems-medicine approach will also be extended to COVID-19 induced pneumonia.


    A Diagnostic Test to Improve Surveillance and Care of COVID-19 Patients
    This project aims at establishing gene-expression signatures predicting the outcome of COVID-19 patients and translating them into marked-ready diagnostic or prognostic tests. Patients are collected throughout European countries. We contribute to bioinformatics analyses of this European collaborative effort. See here for details.

  • HaematoOPT

    Model-based Optimisation and Individualisation of Treatment Strategies in Haematology
    In this project, we aimed at transfering results of biomathematical model simulations into clinical practice. Major fields of applications are haematopoietic growth-factor opimization during cytotoxic chemotherapy and optimization of EPO applications in chronic kidney disease.

  • HaematoSys

    Systems Biology of the Haematopoietic Systems and Related Neoplasias
    Our group performed systems-biological modelling regarding dynamics of haematopoiesis and tumour growth in mice and humans in the framework of HaematoSys.

  • Leipzig Health Atlas

    The Leipzig Health Atlas is a medical informatics infrastructure to provide research data and biomathematical models to the scientific community following FAIR principles. We contribute to this effort by preparing and providing genetic research data, models and our COVID-19 bulletins.


    Clinical, Molecular and Functional Biomarkers for Prognosis, Pathomechanisms and Treatment Strategies of COVID-19
    Our group is concerned with planning, management, data banking and data analysis of the PROVID consortium aiming at establishing biomarkers and molecular pathomechanisms of disease progression in hospitalized COVID-19 patients.


    Integrated Platform for Identification and Validation of innovative, RNA-based Biomarkers for Personalised Medicine
    Our group supported planning, remote data entry, data banking, and data analysis of the Ribolution consortium.

  • SaxoCov

    Saxonian Corona Field Study and Accompanying Research
    In the framework of the SaxoCov collaborative research programme, we perform data investigations and integrations to understand COVID-19 disease spread, efficacy of non-pharmaceutical interventions and vaccination effects. Moreover, we develop epidemiologic models to describe the disease spread in Saxony, to evaluate the efficacy of non-pharmaceutical interventions and to make predictions of the future course of the pandemic. Our analyses are provided to the Saxonian government for decision support and are regularly published on our website and the Leipzig Health Atlas.