Modelling and Manipulating the Phagocyte-Mycobacteria Interface. Tuberculosis is the most pervasive infectious disease worldwide, and recent emergence of drug-resistant strains emphasises the need for improved drug treatments. An integrated approach to dissect, model and ultimately manipulate the interactions between mycobacteria and their host is a promising innovative strategy to develop new anti-infective drugs.

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Started in 2020, the LOEWE center emergenCITY is researching resilient infrastructures of digital cities that can withstand crises and disasters. emergenCITY is organized as an interdisciplinary and multi-site cooperation led by Technische Universität Darmstadt, Universität Kassel, and Philipps-Universität Marburg as well as the Federal Office of Civil Protection and Disaster Assistance and the City of Darmstadt. The center partners with several other institutions from academia, industry, and public administration.

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Goal of this EU-funded project was the development of a predictive computational technology that can exploit molecular and clinical data to improve the understanding of disease mechanism and to inform clinicians about optimized treatment strategies.

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Within this project we will validate RNA biosensor technology for the detection of viral RNA.

The modularity of cell-free systems in their composition of specific sensory components and universal building blocks such as coupling to signal amplifying reactions or different reporters will enable us to provide a platform for the rapid and efficient development of biosensors for the detection of viral RNA.

This project is funded by the European Regional Development Fund as part of the Union's response to the COVID-19 pandemic. The budget is used for necessary laboratory equipment.

This EU funded project has the goal to collect, standardize and harmonize existing clinical knowledge and medical data and, with the help of artificial intelligence, create treatment models for patients. Armed with these treatment models, scientists will then test them on virtual patients to evaluate treatment efficacy and toxicity, thus improving both patient survival and their quality of life. To accomplish our goals, we have assembled an interdisciplinary team consisting of basic, translational, and clinical researchers—all amongst the leaders in their respective fields—and established strong relationships with European Centres of Excellence, patient organizations, and clinical trials focus on personalized medicine for our proposed case studies. In summary, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows, and enable the Digital Single Market of the EU data infrastructure.

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Contextualizing biomolecular circuit models for synthetic biology

Synthetic biology is the bottom-up engineering of new molecular functionality inside a biological cell. Although it aims at a quantitative and compositional approach, most of today’s implementations of synthetic circuits are based on inefficient trial-and-error runs. This approach to circuit design does not scale well with circuit complexity and is against the basic paradigm of synthetic biology. This unsatisfactory state of affairs is partly due to the lack of the right computational methodology that can support the quantitative characterization of circuits and their significant context dependency, i.e. their change in behavior upon interactions with the host machinery and with other circuit elements.

CONSYN will contribute computational methodology to overcome the trial-and-error approach and to ultimately turn synthetic circuit design into a rational bottom-up process that heavily relies on computational analysis before any actual biomolecular implementation is considered.

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This EU funded project has the goal to collect, standardize and harmonize existing clinical knowledge and medical data and, with the help of artificial intelligence, create treatment models for patients. Armed with these treatment models, scientists will then test them on virtual patients to evaluate treatment efficacy and toxicity, thus improving both patient survival and their quality of life. To accomplish our goals, we have assembled an interdisciplinary team consisting of basic, translational, and clinical researchers—all amongst the leaders in their respective fields—and established strong relationships with European Centres of Excellence, patient organizations, and clinical trials focus on personalized medicine for our proposed case studies. In summary, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows, and enable the Digital Single Market of the EU data infrastructure.

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The goal of CompuGene was to develop computer-aided processes to enable the design of complex genetic circuits in biological systems, with a highly interdisciplinary approach. The resulting implementation of biomolecular functions in cells, and their targeted usage, bears great scientific and economic potential. In order to make genetic circuits of higher complexity feasible, a novel scientific approach is required to replace current trial-and-error methods of synthetic biology.

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MAKI creates an innovative premise for the communication systems of the future. Its aim is to be more adaptive to changes, particularly during ongoing operations. This could facilitate, for instance, the ability to stream video on a smartphone in high quality without interruptions in spite of busy or overloaded mobile networks. Users would be able to rely on steady and reliable reception even while attending festivals or crowded sporting events.

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Regulatory processes within living cells have long been the topic of research interest and the key to understanding various diseases. The decades of studies resulted in a large body of knowledge on molecular interactions and regulatory pathways in the cells of model organisms ranging from microorganisms to mammals. Nevertheless, accurately inferring gene network topology at the scale of a whole cell has remained an intractable task until recently, mostly due to the large amount of single-cell data needed for such inference. In the last few years, single-cell RNA sequencing (scRNA-seq) technology enabled measuring transcriptome of high numbers of individual cells, which allowed observing a much greater share of the multidimensional parameter space of large gene networks and gave rise to multiple inference methods. However, none of the existing methods incorporate all relevant knowledge on biophysical constraints. This project aims to incorporate prior knowledge on the system; decomposition of measurement, extrinsic and intrinsic noise; and accurate representation of stochastic gene expression and its regulation into a Bayesian inference framework for identifying topology of a gene network and rate constants of its molecular interactions. The performance of the inference algorithm will be tested by evaluating its ability to predict the effects of transcription factor deletion perturbations. Enhancing gene network inference by accounting for the wealth of known biophysical constraints could provide insights into the gene regulatory processes that would enable advancement in developmental and evolutionary biology, biomedicine and bioengineering

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