ETH Zurich

Country: Switzerland
Funder (3)
Top 100 values are shown in the filters
Results number
1,155 Projects, page 1 of 231
  • Funder: EC Project Code: 335006
    Partners: ETH Zurich
  • Open Access mandate for Publications
    Funder: EC Project Code: 668991
    Overall Budget: 2,499,980 EURFunder Contribution: 2,499,980 EUR
    Partners: ETH Zurich

    Understanding processes in microbial communities is a crucial task given the impact of microbial communities on environmental systems, including plants and animals. There is a rapidly increasing number of microbial communities whose collective genomes have been determined; however, it is important to uncover their collective function and to understand how community properties emerge from the properties of individual microbial types and their interactions. One habitat that has been gaining growing interest is the phyllosphere, or the aerial parts of plants, which carry out the majority of terrestrial carbon dioxide fixation. There is a urgent need to better understand the microorganisms living in the phyllosphere and an increasing awareness of the importance of indigenous microbiota and their role in microbe-microbe and host-microbe interactions for both plant growth and protection. This project aims to uncover the molecular basis shaping microbial communities in the phyllosphere in order to improve our functional understanding of microbial interaction in the context of the plant host and to unravel the principles of the formation of community pattern and function in situ. To reach these objectives, a reductionist approach will be used to generate and test new hypotheses regarding microbial interactions in phyllosphere communities. Synthetic, tractable microbial communities will be formulated and analyzed under gnotobiotic conditions. In situ community approaches will be developed and applied, while community genetics and experimental evolution will provide complementary perspectives on the community structure and function. These approaches will be mirrored by manipulating interactions on the host side through the use of plant mutants and ecotypes. Taken together, using multifaceted perspectives on microbial interactions in situ will allow unprecedented insights into the biology of bacteria living in the phyllosphere and their individual and collective function.

  • Open Access mandate for Publications
    Funder: EC Project Code: 841316
    Overall Budget: 191,149 EURFunder Contribution: 191,149 EUR
    Partners: ETH Zurich

    Current knowledge of fracture healing is based on experimental animal studies of diaphyseal bone, despite 20% of fractures occurring in the metaphysis of the distal forearm. Moreover, current fracture healing assessment tools lack the resolution to identify and isolate the initial fracture site in cases involving a crushing fracture. The objectives of this project are to (1) develop a micro-finite element (μFE) model for fracture site identification, (2) develop an in silico model for human bone fracture healing capable of tracking local microstructural changes, and (3) test the predictive power of the in silico model using clinical data. μFE models will be generated from high-resolution peripheral computed tomography (HRpQCT) data of the distal radius from wrist fracture patients. The μFE models will be generated from HRpQCT data collected at the early stages of fracture healing and compared to remodelling maps in order to determine if μFE models can be used to isolate the site of the initial fracture. Adaptations will be made to an existing in silico model based on the findings of the μFE analysis. The resulting in silico model will be applied to clinical HRpQCT data to predict endpoint microstructural changes as well as patterns in fracture healing and remodelling. The predictive power of this in silico model will be determined by comparing the simulation results to observed behavior in vivo. The proposed project merges recent advances in bone mechanobiology, μFE simulations, and medical imaging to develop novel image analysis and registration methods as well as a tool for predicting if, when, and where fracture healing will occur. The proposed work will provide insights into the local behavior of trabecular bone fracture healing and help the fellow achieve professional maturity. Further, the in silico model has the potential to change the landscape of fracture healing research, particularly in areas of preclinical testing and personalized medicine.

  • Open Access mandate for Publications
    Funder: EC Project Code: 741883
    Overall Budget: 2,500,000 EURFunder Contribution: 2,500,000 EUR
    Partners: ETH Zurich

    Osteoporosis, one of the most prevalent degenerative diseases, is characterized by a reduction in bone mass and increased fracture risk and has been partly attributed to the decrease in mechanical usage of the skeleton. A detailed understanding of the molecular mechanisms governing load-regulated bone remodeling could therefore lead to the identification of molecular targets for the development of novel therapies. Bone remodeling is a multiscale process mediated through complex interactions between multiple cell types and their local 3D environments. However, the underlying mechanisms of how cells respond to mechanical signals are still unclear. By combining single-cell “omics” technologies with well-established tissue-scale models of bone mechanobiology, MechAGE proposes to develop the technology required to allow spatially resolved in vivo single-cell mechanomics of bone adaptation and regeneration. CRISPR/Cas technology will be exploited to generate fluorescent reporter mice to identify the different cell types involved in the bone remodeling process. By combining RNA-sequencing of single cells isolated by laser-capture microdissection with micro-finite element analysis and time-lapsed in vivo micro-CT, MechAGE will link the transcriptome of hundreds of single cells to their local mechanical in vivo environment (LivE). This will allow investigation of molecular responses of the cells to LivE changes with aging in established mouse models of bone adaptation and regeneration. In addition to in vivo mechanomics, MechAGE proposes to use cellular and multiscale computational modeling to run in silico simulations of real-world events for better understanding of diseases of aging in mice and to maximize the use of the high quality in vivo mechanomic data. Findings from MechAGE will lead to a systems level understanding of the spatio-temporal regulation of gene expression during the process of load-induced bone adaptation and regeneration in the aging mouse.

  • Funder: EC Project Code: 244947
    Partners: ETH Zurich