Modeling Cartilage Degeneration by Combining Systems Biology and Biomechanical Approaches

• Applicant: Dr. Michael Neidlin
• Subject Area: Orthopaedics, Traumatology, Reconstructive Surgery ; Bioinformatics and Theoretical Biology ; Medical Physics, Biomedical Technology
• Term: from 2017 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 387071423

One of every six people is affected by arthritis in the developed countries. Osteoarthritis, the most common type of arthritis, is characterized by cartilage degeneration leading to severe pain, restriction of joint motion and reduction of the overall quality of life. Currently, this disease is treated with pain-relief drugs that simply delay joint replacement and do not affect cartilage degeneration. The goal of this project is to understand cartilage degeneration and deliver compounds with proven in vitro efficacy.To accomplish this task, we follow a multidisciplinary and holistic approach involving systems biology and biomechanics. Systems biology will be applied to analyze the disease and discover drug treatment opportunities on the cellular level. High-throughput proteomic measurements and cell signaling networks in combination with machine-learning algorithms will be used to accomplish this task. The identified compounds for disease treatment will then be tested in vitro with a real time monitoring device of cartilage degeneration and quantitatively evaluated with tools from experimental and computational biomechanics. Thus, there is a shift from the cellular level to the tissue level. Altogether a connection between intra -and extracellular mechanisms and the mechanical behavior of articular cartilage will be made.The studies in this research project are spanned over different scales, providing new and innovative insights in osteoarthritis research and generating results for the treatment of a disease with high medical, economic and social impact.

• DFG Programme: Research Fellowships
• International Connection: Greece
• Host: Leonidas Alexopoulos, Ph.D.