Spatial Networks
Summary
We use image analysis, topological quantification and growth modeling to understand the mechanisms behind the development and function of spatial biological networks.
Details
Image Analysis
We develop novel methods to extract curvilinear structures from 3D microscopy images, and to derive their skeleton and topological structure.
Modeling
By simulating the growth of spatial networks under different assumptions, we want to understand the relationship between local growth rules and global network connectivity.
Code and Data
- MATLAB code for 3D skeletonization:
https://github.com/phi-max/skeleton3d-matlab
2. MATLAB code for 3D skeleton-to-graph conversion:
https://github.com/phi-max/skel2graph3d-matlab
3. MATLAB code and data to reproduce the figures from the "Osteocyte Connectomics" paper (New Journal of Physics 2017):
https://github.com/phi-max/OCY_connectomics (Code)
https://zenodo.org/record/291852 (Data)
4. Tool for Image and Network Analysis (TINA) - Python code developed by Felix Repp during his PhD thesis at the MPIKG and used in the two papers by Repp et al. (Bone Reports 2017 and Journal of Structural Biology 2017):
