PhD Position: 3D cell-based tissue simulation framework

We develop data-​driven, predictive models of biological signaling networks with a view to gain a comprehensive understanding of the dynamics and evolution of cellular signaling.

The experimental characterization of developing tissues is often difficult. Cell-based simulations have long been used to study the contribution of single cells to the overall architecture and dynamic behavior of tissues. However, computational studies have long been limited by the level of detail that can be represented. The Iber group has recently developed a highly efficient 3D cell-based simulation framework that allows for the fast and efficient simulation of large tissues at an unprecedented resolution [1]. Tissues can be grown from a single cell to a tissue with 100'000 cells within a week. The tissue simulation framework also represents cavities and extracellular matrix, and cells can freely deform and detach such that complex developmental processes can also be represented and simulated. 

[1] Runser, S., Vetter, R. and Iber, D. (2023). 3D Simulation of Tissue Mechanics with Cell Polarization. bioRxiv; Tweetorial.

We are looking for a PhD student with a strong background in computational science, computational biology or applied mathematics to extend a recently developed C++- based cell-based tissue simulation framework to represent further biophysical features, and to include a numerical solver for reaction-diffusion equations. You will integrate available image processing and meshing algorithms to create an integrated framework that simulates imaged tissues at cellular resolution. The framework will be applied to morphogenetic problems in developmental biology using imaging data. The simulations will be performed by the student on the cluster computing infrastructure of ETH. Your tasks will also include model calibration, extensive validation and testing, parameter screening and optimization, simulation post-processing, quantitative comparison between numerical results and imaged tissues, and data visualization.

The project would suit students with a strong background in numerical modeling and C++ programming. Prior knowledge of the finite difference method (FDM), finite element method (FEM), or similar is desirable. Experience with cluster computing is welcome, but not required. A general interest in biophysics, developmental biology and biochemical signaling would be a plus. You must be able to work with a high level of independence and dedication. We particularly welcome applications from Master students interested in carrying out their Master thesis on the topic before starting a PhD.

• An internationally highly competitive salary and further benefits
• Excellent working conditions, with the possibility to work partly in the home office
• A friendly and collaborative working environment