Scientific Computing for Systems Biology
Previous and Current Research
The “MOdels, Simulations, and Algorithms for Interdisciplinary Computing” Group develops and applies computational methods for image-based systems biology. This includes particle methods for deterministic and stochastic spatiotemporal simulations, bio-image processing, optimization, and parallel high-performance computing. We aim at addressing significant biological challenges using novel computational methods and algorithms, without which the problem could not be solved. Our interdisciplinary group combines expertise from computer science, mathematics, physics, and engineering.
Future Projects and Goals
We develop computational methods based on the algorithmic abstraction of particles. Particle methods can cover the entire workflow of image-based systems biology from image processing to computer simulations and model/systems validation. We exploit this unifying framework to develop novel theories and tools and demonstrate them in biological applications ranging from intra-cellular transport processes to tissue development and growth. In particular, we focus on adaptive multi-resolution simulations using particles, bio-image segmentation using particle methods, and randomized algorithms for particle-based sampling and black-box optimization. All algorithms rely on efficient parallelization and implementation on multi-core computer systems using the PPM Library developed in our group.
Methodological and Technical Expertise
- mesh-free discretization schemes for partial differential equations
- Bayesian bio-image analysis
- parallel high-performance computing
- bio-inspired computation
- image-based modeling and simulation of biological processes in space and time
R. Ramaswamy, G. Bourantas, F. Jülicher, and I. F. Sbalzarini
A hybrid particle-mesh method for incompressible active polar viscous gels.
J. Comput. Phys., 291:334–361 (2015)
S. Reboux, B. Schrader, and I. F. Sbalzarini
A self-organizing Lagrangian particle method for adaptive-resolution advection–diffusion simulations.
J. Comput. Phys., 231:3623–3646 (2012)
G. Paul, J. Cardinale, and I. F. Sbalzarini
Coupling image restoration and segmentation: A generalized linear model/Bregman perspective.
Int. J. Comput. Vis., 104(1):69–93 (2013)
I. F. Sbalzarini
Modeling and simulation of biological systems from image data.
Bioessays, 35(5):482–490 (2013)
I. F. Sbalzarini, A. Mezzacasa, A. Helenius, and P. Koumoutsakos
Effects of Organelle Shape on Fluorescence Recovery after Photobleaching
Biophysical Journal 89(3):1482–1492 (2005)