# The MOSAIC Group: Scientific Computing for Image-based Systems Biology

**For more information, visit the MOSAIC Group website at** mosaic.mpi-cbg.de.

The MOSAIC Group does research in scientific computing for image-based systems biology. We combine expertise from computer science, mathematics, physics, and biology in order to develop and apply computational methods for the study of spatiotemporal biological processes in 3D. We exploit the unifying framework of particle methods for numerical simulation, image analysis, and model identification.

Since computational biology comes with a unique set of challenges, from complex geometric shapes to non-equilibrium processes, we develop and apply novel computational methods in a targeted co-design approach with the ultimate mission of understanding the Algorithms of Tissue Formation.

In our work, Theory, Algorithms, Software, and the biological Application co-evolve. We focus on the following topics as required in image-based systems biology:

- Adaptive multi-resolution simulation methods using particles to numerically solve partial differential equation models on complex 3D geometries and surfaces
- Bio-image processing and analysis using particle methods for segmentation and tracking in fluorescence microscopy images
- Parallel high-performance computing for particle methods and hybrid particle-mesh methods and software engineering of it
- Bio-inspired algorithms for black-box optimization, sensitivity analysis, and design centering for biological model learning

Some of our previous contributions include:

- an efficient algorithm for
**single-particle tracking**without motion priors [1,2] and its application to virus entry [3,4]. - an
**image segmentation**framework that accounts and corrects for the aberrations introduced by the microscope optics, allowing nanometer-precise reconstruction of the outlines of small intracellular objects [5]. It has been applied to characterize for the first time the morphodynamics of endosomes in live cells [6] and is the basis of the**Squassh protocol**for high-content screening [7]. - a new particle-based image segmentation framework that is particularly well suited for 2D and 3D fluorescence microscopy [8].
- a new class of
**exact simulation algorithms**for biochemical networks in space [9] and time [10-12]. The algorithms are orders of magnitude faster than previous ones at the same accuracy. This has enabled the discovery of fundamental effects in biochemical kinetics [13-15]. - a
**self-organizing adaptive particle method**for simulating continuum models in complex and multi-scale geometries [16,17], and its application to the first-ever numerical solution of active polar gel models of biomechanics [18]. - the
**PPM Library**, a parallel computing middleware for hybrid particle-mesh methods [19] and its domain-specific programming language [20]. PPM-based simulations can be implemented in a fraction of the traditional software development time (days instead of years) and often outperform hand-written simulation programs [21].

### References

[1] I. F. Sbalzarini and P. Koumoutsakos. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol., 151(2):182–195, 2005.

[2] N. Chenouard, I. Smal, F. de Chaumont, M. Maska, I. F. Sbalzarini, Y. Gong, J. Cardinale, C. Carthel, S. Coraluppi, M. Winter, A. R. Cohen, W. J. Godinez, K. Rohr, Y. Kalaidzidis, L. Liang, J. Duncan, H. Shen, Y. Xu, K. E. G. Magnusson, J. Jalden, H. M. Blau, P. Paul-Gilloteaux, P. Roudot, C. Kervrann, F. Waharte, J.-Y. Tinevez, S. L. Shorte, J. Willemse, K. Celler, G. P. van Wezel, H.-W. Dan, Y.-S. Tsai, C. O. de Solorzano, J.-C. Olivo-Marin, and E. Meijering. Objective comparison of particle tracking methods. Nature Methods, 11(3):281–289, 2014.

[3] H. Ewers, A. E. Smith, I. F. Sbalzarini, H. Lilie, P. Koumoutsakos, and A. Helenius. Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranes. Proc. Natl. Acad. Sci. USA, 102(42):15110–15115, 2005.

[4] Y. Yamauchi, H. Boukari, I. Banerjee, I. F. Sbalzarini, P. Horvath, and A. Helenius. Histone deacetylase 8 is required for centrosome cohesion and influenza A virus entry. PLoS Pathog., 7(10):e1002316, 2011.

[5] 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.

[6] J. A. Helmuth, C. J. Burckhardt, U. F. Greber, and I. F. Sbalzarini. Shape reconstruction of subcellular structures from live cell fluorescence microscopy images. J. Struct. Biol., 167:1–10, 2009.

[7] A. Rizk, G. Paul, P. Incardona, M. Bugarski, M. Mansouri, A. Niemann, U. Ziegler, P. Berger, and I. F. Sbalzarini. Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh. Nature Protocols, 9(3):586–596, 2014.

[8] J. Cardinale, G. Paul, and I. F. Sbalzarini. Discrete region competition for unknown numbers of connected regions. IEEE Trans. Image Process., 2012.

[9] R. Ramaswamy and I. F. Sbalzarini. Exact on-lattice stochastic reaction-diffusion simulations using partial-propensity methods. J. Chem. Phys., 135:244103, 2011.

[10] R. Ramaswamy, N. González-Segredo, and I. F. Sbalzarini. A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks. J. Chem. Phys., 130(24):244104, 2009.

[11] R. Ramaswamy and I. F. Sbalzarini. A partial-propensity variant of the composition-rejection stochastic simulation algorithm for chemical reaction networks. J. Chem. Phys., 132(4):044102, 2010.

[12] R. Ramaswamy and I. F. Sbalzarini. A partial-propensity formulation of the stochastic simulation algorithm for chemical reaction networks with delays. J. Chem. Phys., 134:014106, 2011.

[13] R. Ramaswamy, N. González-Segredo, I. F. Sbalzarini, and R. Grima. Discreteness-induced concentraiton inversion in mesoscopic chemical systems. Nat. Commun., 3:779, 2012.

[14] R. Ramaswamy and I. F. Sbalzarini. Intrinsic noise alters the frequency spectrum of mesoscopic oscillatory chemical reaction systems. Sci. Rep., 1:154, 2011.

[15] R. Ramaswamy, I. F. Sbalzarini, and N. González-Segredo. Noise-induced modulation of the relaxation kinetics around a non-equilibrium steady state of non-linear chemical reaction networks. PLoS ONE, 6(1):e16045, 2011.

[16] B. Schrader, S. Reboux, and I. F. Sbalzarini. Discretization correction of general integral PSE operators in particle methods. J. Comput. Phys., 229:4159–4182, 2010.

[17] 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.

[18] 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.

[19] I. F. Sbalzarini, J. H. Walther, M. Bergdorf, S. E. Hieber, E. M. Kotsalis, and P. Koumoutsakos. PPM – a highly efficient parallel particle-mesh library for the simulation of continuum systems. J. Comput. Phys., 215(2):566–588, 2006.

[20] O. Awile, M. Mitrovic, S. Reboux, and I. F. Sbalzarini. A domain-specific programming language for particle simulations on distributed-memory parallel computers. In Proc. III Intl. Conf. Particle-based Methods (PARTICLES), paper p52, Stuttgart, Germany, 2013.

[21] I. F. Sbalzarini. Abstractions and middleware for petascale computing and beyond. Intl. J. Distr. Systems & Technol., 1(2):40–56, 2010.