Research Highlights

Research Highlights

The complexity of natural and manmade systems constantly challenges the capabilities and the robustness of computational methods, and it continues to inspire development in Computational Science. The MOSAIC group focuses on developing and applying computational methods for data analysis, simulation, and optimization of complex real-world systems. While each of these areas is vast in itself, we focus on:

  • Bio-medical image processing: Many of the modern experimental assays in the life sciences deliver data in the form of digital videos or images, rather than direct quantitative measurements. Live cell imaging has become a standard method in many areas of biology. The acquired images are, however, complex and under-explored. Robust and accurate automated image processing algorithms are required for unbiased, reproducible, and quantitative analysis of the large amounts of image data acquired. In addition, it is imperative for subsequent modeling that the confidence and reliability of the image processing results are known.
  • Multi-resolution simulations using particles: Numerical simulation of many real-world systems is complicated by the presence of multiple time and length scales. Simulations are, however, the third polar of science, alongside theory and experiment. In the sciences, they enable virtual experiments where everything is controllable and observable, catalyzing discoveries that would not be possible otherwise. The availability of robust, accurate, and computationally efficient numerical simulation methods for multi-resolution systems is thus a key prerequisite.
  • Black-box optimization: Bio-inspired computing is a promising concept for the off-line optimization of complex black-box systems and for adaptive sampling. In black-box optimization, the objective function is not known explicitly, but can only be evaluated. This situation is frequently found in model fitting and data analysis in complex systems where the governing equations are unknown. The lack of theoretical understanding and of higher-order information (e.g., about uncertainties) has, however, limited the practical application of black-box optimization.

Please choose from the selection to the left for more details.

See for our parallel simulation software library.

Previous Contributions

Some of our previous contributions include:

  • an efficient algorithm for single-particle tracking without motion priors [1] and its application to virus entry [2,3].
  • a trainable algorithm for detecting and extracting motion patterns from trajectories of moving objects and its application to virus entry [4].
  • an image segmentation framework that accounts for and corrects the optical blur 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].
  • a new particle-based image segmentation framework that is particularly well suited for 2D and 3D fluorescence microscopy [7].
  • a statistical framework for inferring interactions between objects in images [8]. This extends classical co-localization analysis to interaction analysis and allows correcting the systematic errors inherent to co-localization analysis.
  • 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 chemical kinetics [13-15].
  • a self-organizing adaptive particle method for simulating continuum models in complex and multi-scale geometries [16,17]. The number and placement of particles is automatically determined by the method, making it the most user-friendly numerical simulation scheme available to date. the first randomized optimization heuristic ever reported to robustly solve a multi-funnel problem [18] and its application to protein structures [19].
  • the PPM Library, a parallel computing middleware for hybrid particle-mesh methods [20,21]. 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 [22].
  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. 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.

  3. 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.

  4. J. A. Helmuth, C. J. Burckhardt, P. Koumoutsakos, U. F. Greber, and I. F. Sbalzarini. A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells. J. Struct. Biol., 159(3):347–358, 2007.

  5. J. A. Helmuth and I. F. Sbalzarini. Deconvolving active contours for fluorescence microscopy images. In Proc. Intl. Symp. Visual Computing (ISVC), volume 5875 of Lecture Notes in Computer Science, pages 544–553, Las Vegas, USA, November 2009. Springer.

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

  8. J. A. Helmuth, G. Paul, and I. F. Sbalzarini. Beyond co-localization: inferring spatial interactions between sub-cellular structures from microscopy images. BMC Bioinformatics, 11:372, 2010.

  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. C. L. Müller, B. Baumgartner, and I. F. Sbalzarini. Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes. In Proc. IEEE Congress on Evolutionary Computation (CEC), pages 2685–2692, Trondheim, Norway, May 2009. IEEE.

  19. C. L. Müller, I. F. Sbalzarini, W. F. van Gunsteren, B. Žagrović, and P. H. Hünenberger. In the eye of the beholder: Inhomogeneous distribution of high-resolution shapes within the random-walk ensemble. J. Chem. Phys., 130(21):214904, 2009.

  20. 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.

  21. O. Awile, O. Demirel, and I. F. Sbalzarini. Toward an object-oriented core of the PPM library. In Proc. ICNAAM, Numerical Analysis and Applied Mathematics, International Conference, pages 1313–1316. AIP, 2010.

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