Hoehme Lab • Research group
Systems medicine • AI-based image analysis • Multiscale 3D liver models
We are developing the software packages TiQuant and TiSim in close collaboration with the INRIA/IfADo joint research group for Multicellular Systems Biology.

TiQuant 2

Guided interactive image segmentation using machine learning and color-based data set clustering

TiSim / CellSys

Modeling and simulations of biological tissue


Construction of models based on experimental images

Modeling and simulations of biological tissue

A previous version of our modelling software named CellSys can be found below. A comprehensive publication including open-source access to the new TiSim is currently being prepared.

CellSys Software (Predecessor of TiSim)

CellSys is a modular software tool for efficient off-lattice simulation of growth and organization processes in multicellular systems in two and three dimensions. It implements an agent-based model that approximates cells as isotropic, elastic and adhesive objects. Cell migration is modeled by an equation of motion for each cell. The software includes many modules specifically tailored to support the simulation and analysis of virtual tissues including real-time 3D visualization and VRML 2.0 support. All cell and environment parameters can be independently varied which facilitates species specific simulations and allows for detailed analyses of growth dynamics and links between cellular and multicellular phenotypes.

If you have questions, problems or comments, please contact S. Hoehme .
Activated feature sets
Deactivated feature sets
(that will be activated with regard to published papers)
. .
  • 2D monolayer growth
  • 3D spheroid growth
  • Cellular growth in tissue-like environment
  • Evolution and mutation component
  • Liver model in 3D
. .
.Copyright notice
The Cellsys software package and the corresponding documentations are free for non-commercial usage and is supplied in the presented form (all warranties are excluded). If you use the software in your publications please cite reference [8] from the list below. You may not decompile, reverse engineer, disassemble, attempt to derive the source code of, modify, or create derivative works of the software.
.Download binaries and documentation
 Windows / Linux (32 Bit)
Current version: 5.0 (Build: 3001.19)
 Documentation / User guide
Current version: 0.16

.Selected references
8. Hoehme, S. and Drasdo D. (2010) 
A cell-based simulation software for multicellular systems 
Bioinformatics (Accepted)
This reference should be used for citations.
7. Hoehme, S., Brulport, M., Bauer, A., Bedawy, E., Schormann, W., Gebhardt, R., Zellmer, S., Schwarz, M., Bockamp, E., Timmel, T., G. Hengstler, J.G., and Drasdo, D. (2010).
Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration
Proc. Natl. Acad. Sci. (USA), 107(23), 10371-10376. The publication is a PNAS open access article.
6. Hoehme, S. and Drasdo D. (2009)
Biomechanical versus nutrient control: what determines the growth dynamics of mammalian cell populations ?
Mathematical Population Studies. (in press)
5. Rohrschneider M., Scheuermann G., Hoehme, S. and Drasdo D. (2007)
Shape Characterization of Extracted and Simulated Tumor Samples using Topological and Geometric Measures
IEEE Engineering in Medicine and Biology Society
4. Hoehme, S., Hengstler J.G., Brulport M., Schäfer M., Bauer A., Gebhardt R. and Drasdo D. (2007)
Mathematical modelling of liver regeneration after intoxication with CCl4
Chemico-Biological Interaction, 168, 74-93.
3. Drasdo, D., Hoehme, S. and Block, M. (2007)
On the Role of Physics in the Growth and Pattern Formation of Multi-Cellular Systems: What can we Learn from Individual-Cell Based Models?
Journal of Statistical Physics, 128, 287-345.
2. Drasdo, D. (2007)
Center-based Single-cell Models: An Approach to Multi-cellular Organization Based on a Conceptual Analogy to Colloidal Particles.
In Single-Cell-Based Models in Biology and Medicine, Anderson, A.R.A., Chaplain M.A.J., Rejniak K.A. (Eds), Birkhäuser, Basel

1. Drasdo, D. (2005)
Coarse Graining in Simulated Cell Populations.
Adv. Complex Syst., 2 & 3, 319-363.

.Monte-Carlo simulation model variant

The model simulated in this CellSys-package is based on equations of motion for each individual cell. A Monte-Carlo simulation model variant has been described in the following references (a comparison between both is discussed in Ref. [3]):

  H. Byrne and D. Drasdo. 
Individual-based and continuum models of growing cell populations: A comparison. 
J Math Biol, 58(4-5):657–87, 2009.
  Dirk Drasdo and Stefan Hoehme. 
A single-cell-based model of tumor growth in vitro: monolayers and spheroids. 
Phys Biol
, 2(3):133–147, Sep 2005.
  D. Drasdo and S. Hoehme.
Individual-based approaches to birth and death in avascular tumors. 
Math. and Comp. Modelling, 37:1163 – 1175, 2003.
  D. Drasdo. 
Buckling instabilities in one-layered growing tissues. 
Phys. Rev. Lett., 84(18):4244–4247, 2000.
  D. Drasdo and G. Forgacs. 
Modelling the interplay of generic and genetic mechanisms in cleavage, blastulation and gastrulation. 
Dev. Dyn.
, 219:182–191, 2000.
  Drasdo, Kree, and McCaskill. 
Monte carlo approach to tissue-cell populations. 
Phys. Rev. E
, 52(6):6635–6657, 1995.