Research.

Many diseases become manifest at the multi-cellular level. In such cases the analysis of the intracellular regulatory processes is insufficient both to understand the mechanisms that led to the diseases and to optimize their therapy.We use methods from Computer Science, Physics and Mathematics to analyze and model multicellular systems on different lengths and time scales.

Since 2005 our group has been involved as PI in three EU projects, four BMBF (German Federal Ministry of Education and Research) projects and one ANR-grant (see funding section).

The major aims of the Research Group are:

 

A

The development of mathematical tissue models at different hierarchical levels. The model development is in each case guided by biological questions.

 

B

The applications of the models.

 

C

The data analysis at the interface between experiments and modeling.

         
  A: Structure formation in tissues as well as mal-functions on the multi-cellular level are inherently of multi-scale nature. Modifications on the molecular level by intrinsic or extrinsic factors affect the architecture and function on the multi-cellular tissue level. Much of the current research so far focuses on the analysis of intracellular pathways, genetic and metabolic regulation on the intracellular scale and on continuum equations for local densities of cells to capture multi-cellular objects on large spatial scalesbut only recently increasing effort is made on the interface between both: individual cell based models (IBMs) which permit to include the molecular information on one hand and to extrapolate to the multi-cellular tissue level on the other hand. In order to bridge the existing gap we have studied different approaches:
  • intracellular regulation networks [A1,A2]

  • lattice-free IBMs [A3]

  • cellular automaton (CA) models [A4]

  • continuum models [A5]. 

 
[A1]
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.
[A2]
Hoehme S and Drasdo D. (2010) 
A cell-based simulation software for multicellular systems. 
Bioinformatics (In press)
[A3]
A. Braeuning, Y. Singh, B. Rignall, A. Buchmann, S. Hammad, A. Othman, I.v. Recklinghausen, P. Godoy,
S. Hoehme, D. Drasdo, J.G. Hengstler, M. Schwarz (2010) 
Phenotype and growth behavior of residual β-catenin-positive hepatocytes in livers of β-catenin-deficient mice.
Histochemistry and Cell Biology (Accepted)
[A4]
Block, M., Schoell, E. and Drasdo, D. (2007)
Classifying the growth kinetics and surface dynamics in growing cell populations.
Phys. Rev. Lett. (accepted)
[A5]
Drasdo
Coarse Graining in Simulated Cell Populations
Adv. Complex Syst., 2 & 3, 319-363 (2005)
   
 

B: Besides the methodical aspects we focus on a number of applications:

  • unstructured cell populations growing in monolayer [B1,B2]

  • multicellular spheroids [B3,B4]

  • biotechnological applications such as the optimization of cell yield of MDCK-cells for vaccine production

  • complex tissue architectures in regenerative tissues such as the regeneration of liver lobules after toxic damage [B5]

The applications are guided by quantitative comparisons to experimental data either from published knowledge or generated by experimental partners. 

 
[B1]
D. Drasdo, N. Jagiella, I. Ramis-Conde, I. Vignon-Clementel, W. Weens. (2009)
Modeling steps from a benign tumor to an invasive cancer: examples of intrinsically multi-scale problems in: From single scale-based models to multiscale modeling 
(eds. A. Chauviere, L. Preziosi, C. Verdier) (in press)
[B2]
M. Radszuweit, M. Block, J.G. Hengstler, E. Schöll, D. Drasdo . (2009)
Comparing the growth kinetics of cell populations in two and three dimensions 
Phys. Rev. E, 79, 051907
(Selected for Virtual Journal of Biological Physics Research)
[B3]
S. Hoehme and D. Drasdo. (2009)
Biomechanical versus nutrient control: what determines the growth dynamics of mammalian cell populations ? 
Mathematical Population Studies, 1547-724X, Volume 17, Issue 3, 2010, 166–187.
[B4]
Drasdo and Hoehme
A single-cell-based model of tumor growth in vitro: monolayers and spheroids

Phys. Biol. 2:133-147 (2005)
[B5]
Hoehme, Hengstler, Brulport, Schäfer, Bauer, Gebhardt and Drasdo. (2007) 
Mathematical modelling of liver regeneration after intoxication with CCl4
 
Chemico-Biological Interaction, 168, 74-93
   
 

C: The adjustment of the models developed in (A) to applications requires data analysis both, of molecular data such as gene expression profiles and of image data such as spatial-temporal growth pattern. For this purpose we recently considered:

  • on the molecular level the reconstruction of gene regulatory networks from single cell expression data [C1]

  • on the multi-cellular level the geometric and topological measures to quantify tumor shapes [C2]

 
[C1]
A. Braeuning, Y. Singh, B. Rignall, A. Buchmann, S. Hammad, A. Othman, I.v. Recklinghausen, P. Godoy,
S. Hoehme, D. Drasdo, J.G. Hengstler, M. Schwarz (2010) 
Phenotype and growth behavior of residual β-catenin-positive hepatocytes in livers of β-catenin-deficient mice.
Histochemistry and Cell Biology (Accepted)
[C2]
Ramis-Conde I, Chaplain MA, Anderson AR, Drasdo D. (2009)
Multi-scale modelling of cancer cell intravasation: the role of cadherins in metastasis.
Phys Biol. 2009 Mar 25;6(1):16008 - 16020.
[C3]
D. Drasdo, N. Jagiella, I. Ramis-Conde, I. Vignon-Clementel, W. Weens. (2009)
Modeling steps from a benign tumor to an invasive cancer: examples of intrinsically multi-scale problems in: From single scale-based models to multiscale modeling 
(eds. A. Chauviere, L. Preziosi, C. Verdier) (in press)