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Complex Systems

Brain Tumor Center @ MGH 

Neurosurgery @ MGH

Research @ MGH Neurosurgery

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1. Modeling Tumor Growth:

tumormodelprinceton.gif (4548 bytes) Figure 5:   3D-Cellular Automaton Model: Shown is the central slice of a human GBM tumor (volume = 65 cm3) roughly 3 month after the expected time of diagnosis. The outermost red cell fraction corresponds to highly metabolizing and rapidly dividing tumor cells, whereas the yellow region consists of quiescent (alive but non-proliferative) tumor cells. The innermost black tumor region corresponds to an area of apoptotic and necrotic cells. (Compare with Figure 3).
For more details please see: Kansal A.R., Torquato S., Harsh IV G.R., Chiocca E.A., and Deisboeck T.S.: Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J. Theor. Biol. 203: 367-382, 2000.

2. Modeling Clonal Competition:

Fig9.JPG (74774 bytes) Figure 6: Cross-section of a tumor, showing the emergence and dominance of a more rapidly growing secondary tumor clone. The red region corresponds to the proliferating cells of the primary clone and the blue region depicts those of the secondary strain. Yellow and black fractions correspond again to the quiescent and necrotic regions of either strain (compare with Figure 5). The cross-sections are taken 2.5 mm from the tumor’s central plane. (a) Depicts the tumor roughly 1 month after the initial mutated CA cell was introduced in the simulation, (b) 5 months after the mutation, (c) 10 months after the mutation and (d) 20 months after the mutation. Note that the center of mass of the tumor shifts before being forced back by boundary conditions.
For more details please see: Kansal A.R., Torquato S., Harsh IV G.R., Chiocca E.A., and Deisboeck T.S.: Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J. Theor. Biol. 203: 367-382, 2000.

3. Modeling Tumor Invasion:

"under construction"

4. Modeling Tumor Heterogeneity:

(click image for larger view)

Figure 7: Depicted is the 3D-growth of a brain tumor over time, showing the entire tumor (top row) and its central slice (bottom row) respectively. The different colors correspond to proposed genetic-net-states of tumor cell populations, which in turn are linked to distinct epigenetic traits. The purpose of this model is to specifically investigate the impact of environmental factors on the regional genetic and epigenetic composition of malignant brain tumors. Since such heterogeneity is thought to be essential for the emergence of treatment resistance in these tumors, a better understanding of the underlying dynamics is crucial for the development of novel treatment strategies.

5. Modeling Tumor Cell Clusters:

Figure 8: Shown is a simulation using an agent-based computer model. The clusters represent dynamically emerging tumor cell aggregates as studied in experimental settings. The hypothesis is that in reality secondary as well as recurrent brain tumor foci can emerge from such microscopic cell clusters. Since these structures would be below the all current imaging thresholds, computer animations such as this one may help understand the importance of these structures for the dynamics of the entire tumor system as well as for future treatment strategies. The model describes the emergence of these structural elements depending on guiding environmental factors. In this particular animation, a nutrition source is located in the top lattice corner but is non-replenished during the run. Tumor cells aggregate in this area, consume the nutrients and move on once the nutrient concentration declines.
Figure 9: Shown is a different simulation using the aforementioned model. In this particular animation the nutrient source is replenished, much like a blood vessel in the real brain. In comparison with Figure 8, tumor cells now tend to be much less mobile, i.e. stay attached to the growing mass. Thus the model describes two variations of spatio-temporal tumor systems growth: migratory-invasive behavior and expansive volumetric growth. Both patterns are likely to occur in real (brain) tumors depending on various intrinsic and extrinsic factors.
For more details please see: Mansuri Y., M. Kimura, Lobo J., and Deisboeck T.S.: Emerging pattern in tumor systems: simulating the complex dynamics of multicellular cluster structures by using a novel agent-based spatio-temporal agglomeration model. J. Theor. Biol. In press.

Disclaimer: The information and reference materials contained herein is intended solely for  the information of the reader. It should not be used for treatment purposes, but rather for  discussion  with the patient's own physician. Information on this site. Copyright99.
CONTACT INFORMATION: All general inquiries about the project as well as all questions regarding assay engineering, underlying tumor modeling concepts and complex biosystems modeling should be sent to Thomas S. Deisboeck, M.D  
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