TY - JOUR
T1 - Emerging patterns in tumor systems
T2 - Simulating the dynamics of multicellular clusters with an agent-based spatial agglomeration model
AU - Mansury, Yuri
AU - Kimura, Mark
AU - Lobo, Jose
AU - Deisboeck, Thomas S.
N1 - Funding Information:
This work was supported in part by grant CA69246 from the National Institutes of Health and by venture capital funding from Cornell College of Architecture, Arts, and Planning to Y.M., M.K., and J.L. The authors would like to thank especially Prof. Stuart Kauffman (Bios Group), Profs. Walter Isard and Richard Schuler (both Cornell University) for inspiring discussions as well as Dr E. Antonio Chiocca (Harvard Medical School) for his support of the Tumor Complexity Modeling Project (TCMP).
PY - 2002
Y1 - 2002
N2 - Brain cancer cells invade early on surrounding parenchyma, which makes it impossible to surgically remove all tumor cells and thus significantly worsens the prognosis of the patient. Specific structural elements such as multicellular clusters have been seen in experimental settings to emerge within the invasive cell system and are believed to express the systems' guidance toward nutritive sites in a heterogeneous environment. Based on these observations, we developed a novel agent-based model of spatio-temporal search and agglomeration to investigate the dynamics of cell motility and aggregation with the assumption that tumors behave as complex dynamic self-organizing biosystems. In this model, virtual cells migrate because they are attracted by higher nutrient concentrations and to avoid overpopulated areas with high levels of toxic metabolites. A specific feature of our model is the capability of cells to search both globally and locally. This concept is applied to simulate cell-surface receptor-mediated information processing of tumor cells such that a cell searching for a more growth-permissive place "learns" the information content of a brain tissue region within a two-dimensional lattice in two stages, processing first the global and then the local input. In both stages, differences in microenvironment characteristics define distinctions in energy expenditure for a moving cell and thus influence cell migration, proliferation, agglomeration, and cell death. Numerical results of our model show a phase transition leading to the emergence of two distinct spatio-temporal patterns depending on the dominant search mechanism. If global search is dominant, the result is a small number of large clusters exhibiting rapid spatial expansion but shorter lifetime of the tumor system. By contrast, if local search is dominant, the trade-off is many small clusters with longer lifetime but much slower velocity of expansion. Furthermore, in the case of such dominant local search, the model reveals an expansive advantage for tumor cell populations with a lower nutrient- depletion rate. Important implications of these results for cancer research are discussed.
AB - Brain cancer cells invade early on surrounding parenchyma, which makes it impossible to surgically remove all tumor cells and thus significantly worsens the prognosis of the patient. Specific structural elements such as multicellular clusters have been seen in experimental settings to emerge within the invasive cell system and are believed to express the systems' guidance toward nutritive sites in a heterogeneous environment. Based on these observations, we developed a novel agent-based model of spatio-temporal search and agglomeration to investigate the dynamics of cell motility and aggregation with the assumption that tumors behave as complex dynamic self-organizing biosystems. In this model, virtual cells migrate because they are attracted by higher nutrient concentrations and to avoid overpopulated areas with high levels of toxic metabolites. A specific feature of our model is the capability of cells to search both globally and locally. This concept is applied to simulate cell-surface receptor-mediated information processing of tumor cells such that a cell searching for a more growth-permissive place "learns" the information content of a brain tissue region within a two-dimensional lattice in two stages, processing first the global and then the local input. In both stages, differences in microenvironment characteristics define distinctions in energy expenditure for a moving cell and thus influence cell migration, proliferation, agglomeration, and cell death. Numerical results of our model show a phase transition leading to the emergence of two distinct spatio-temporal patterns depending on the dominant search mechanism. If global search is dominant, the result is a small number of large clusters exhibiting rapid spatial expansion but shorter lifetime of the tumor system. By contrast, if local search is dominant, the trade-off is many small clusters with longer lifetime but much slower velocity of expansion. Furthermore, in the case of such dominant local search, the model reveals an expansive advantage for tumor cell populations with a lower nutrient- depletion rate. Important implications of these results for cancer research are discussed.
UR - http://www.scopus.com/inward/record.url?scp=0036428799&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036428799&partnerID=8YFLogxK
U2 - 10.1006/jtbi.2002.3131
DO - 10.1006/jtbi.2002.3131
M3 - Article
C2 - 12419662
AN - SCOPUS:0036428799
SN - 0022-5193
VL - 219
SP - 343
EP - 370
JO - Journal of Theoretical Biology
JF - Journal of Theoretical Biology
IS - 3
ER -