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Advancing cancer systems biology: introducing the Center for the Development of a Virtual Tumor, CViT.

Deisboeck TS, Zhang L, Martin S - Cancer Inform (2007)

Bottom Line: Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research.These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics.Such innovative 'systems' research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access.

View Article: PubMed Central - PubMed

Affiliation: Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA. deisboec@helix.mgh.harvard.edu

ABSTRACT
Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative 'systems' research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute's recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation.

No MeSH data available.


Related in: MedlinePlus

Multi-Scale Modeling. 3D snapshots of a virtual brain tumor at three consecutive time points (left to right), from Zhang et al. (2007). Blue color represents proliferating tumor cells, while red depicts migratory, green quiescent and grey dead tumor cells. At an early stage the proliferative tumor core appears to be completely surrounded by a cloud of migratory cells, at a later time point, however, a more heterogeneous picture emerges where ultimately a ‘tip’-population of migratory cells can be found adjacent to the location of a nutrient source (top right quadrant, not shown).
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f1-cin-05-01: Multi-Scale Modeling. 3D snapshots of a virtual brain tumor at three consecutive time points (left to right), from Zhang et al. (2007). Blue color represents proliferating tumor cells, while red depicts migratory, green quiescent and grey dead tumor cells. At an early stage the proliferative tumor core appears to be completely surrounded by a cloud of migratory cells, at a later time point, however, a more heterogeneous picture emerges where ultimately a ‘tip’-population of migratory cells can be found adjacent to the location of a nutrient source (top right quadrant, not shown).

Mentions: If a tumor is thought of as a dynamic self-organizing biosystem, one can argue that cancer is an almost ideal case to apply the considerable strengths of this new systems biology concept. This is not only because we are still far from deciphering the complexity of all the factors involved in tumorigenesis but also since the countless experimental and clinical studies devoted to it continue to generate an ever growing amount of disparate data with little chance of connecting the ‘dots’ using conventional scientific approaches only. There is no doubt then that innovative computational modeling and simulation, in conjunction with appropriately designed experiments, will rapidly become a valuable if not crucial tool for this new scientific path in cancer biology. Specifically, cutting edge multi-scale computational modeling will be able (a) to help generate experimentally testable hypotheses, (b) to integrate diverse data, and ultimately, (c) to predict outcome also for clinical purposes. Currently, mechanistic dynamical simulations and inferential data mining constitute the two main approaches in interdisciplinary cancer systems biology research with significant progress. (1) For instance, molecular pathway simulation has shown promise exemplified through the work by Araujo et al. (2005) who have developed a mathematical model to investigate combination therapy with kinase inhibitors by building upon theoretical studies of the epidermal growth factor receptor (EGFR) pathway (Kholodenko et al. 1999). Another example is Athale et al. (2005, 2006) who, based on previous works by Mansury and Deisboeck (2003, 2004), have modeled a proposed cellular phenotypic switching mechanism also in the EGFR signaling pathway. Most recently, Zhang et al. (2007) have then extended this work in order to simulate the dynamics of EGFR gene-protein interaction profiles, alternating cell phenotypes and emergent multi-cellular patterns with a three dimensional agent-based multi-scaled model (Figure 1). (2) On the other hand, because it is now possible to extract knowledge from large-scale data sets employing advanced data mining techniques (Khalil and Hill, 2005), progress has been made in detecting patterns and correlations in the data that lead to new hypotheses about possible interactions such as on the protein-protein and gene-protein level (e.g. Yeger-Lotem et al. 2004). It is thus a reasonable goal to combine these two promising paths in the future.


Advancing cancer systems biology: introducing the Center for the Development of a Virtual Tumor, CViT.

Deisboeck TS, Zhang L, Martin S - Cancer Inform (2007)

Multi-Scale Modeling. 3D snapshots of a virtual brain tumor at three consecutive time points (left to right), from Zhang et al. (2007). Blue color represents proliferating tumor cells, while red depicts migratory, green quiescent and grey dead tumor cells. At an early stage the proliferative tumor core appears to be completely surrounded by a cloud of migratory cells, at a later time point, however, a more heterogeneous picture emerges where ultimately a ‘tip’-population of migratory cells can be found adjacent to the location of a nutrient source (top right quadrant, not shown).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2666954&req=5

f1-cin-05-01: Multi-Scale Modeling. 3D snapshots of a virtual brain tumor at three consecutive time points (left to right), from Zhang et al. (2007). Blue color represents proliferating tumor cells, while red depicts migratory, green quiescent and grey dead tumor cells. At an early stage the proliferative tumor core appears to be completely surrounded by a cloud of migratory cells, at a later time point, however, a more heterogeneous picture emerges where ultimately a ‘tip’-population of migratory cells can be found adjacent to the location of a nutrient source (top right quadrant, not shown).
Mentions: If a tumor is thought of as a dynamic self-organizing biosystem, one can argue that cancer is an almost ideal case to apply the considerable strengths of this new systems biology concept. This is not only because we are still far from deciphering the complexity of all the factors involved in tumorigenesis but also since the countless experimental and clinical studies devoted to it continue to generate an ever growing amount of disparate data with little chance of connecting the ‘dots’ using conventional scientific approaches only. There is no doubt then that innovative computational modeling and simulation, in conjunction with appropriately designed experiments, will rapidly become a valuable if not crucial tool for this new scientific path in cancer biology. Specifically, cutting edge multi-scale computational modeling will be able (a) to help generate experimentally testable hypotheses, (b) to integrate diverse data, and ultimately, (c) to predict outcome also for clinical purposes. Currently, mechanistic dynamical simulations and inferential data mining constitute the two main approaches in interdisciplinary cancer systems biology research with significant progress. (1) For instance, molecular pathway simulation has shown promise exemplified through the work by Araujo et al. (2005) who have developed a mathematical model to investigate combination therapy with kinase inhibitors by building upon theoretical studies of the epidermal growth factor receptor (EGFR) pathway (Kholodenko et al. 1999). Another example is Athale et al. (2005, 2006) who, based on previous works by Mansury and Deisboeck (2003, 2004), have modeled a proposed cellular phenotypic switching mechanism also in the EGFR signaling pathway. Most recently, Zhang et al. (2007) have then extended this work in order to simulate the dynamics of EGFR gene-protein interaction profiles, alternating cell phenotypes and emergent multi-cellular patterns with a three dimensional agent-based multi-scaled model (Figure 1). (2) On the other hand, because it is now possible to extract knowledge from large-scale data sets employing advanced data mining techniques (Khalil and Hill, 2005), progress has been made in detecting patterns and correlations in the data that lead to new hypotheses about possible interactions such as on the protein-protein and gene-protein level (e.g. Yeger-Lotem et al. 2004). It is thus a reasonable goal to combine these two promising paths in the future.

Bottom Line: Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research.These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics.Such innovative 'systems' research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access.

View Article: PubMed Central - PubMed

Affiliation: Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA. deisboec@helix.mgh.harvard.edu

ABSTRACT
Integrative cancer biology research relies on a variety of data-driven computational modeling and simulation methods and techniques geared towards gaining new insights into the complexity of biological processes that are of critical importance for cancer research. These include the dynamics of gene-protein interaction networks, the percolation of sub-cellular perturbations across scales and the impact they may have on tumorigenesis in both experiments and clinics. Such innovative 'systems' research will greatly benefit from enabling Information Technology that is currently under development, including an online collaborative environment, a Semantic Web based computing platform that hosts data and model repositories as well as high-performance computing access. Here, we present one of the National Cancer Institute's recently established Integrative Cancer Biology Programs, i.e. the Center for the Development of a Virtual Tumor, CViT, which is charged with building a cancer modeling community, developing the aforementioned enabling technologies and fostering multi-scale cancer modeling and simulation.

No MeSH data available.


Related in: MedlinePlus