Low-dose experiments with light ions and microbeam and medium-transfer experiments demonstrate that radiation-damaged cells can induce genomic instability, mutations, and enhance cell killing and neoplastic transformation in nearby undamaged cells (reviewed in Ballarini et al. 2002, Smith et al. 2003, and Morgan 2003a, 2003b). Communication through gap junctions and through secreted (diffusible) messengers both contribute to the observed detrimental bystander effects. Radiation damage to the extracellular matrix (ECM) is another possible mode of action. For example, reactive oxygen species (ROSs) produced in the ECM by radiation may convert latent transforming growth factor β (TGF-β to an active complex that fosters neoplastic growth (Park et al. 2000, Barcellos-Hoff 2001). Persistent disruption of the tissue microenvironment by radiation or other means (e.g., disease, age, and wounds) may give unstable cells a selective growth advantage over normal cells and contribute to tumor formation (Barcellos-Hoff 2001, Barcellos-Hoff and Brooks 2001, Nunney 1999, Radisky 2001, Robert 2000, 2002).

On the other hand, many converging lines of evidence also indicate that multi-cellular communities (i.e., tissues) take an active role in suppressing neoplastic growth.  One of the ways in which a tissue defends against neoplastic growth is through the selective ablation of transformed cells through apoptosis (Bauer 1996, 2002, Dormann et al. 1998, Häufel et al. 1999).  Normal cells have also been shown to inhibit aberrant-cell growth by limiting proliferation (Bignami 1988a, 1988b, Stoker 1964, 1967, Stoker et al. 1966) and by inducing the terminal differentiation of transformed cells (La Rocca et al. 1989).  The effective inhibition of neoplastic growth for all of these modes of action depends critically on the density and the spatial arrangement (size) of the normal and transformed cell populations (reviewed in Bauer 1996).

The evolution of multi-cellular organisms required mechanisms to coordinate cell function and behavior, and the observed positive and negative effects that arise from cell-to-cell interactions may be due to these same homeostatic controls (Barcellos-Hoff and Brooks 2001).  Regardless, cell signaling and tissue microenvironmental factors cast doubt on cell-autonomous modes of action, a central tenet of the current radiobiological paradigm, and pose many new challenges for radiation-response modeling at the cell and tissue levels.

Multiscale Modeling

The physical, chemical and biological processes that ultimately lead to cancer span at least ten orders of magnitude on the time scale (< 10^-3 s to > 10⁷ s) and ten orders of magnitude on the spatial scale (~ 10^-10 m to 1 m).

Multi-stage carcinogenesis models, such as the two-mutation clonal expansion model (Moolgavkar et al. 1988, Moolgavkar and Luebeck 1990), capture many of the putative key steps associated with the development of cancer, and they do so using as few adjustable parameters as possible. However, the desire to keep the number of adjustable model inputs to a minimum means that the effects of many molecular and cellular processes must be lumped together. That is, the details of the specific biological mechanisms of action involved in the development of cancer must be replaced by nonspecific (average) mechanisms that capture events and processes occurring over very large time and spatial scales. However, some in vitro experiments suggest that the risks of low dose radiation exposures may be larger (Sawant et al. 2001, Mothersill et al. 2002) or smaller (Azzam et al. 1996, Redpath et al. 2001, 2003) than the risks expected using simple linear high-to-low-dose extrapolation procedures. Studies such as these indicate that we need to find practical strategies to introduce additional (more specific) biological details into the cancer modeling process.

Multi-scale modeling provides one avenue to better integrate cell signaling and microenvironmental factors into radiation response models. The multi-scale modeling approach begins by formulating relatively detailed mathematical models for specific molecular and cellular processes. For example, one model might capture key elements of the base excision repair (BER) pathway while another model relates the overall mutation rate to the onset of genomic instability and to the eventual neoplastic transformation of a cell or one of its progeny. As a first test of the postulated mechanisms of action, these lower-level models (“sub-models”) are tested against measured data from in vitro systems. To model the emergent behavior (response) of a group of cells or a tissue, these sub-models are linked together to form a “supermodel.” The links (information pathways) among the sub-models represent specific hypotheses about the key events and processes involved in the development of cancer. As a second test of the postulated mechanisms, results from the multi-scale supermodel can be compared to data from in vivo systems.

An attractive feature of the multi-scale modeling approach is that it provides a systematic method to balance the need for greater biological detail against the need for simplicity (fewer adjustable parameters).  That is, each sub-model uses a minimum number of adjustable parameters to capture the putative mechanisms of action underlying a particular endpoint of interest (e.g., mutagenesis, genomic instability or apoptosis).  The potential usefulness of multi-scale biological models is driven by the hypothesis that mechanisms of action operate in approximately the same manner in vitro and in vivo.  If mechanisms of action operate in approximately the same manner, in vitro data can be used to identify appropriate mathematical models for specific mechanisms of action.  The in vitro data may also be used help define bounds on the range of parameter values (model inputs) appropriate for in vivo systems.

The hypothesis that mechanisms and parameters are approximately the same in vitro and in vivo is reasonably well-justified for endpoints such as DNA damage and, perhaps to a lesser extent, the outcome from a pathway-specific DNA repair event (probability a lesion is correctly or incorrectly repaired).  As we progress up through the biological hierarchy to processes such as the induction of apoptosis or the regulation of cell growth or differentiation, the use of model inputs derived from in vitro data becomes increasingly questionable because of differences in the in vitro and in vivo microenvironment.  However, setting some model inputs a priori to an approximate in vitro value may still be preferable to treating all model inputs as purely adjustable.  If all model inputs are treated as adjustable, parameter identifiability issues arise (Hanin and Yakovlev 1996, Hanin and Boucher 1999).  Parameter estimation issues are a, if not the, major challenge associated with the effort to construct mechanism-based radiation response models.

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Last updated: 10 June, 2011
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