* Modelling Cancer Growth and Development Mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. Network-based modeling and multi-scale modeling have begun to show promise in facilitating the process of effective cancer drug discovery. Using a<unk>  network modeling approach, Schoerberl et al. identified a previously unknown, complementary and potentially superior mechanism of inhibiting the ErbB receptor signaling network. ErbB3 was found to be the most sensitive node, leading to Akt activation; Akt regulates many<unk> cal processes, such as proliferation, apoptosis and growth, which are all relevant to tumor progression. This target driven modelling has paved way for first of its kind clinical trials. Bekkal et al. presented a nonlinear model of the dynamics of a cell population divided into proliferative and quiescent compartments. The proliferative phase represents the complete cell cycle (G (1)-S-G (2)-M) of a population committed to divide at its end. The asymptotic behavior of solutions of the nonlinear model is analysed in two cases, exhibiting tissue homeostasis or tumor exponential growth. The model is simulated and its analytic predictions are confirmed numerically. Furthermore, advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved<unk> cal interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. * Cancer Genomics * Statistical and mechanistic modelling of cancer progression and development * Clinical response models / Modelling cellular response to therapeutic interventions * Sub-typing in Cancer. *<unk>  Oncology - Clinical application of<unk>
Cancer systems biology
Cancer research
Comparative oncology
HIV/AIDS research
Cellular neuroscience
Clinical engineering
Spinal cord injury research
Predictive medicine