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Understanding Cancer Dynamics and Improving Treatment Strategies Using Mathematical and Computational Oncology
Mathematical and computational oncology are rapidly emerging as critical areas of research that aim to understand the intricate and complex nature of cancer. New quantitative methodologies and techniques are constantly being developed to better understand and predict how the disease evolves and adapts to treatment. Such models are used not only to check the validity of hypotheses that are postulated to explain experimental observations, but also improve experimental design by making testable predictions or revealing counter-intuitive physical principles. The advent of in silico predictions also plays an increasingly important role in complementing and directing experimental efforts that often occur in high-dimensional feature space. Emerging clinical trials have demonstrated the useful insight provided from these methods in cancer prognosis and treatment planning. Additionally, experimental collaborations have allowed for greater data collection and alternative ways to study the evolution of the disease. As these quantitative and experimental fields continue to evolve, it is essential to highlight recent advances and encourage new collaborations among both researchers and clinicians. In this Special Issue, we invite contributions from computational and mathematical oncology researchers developing new and innovative ways to better understand cancer dynamics, from intracellular to population levels, using continuum, stochastic or hybrid modeling techniques. We invite submissions from both early career and senior researchers, and particularly encourage research with high translational value.
This Collection supports and amplifies research related to SDG 3.