Special Issue "Latest Developments in Mathematical Oncology and Cancer Systems Biology"
Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 40747
Interests: metastasis; mathematical oncology; systems biology; computational biology; phenotypic plasticity; cellular decision-making; cancer stem cells; epithelial-mesenchymal transition
Special Issues, Collections and Topics in MDPI journals
Special Issue in Cancers: Mesenchymal-Epithelial Transition in Cellular Reprogramming and Cancer
Special Issue in Biomolecules: Cancer Systems Biology: Investigating Cancer Dynamics from a Complex Systems Perspective
Interests: integrated mathematical oncology; radiation oncology; cancer biology and evolution; cell–cell interactions; tumor–host interface; stem cells in tumor progression and treatment response; patient-specific treatment design
Cancer is a complex, adaptive and dynamic system, where the trajectory of tumor progression in a patient depends not only on specific genomic mutations, but also on emergent outcomes of signaling networks in a cell, communication among multiple tumor and stromal cells, microenvironmental parameters such as matrix stiffness, nutrient and oxygen availability, and on previous therapies given to the patient against which the tumor has evolved. From these multi-dimensional aspects emerge nonlinear cellular, tissue, and system-level dynamics that need to be quantified rigorously to better guide clinical decisions. Performing pre-clinical experiments and clinical trials for multiple specific targets and in varied dosing sequencing, and timing schedules is often too resource- and time-consuming, and therefore unfeasible. Thus, calibrated and validated mathematical models offer an attractive approach to evaluate untested protocols in silico to narrow the set of promising treatment schemas to be evaluated, to identify new treatment targets, and to reduce the risk of adverse clinical outcomes due to complex feedback mechanisms.
Mathematical models developed, calibrated and validated in close collaboration with experimental cancer biologists and clinicians can help predict a patient’s response to different treatments – both in terms of therapies and their dosage and timings. Moreover, they may offer unprecedented insights into intracellular and tissue-level dynamics aspects of unsolved clinical challenges such as metastasis, tumor relapse, and evolution of resistance against various therapies. The recent deluge of high-throughput single-cell preclinical and clinical data has further strengthened to need of various computational and statistical tools to identify the Achilles’ heel of various hallmarks of cancer and proceed towards the goal of precision medicine. Finally, integrated mathematical-experimental approaches have unraveled many emerging notions in the field such as the evolutionary game theory of cancer dynamics, the crucial role of stochasticity/non-genetic heterogeneity and phenotypic switching in cancer progression, and the design of effective adaptive therapies.
Here, we invite investigators in the interdisciplinary field of mathematical oncology and cancer systems biology to contribute their latest research articles and/or review articles and perspectives on applying the different kinds of computational, mathematical, and statistical tools and techniques to applicable biological or clinical data to train such models to better elucidate the dynamics of tumor progression, to identify novel therapeutic schemas or targets, and to design more effective therapies.
Dr. Mohit Kumar Jolly
Dr. Heiko Enderling
Manuscript Submission Information
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- Mathematical oncology
- Systems approaches to cancer biology
- Physics and bioengineering of cancer
- Adaptive therapies
- Treatment optimization
- Evolution of drug resistance in cancer
- Non-genetic heterogeneity
- Phenotypic plasticity
- Evolutionary game theory