Research Topic

Computational Models in Oncology: from Tumor Initiation to Progression to Treatment

About this Research Topic

Cancer is one of the deadliest diseases of our time. Whilst the war on cancer has cost many millions of dollars, the mechanisms underlying its formation, progression, therapeutic cure or control are still not fully uncovered. An interdisciplinary effort that brings together clinicians, biologists, and ...

Cancer is one of the deadliest diseases of our time. Whilst the war on cancer has cost many millions of dollars, the mechanisms underlying its formation, progression, therapeutic cure or control are still not fully uncovered. An interdisciplinary effort that brings together clinicians, biologists, and quantitative scientists is demanded. Mathematical modeling and computational simulations bring to the table sophisticated tools for analyzing experimental data as well as for systematic, quantitative and multi-scale in silico experimentation. Taken together, such interdisciplinary approach promises to shed light on the underlying rules and/or complex interactions between tumor cells, tumor and stromal cells, as well as other components of the tumor microenvironment, and ultimately predict treatment outcomes.

In this research topic we will present the state-of-the-art in integrative cancer modeling (such as theoretical models based on biological or clinical data, and experimental results influenced by underlying mathematical and physical theories) and their applications to cancer biology and treatment. This collection of papers will showcase computational models addressing the most important current challenges in oncology, such as prognostic screening, metrics of tumor cell response to treatment, cancer cell mechanotransduction, cancer stem cell biology, metastatic cascade steps, and reciprocal co-evolution of tumors and their microenvironment. Quantitative and qualitative models included in this topic will discuss tumor initiation, development of pre-invasive tumors, transition from dormancy to malignancy, tumor angiogenesis, tumor cell signaling, complexity of the cellular, physical and chemical structure of the tumor microenvironment, and various models of anticancer treatment: chemo-, radio-, immuno-, hormone and adaptive therapies. This collection could then serve as an encyclopedic resource for the breadth of mathematical and computational techniques that can be applied to tumor modeling, including ordinary and partial differential equations models, individual-cell-based models, hybrid cellular automata models, bio-fluid approaches, game theory approaches, stochastic and multi-scale modeling.


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