Cancer evolution: mathematical models and computational inference
| Autor: | Roland F. Schwarz, Moritz Gerstung, Niko Beerenwinkel, Florian Markowetz |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
medicine.medical_treatment
Population Inference Tumor initiation Computational biology Biology medicine.disease_cause Bioinformatics Models Biological Targeted therapy Phylogenetics Neoplasms probabilistic graphical models evolution Genetics medicine Humans education Ecology Evolution Behavior and Systematics Phylogeny Cancer Mutation education.field_of_study Phylogenetic tree population genetics medicine.disease Biological Evolution cancer progression 3. Good health Special Issue: Mathematical and Computational Evolutionary Biology (2013) |
| Zdroj: | Systematic Biology |
| ISSN: | 1076-836X |
| Popis: | Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. |
| Databáze: | OpenAIRE |
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