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Explore mathematical models and computational algorithms for understanding cancer evolution in this tutorial lecture from Princeton University's Ben Raphael at IPAM's Mathematics of Cancer workshop. Delve into the fundamental challenges of inferring cancer evolution from high-throughput DNA and RNA sequencing data, including deconvolving mutation mixtures from bulk tumor samples, addressing error rates and missing data in single-cell sequencing, modeling copy number aberrations affecting large genomic regions, and incorporating information from regional, spatial, and longitudinal sampling approaches. Learn how mathematical frameworks can be applied to trace the accumulation of genetic and epigenetic alterations that drive cancer development in tissue cells, providing essential insights for computational biology and cancer research applications.
