Human cancer progresses under Darwinian evolution where (epi)genetic variation alters molecular phenotypes in individual cells. Consequently, tumours at diagnosis often consist of multiple, genotypically distinct cell populations. Somatic single nucleotide variants (SNVs) are mutations resulting from the substitution of a single nucleotide in the genome of cancer cells relative to non-malignant cells. SNVs can contribute to the malignant phenotype of cancer cells, though many SNVs likely have negligible selective value. Because many SNVs are selectively neutral, their presence in a measurable proportion of cells is likely due to drift or genetic hitchhiking. This makes SNVs an appealing class of genomic aberrations to use as markers of clonal populations and ultimately tumour evolution. Advances in sequencing technology, in particular the development of high throughput sequencing (HTS) technologies, have made it possible to systematically profile SNVs in tumour genomes. This has created an opportunity to not only catalogue SNVs in tumours but also study clonal population structure through digital allele counting capacity. Probabilistic modelling provides an attractive means to analyse this data in a coherent and statistically sound manner. I will present several probabilistic models we have developed to identify SNVs, infer clonal populations structures and resolve clonal genotypes at single cell resolution. I will then discuss our recent work that has applied these models to study metastatic migration in ovarian cancer patients.