"Making predictions is hard, especially about the future" - Nils Bohr We have a keen interest in learning fitness trajectories from timeseries study of cancer populations within controlled interventions such as CRISPR or pharmacologic methods as a means to predict response to drugs. Using extensions of population genetics theory, we are interested in predicting how cell populations will respond in the presence of a perturbation. This is indeed ‘hard’ and entails the need to decipher stochastic drift, clonal interaction and positive selection. Furthermore, drug response may not be encoded in the genome, requiring dynamic state switching through the epigenome and reflected in the transcriptome. What proportion of drug response can be explained through encoded mutations in the genome? This remains unknown. We are pursuing integrative, multimodal molecular views over time in bulk tissues and single cells as substrate to address this question.
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