High-throughput sequencing has opened up a new chapter in the study of molecular evolution and genetics by allowing deep sequencing of whole populations of organisms and cells.
We are in a unique position to study in detail how genetic composition of populations change as they respond to external pressures such as drug therapies. We can ask: What is the role of genetics in a person's susceptibility to develop a cancer, or another potentially fatal disease? Are the observed differences between individuals mostly a result of neutral evolution or do they bear a fitness advantage? These questions are not only interesting for understanding evolution but can also make a fundamental contribution to biomedical applications. The promise of personalised medicine will critically depend on finding and understanding molecular disease phenotypes and on developing algorithms to help bring actionable insights to clinics.
However, data alone will not solve the problem of resistance. The development of cancer or the spread of infection within a host's cell population are dynamic processes. Similarly, therapeutic interventions against them will cause time-dependent responses.
Therefore, new evolutionary-theory based computational methods and ideas are needed to analyse these data. These methods can help to characterise the emergence of drug resistance in model systems and to design experiments that ultimately lead to novel approaches in combating resistance.
Our group contributed to this effort by developing scalable methods for biomedical applications of data. We further used these data to address basic biological research questions such as how drug resistance arises.