Dr. Alexander Schug
Methods based on statistical analysis of large sets of data have led to groundbreaking discoveries in diverse scientific fields. These range from physics by explaining phenomenological results to applications in chemistry, the life sciences but also in, e.g., social sciences or economics. In the past two decades, as one of the results of the human genome project, sequential genomic data has experienced an exponential growth. This wealth of genomic data has boosted research in analyzing molecular evolution. One striking example is tracing residue co-evolution in biomolecules to predict spatial adjacencies. These can be exploited in biomolecular structure prediction even on large scales or for experimentally poorly accessible systems. Apart from the structural insight, the statistical model can also be interpreted in terms of fitness landscapes which allows to make, e.g., predictions on antibiotics resistance, drug design, biological signaling, epistatic effects or protein/ protein interactions. Another impressive example is the analysis of molecular evolution with the explicit aim of better understanding disease, such as HIV-1 viral evolution or immune system strategies to recognize and fight pathogens. Overall, understanding molecular evolution is a prime example how knowledge from statistical analysis allows for paradigm change in the life sciences.