Periodic Reporting for period 1 - CharFL (Characterizing the fitness landscape on population and global scales)
Reporting period: 2019-01-01 to 2020-06-30
Can two negative mutations make a positive impact? What about many negative mutations? What about a mix of negative and positive mutations? What are the rules governing their interactions? These are the types of questions we aim to understand. In our work, we are focusing on the study of the interaction of thousands of mutations in different contexts. We are looking at mutations that influence the function of a specific protein. We are looking at random mutations, and also at mutations that have been selected in the course of recent population dynamics. Towards the end of the project, we are hoping to take what we have learned from our experiments and to create a set of mathematical models that would allow us to make simple predictions for different proteins.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
The first part of the project up to the period covered in this report revolved around three research directions. First, we created many thousands of artificial mutations and gene combinations. We then investigated the nature of how mutations interact. The large libraries of mutations were tested in the lab, which allowed us to look at the impact of many different combinations of mutations. Now that we have this data, we are starting to come up with various theoretical approaches to see if we can detect any general rules about their interaction. In parallel, we have studied the interaction of mutations that have been undergoing evolutionary change in standing populations.
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
Our work is ongoing, but we hope to be able to achieve an understanding of how mutations interact. Presently, we have finished a set of experiments that provide the raw data for our modeling. In the end, we will be using a combination of machine learning tools and analytical mathematical approaches to determine whether or not we can predict the nature of how mutations interact.