Research Supervisor Connect
Using an open source drug discovery approach, current research shows that it is possible to find an improved synthesis of drugs for diseases (e.g. a tropical neglected disease such as malaria) for a low price. Research towards such discovery proceeds faster because of its open source nature where all data and access is readily available to anyone. In working towards a means of effective and efficient information sharing and intelligent data searching, we are interested in understanding how the overall project functions best using a social networks perspective. Motivating questions for this study are: (1) What are the dynamics of people sharing a lot of data in an open project? (2) how does a project like this scales and self-organises? (3) Can we rely on self-organisation vs top-down management? (4) Can we learn anything from open source software projects? From a social networks perspective specifically, the research questions are: (1) what network structures are conducive to innovation and drug discovery? (2) How does one account for social and professional network properties in designing complex and dynamic open source project teams for drug discovery? (3) Is it possible to model the impact of network structure, ties and position for predicting and understanding open source drug discovery success?
Masters/PHD
This project involves using a social network approach to model social network properties of structure, position and relations in explaining the coordination process of open source drug discovery.
Collaborator: Dr. Matt Todd (School of Chemistry)
The opportunity ID for this research opportunity is 1474