Glioblastoma is the most common and the most lethal malignant brain tumor. In vitro cell culture of glioma cells is essential for the discovery and development of novel anticancer treatments. However, the optimal cell culture medium required to culture different types of glioma cells is unknown. An important open challenge is to computationally model human patient glioblastoma metabolism and use that knowledge to design 3D glioma cell culture conditions that mimic the in vivo environment and are optimal for the growth of different glioma cells that mimic tumor cells as closely as possible. In this project, experimental data-driven constraint-based computational modeling of patient-derived and in vitro glioma metabolic networks shall be used to predict the optimal metabolomic, geometric and fluid flow environment for 3D glioma cell culture.
· Development of a genome-scale, constraint-based, mechanistic computational model of metabolism in glioma cells
· Integration of multi-omic data to develop context-specific models of glioma metabolism
· Prediction of the optimal fresh culture medium composition and supply rate for 3D culture of various glioma cells.
· Actively collaborate in an interdisciplinary, international team including partners with expertise in computational biology (Dr. Nameeta Shah), single-cell omics (Dr. Woong-Yang Park) and glioblastoma treatment (Dr. Chul-kee Park)
· Contribute to the writing of scientific publications, progress reports, and grant applications.
· Present at national and international conferences.
· Acquire working skills in time and project management
· Participate in outreach activities, seminars, and workshops for the development of scientific and transferable skills.
Essential Requirements: (i.e criteria for shortlisting)
· Master degree (or equivalent) in Computational Biology, Bioengineering, or the like.
· Demonstrable interest in cancer.
· Demonstrable interest in interdisciplinary, international collaborative research.
· Demonstrable experience in scientific programming
· Excellent working knowledge of spoken and written English.
Demonstrable experience in the constraint-based reconstruction and analysis approach is highly desirable.
Experience in cancer biology, experimental biomedical science or applied mathematics (optimisation) or statistics is desirable.
Stipend: 18,500 per annum, for 3 years.
Fees: No fees for applicants from EU/UK, but fees of 10k per annum for non-EU students.