Emanuela Manea is a General Pediatrician with 8 years of experience practicing medicine in the European Union. She recently completed a Fellowship in Inherited Metabolic Disorders in London, where she continues to work as Consultant in General Paediatrics.
Ever since medical school, I have been fascinated by cellular signalling and its pathology. This was the topic of my medical graduation paper. It’s also what attracted me to the Systems Biology and Biotechnology Specialization on Coursera. One of my best friends recommended Coursera to me due to my interest in biotechnology.
Although I was already familiar with cellular processes and technologies such as genome sequencing and mass spectrometry-based proteomics, the Systems Biology and Biotechnology Specialization was an eye-opening learning experience that changed my perspective on the ways I could help advance medicine.
Through the course, I learned how data can be used to create computational models that offer insights into how metabolic and signalling pathways work. From there, I was able to generate predictions that can be tested in the lab. I also learned how big data can be integrated and statistically analyzed to understand system-level behaviour in cancer.
As I was completing the Specialization, I came across 2 patients with a rare class of glycosylation disorder: GPI-anchor defects. To get a better understanding of these disorders, I conducted research using 3 big data platforms: Uniprot, Reactome, and Omim. The study and its conclusions were recently published in Molecular Genetics and Metabolism Reports.
After completing the Systems Biology and Biotechnology Specialization, I feel that I’ve only scratched the surface of what I can achieved with mathematical modelling. I feel drawn to research that leads the way forward, rather than applying current status-quo research techniques to clinical settings.
After receiving encouraging feedback on my published work, I want to continue my research and eventually apply for a PhD Program. Today, I’m exploring ways to create more personalized treatments by better understanding how metabolic pathways relate to each other.
