Systems Biology: towards understanding the molecular complexity underpinning pathogenesis

Speaker: 
Dr Fatemeh Vafaee
Affiliation: 
School of BABS, UNSW
Date: 
18 August 2017 - 3:00pm
Venue: 
Rountree Room 356, Level 3, Biological Sciences Building D26
Abstract: 

The advent of technological breakthroughs has provided a wealth of omics data which has provoked a fundamental transformation of the science of biology. Biology has increasingly become an information-based discipline focusing on “systems” approaches to decipher the complexity of biological systems in a holistic and dynamical manner. This seminar will portray an overview of Dr Vafaee’s research in the emerging field of Systems Biology on different topics including systems-level biomarker discovery which aims to integrate multiple data sources, network and temporal information using advanced computational approaches to identify novel, precise biomarkers for disease early-detection and prognosis. The talk will also briefly touch upon interactome reconstruction, network-based functional genomics, and ‘deep’ -omics. The latter is to bring ‘deep learning’—a new generation of machine learning technologies—to find patterns in massive -omics datasets and infer computer models which precisely predict and interpret phenotypes.

Biography: Dr Fatemeh Vafaee received her BSc in mathematics and computer engineering from Sharif University of Technology in 2006 and awarded her PhD in 2011 in machine learning and artificial intelligence at the University of Illinois at Chicago. Fatemeh then conducted an interdisciplinary postdoctoral fellowship at University of Toronto and Ontario Cancer Institute on integrative computational biology and cancer informatics. She then joined the University of Sydney and Charles Perkins Centre as a Research Fellow in systems biology. Fatemeh is now a Senior Lecturer at UNSW School of Biotechnology and Biomolecular Sciences. Her research is focused on applying a 'systems' way-of-thinking triggered by advanced computational approaches to integrate diverse datatypes towards a better understanding of biological systems and unravelling the molecular complexities underlying diseases.