Dr Sungyoung Shin received a bachelor’s degree in Electronics (Feb. 2000), a master’s degree (Feb. 2002)  in Control Engineering, and a PhD (Aug. 2007) in Systems Biology at University of Ulsan (South Korea),  funded by Ministry of Science and Technology (South Korea). After the completion of his PhD, he joined Laboratory for Systems Biology and Bio-Inspired Engineering (SBIE), KAIST (Korea Advanced Institute of Science and Technology) in Daejeon, South Korea. In April 2009, he was promoted to become an assistant research professor. In May 2013, he moved to Ireland and joined Systems Biology Ireland (SBI), University College Dublin as a research scientist (2013-2014) and a Marie Curie Fellow (2014-2015). In October 2015, he joined the Department of Biochemistry and Molecular Biology, School of Biomedical Science at Monash University.

Over last three decades, we have seen a paradigm shift from the traditional characterisation of individual molecules - how a single gene functions towards an understanding of interactive pathways and networks - how all genes and gene products of a cell work together. Systems biology leads this new paradigm shift for understanding the functional role of genes, proteins, metabolites and cells through their interactions within a much broader context at networks and systems levels.

The systems biology approaches to cope with the complex nature of cellular systems, covering a broad range of spatial and temporal scale rests on two key features of computational (dynamic) modelling and data integration from different sources, such as genomics, transcriptomics, proteomics.

Mathematical models based on quantitative (high throughput) measurement predict and explain how a cell reacts under different conditions, and the variation of different species in the way they react and respond to these conditions, Through this modelling approach we can get more profound understanding and insight into design principle of biological systems, allowing to create better experimental designs, and eventually to control the dynamic behaviour of a biological system.

Deploying a variety of systems biology approaches to tackle important issues such as cancer, inflammatory and cardiac diseases, my research focuses on the development of accurate and predictive computational models, and analysis of the network structure and regulation of cell signalling, in normal and disease-related contexts. Besides, as a complementary approach to mechanistic computational modelling, my research employs a broad range of artificial intelligence (AI) technologies such as a support vector machine, artificial neural network, and deep learning. Main objectives of my study are  

1. to create an accurate and predictive model to analyse the network structure and regulation of cell signalling, in normal and disease-related contexts,
2. to predict biological network responses to therapeutic interventions (e.g., synergistic drug effect),
3. to define the most vulnerable drug targets (‘druggable’) whose activity (behaviour or function) can be effectively modulated by a therapeutic,
4. to identify biomarkers that predict the responses of patients to drugs before treatment. 

Ultimately, a series of my research hopes to contribute to a better understanding of signalling networks at a systems level in normal and disease states, and enhancement of translational medicine that improve diagnosis, treatment, and prevention of disease.