Assoc. Prof. Jin-Ku Lee

Seoul National University, South Korea

Jin-Ku Lee is an associate professor at Seoul National University College of Medicine (SNUCM), Korea. He achieved both M.D. (2003) and Ph.D. (2013) at SNU. His research fields of interests were cancer genomics and pharmacogenomic analysis using patient-derived tumor models for precision oncology. In respect to these research areas, he has published many SCI(E) articles, including Nature genetics (2017, 2018), Genome Biology (2019) and Biomaterials (2023). In particular, his lab is focused on developing cutting-edge technologies in patient tumor organoid cultures and 3D-based drug screening accompanied with systemic identification of genomic biomarkers for drug sensitivity.

 

Prof. Jose Nacher

Toho University, Japan

He obtained his Ph.D. in Theoretical Physics from Valencia University (Spain) in 2001. From 2003 to 2007, he served as a postdoctoral fellow at the Bioinformatics Center, Institute for Chemical Research, Kyoto University (Japan). During this time, he also held a JSPS-funded research fellowship from 2005 to 2007 at the Bioinformatics Center, Kyoto University. Between 2007 and 2012, he held positions as Lecturer and Associate Professor at the Department of Complex and Intelligent Systems at Hakodate Future University (Japan), while concurrently serving as a visiting Associate Professor at Kyoto University (2011-2012). In April 2012, he joined Toho University (Japan) as an Associate Professor at the Department of Information Science, Faculty of Science, and since April 2016, he has been serving as a Professor in the same department.

Speech Title: "Unveiling the Biological Roles of Intermittent Nodes Through Network Controllability"

Abstract: Controllability methods link network analysis with control theory, offering new insights into diverse fields where systems can be represented as networks, such as molecular biology and brain science. In this talk, we briefly review some controllability models and focus on the analysis of control categories. Recent studies show that driver nodes are often tied to genes crucial for biological functions and diseases. While critical nodes—those present in all controllability solutions—are well studied, intermittent nodes, which appear in only some solutions, have received less attention. We propose an efficient algorithm to assess the importance of intermittent nodes in a Minimum Dominating Set (MDS)-based control model, called criticality. Applying this algorithm to various biological networks, from human signaling pathways to the nervous system of C. elegans, reveals the biological roles of intermittent nodes. These tools open new possibilities for studying intermittent nodes in various biological networks and their control.