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.

Speech Title: "Integrative Genomic and Pharmacological Approaches for Ovarian Cancer Research"

Abstract: Gynecologic malignancies remain a significant cause of mortality in women worldwide, necessitating innovative approaches for targeted therapies. This study presents a comprehensive analysis of gynecologic tumors, integrating genomic profiling, pharmacological screening, and tumor microenvironment assessment to advance precision medicine strategies. We established a library of 139 gynecologic tumors, including ovarian, cervical, and endometrial cancers, annotated with genomic and pharmacological data. Our findings reveal lineage-specific drug sensitivities and identify TP53 mutation as a determinant of response to PARP inhibitors. Additionally, we propose ID2 expression as a potential biomarker for olaparib response. Further investigation into homologous recombination deficiency (HRD) uncovered its association with enhanced oxidative phosphorylation and immune cell activation, particularly M1-like macrophages in high-grade serous ovarian cancer. We introduce expHRD, a novel transcriptome-based method for quantitative HRD scoring, addressing limitations in existing approaches and enabling n-of-1-style predictions across diverse malignancies. Finally, we explored the role of the tumor microenvironment in epithelial ovarian cancer heterogeneity, identifying distinct subgroups with differential responsiveness to growth factors. Our analysis reveals the importance of G protein-coupled estrogen receptor signaling in shaping molecular subtypes and potentially contributing to an immunosuppressive microenvironment. These findings collectively provide crucial insights into the molecular landscape of gynecologic cancers and offer new avenues for personalized therapeutic strategies.

 

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.

 

Prof. Ovidiu Radulescu

University of Montpellier, France

Ovidiu Radulescu is a Professor of Systems Biology at the University of Montpellier. He obtained his PhD in Theoretical Solid State Physics from the University of Paris 11, Orsay. He also holds an MS degree in Probability Theory from the University of Marne-la-Vallée and a higher doctorate (HDR) in Applied Mathematics from the University of Rennes 1. Previously, he was a postdoctoral researcher at the Institute of Theoretical Physics in Nijmegen, followed by a postdoctoral position at the IRC in Polymer Science and Technology and the Physics Department of the University of Leeds. He later served as an Assistant Professor in Mathematics at the University of Rennes 1 and was an associate member of the French National Institute for Research in Computer Science and Automation (INRIA). His current scientific interests include dynamic modeling and machine learning of biological systems.

Speech Title: "Hierarchical Principles for Machine Learning in Computational Systems Biology"

Abstract: Modularity is recognized as an organizing principle of biological systems. Biological function can be more easily understood by decomposing a large system into smaller parts called modules. Similarly, hierarchical decomposition can facilitate model training and formal analysis in computational biology. Instead of solving these difficult problems directly, one first solves them on sub-models and then extends the solution recursively to the full model. Hierarchical machine learning can also be implemented on hierarchies of models obtained from one another through model reduction. To avoid overfitting, simpler models are trained when available data is limited, while more complex models are used when richer data is available, using the already trained model to constrain the learning of the more complex ones. These principles are general and can be applied in many areas. I will discuss algorithms for hierarchical decomposition and learning, using examples from signaling pathways relevant to cancer and neuroscience.

 

Assoc. Prof. Yingqiu Xie

Nazarbayev University in Astana, Kazakhstan

Dr. Yingqiu Xie is a distinguished researcher specializing in molecular and biochemical mechanisms of cancer progression and precision cancer therapeutics using multiple approaches including nanotechnology and bioinformatics. He earned his Ph.D. in Genetics from the Institute of Genetics and Developmental Biology at the Chinese Academy of Sciences. Currently, Dr. Xie serves as a faculty member in the Department of Biology at Nazarbayev University in Astana, Kazakhstan. Dr. Xie's research primarily focuses on understanding the molecular underpinnings of cancer and developing targeted therapeutic strategies. His work encompasses the study of cell death mechanisms, signal transduction pathways, and the identification of novel drug targets. He has made significant contributions to the scientific community, authoring numerous research articles and reviews in esteemed journals. In addition to his research, Dr. Xie is actively involved in journal peer review, editorial committee, grant review committee, and talks in conferences. His dedication to advancing cancer research and mentoring the next generation of scientists underscores his commitment to the scientific community.

Speech Title: "Integration of Network Pharmacology with New Technology"

Abstract: With the rapid development of nanotechnology and artificial intelligence (AI) and application in biomedical research would make a new revolution in techniques. Recently AI-based protein coding and enzyme design have been making a new direction for integration of new technology into biochemistry research. Here we will discuss our work on integration of enzyme evolution analysis with bioinformatics for nanozyme predication and network pharmacology. First we found nanozymes are predominantly classified as protein-based EC1 oxidoreductase. Then we predicted the natural products of Polygonati Rhizoma have fructose-bisphosphate aldolase of lyase function and nanoscale Goji (Lycium chinense) extract exhibits peroxidase by simulation. Network pharmacology analysis has shown the anti-viral and anticancer function of Polygonati Rhizoma. Moreover, we will discuss how to use AI for nanozyme prediction and screening with network pharmacology integrating nanotechnology.