Prof. Jirong Long

Vanderbilt University, USA

Dr. Jirong Long is a Professor of Medicine at Vanderbilt University School of Medicine. Her research centers on identifying novel genetic, epigenetic, and microbiota biomarkers for cancer risk, deepening insights into cancer development mechanisms. She currently leads five major NIH-funded studies: three focused on breast cancer—a transcriptome-wide association study (TWAS, R01CA235553), a proteome-wide association study (PWAS, R01CA293996), and a methylation-wide association study (MeWAS, R01CA247987)—and two targeting lung cancer: a combined MeWAS/TWAS study (R01CA249863) and a social epigenomics study (R01MD015396). Dr. Long has authored over 300 papers, many published in high-impact journals. She is a frequent invited speaker at national and international academic conferences. She has mentored more than 30 trainees and faculty members, significantly shaping their careers. Dr. Long plays a pivotal role in the molecular and genetic epidemiology programs at the Vanderbilt Epidemiology Center and the Vanderbilt-Ingram Cancer Center.

 

Prof. Jiang Gui

Dartmouth College, USA

My research program sits at the intersection of biostatistics, computational biology, and machine learning. I am interested in understanding the role of genomic variation and environmental context in disease susceptibility. Success in this important public health endeavor will depend critically on the amount of non- linearity in the mapping of genotype to phenotype and our ability to address it. I have developed several novel non-parametric machine learning algorithms to detect and characterize gene-gene and gene- environment interactions in the absence of statistically significant main effects. Those methods have been applied to several large population based genetic association studies and identified a few gene-gene interaction models that would be missed by regular regression methods.

Assoc. Prof. Hamed S. Najafabadi

Human Genetics at McGill University, Canada

Dr. Najafabadi is an Associate Professor of Human Genetics at McGill University, with expertise spanning genomics, computational biology, and machine learning. His research focuses on developing statistical and machine learning methods for modelling genomes and transcriptomes, gene regulatory programs, and complex multi-omic data. His group combines these methods with patient omics data to uncover the basis for development and progression of cancer, and to develop new omics-based diagnostic and prognostic tools. His work has been recognized with several national and international honours, including a Sloan Research Fellowship in Computational and Evolutionary Molecular Biology and a Canada Research Chair in Systems Biology of Gene Regulation.

 

Dr. Michail Smyrnakis

Science and Technology Facilities Council (STFC), UK

Dr. Smyrnakis is leading the AI group of Hartree Centre The Hartree Centre is a UK research laboratory that focuses on translating cutting-edge research into practical solutions for industry, academia, and the public sector. It accelerates innovation through high-performance computing (HPC), data analytics, and artificial intelligence technologies. He has a special interest in the applications of machine learning to the life sciences, particularly in approaches inspired by network biology. His work focuses on implementing a wide range of graph-based methods including graph neural networks (GNNs), graph embeddings, and on developing novel algorithms that integrate multi-modal omics data such as genomics, transcriptomics, proteomics, and metabolomics. Their work also involves creating methodologies grounded in statistical significance and robustness, designed to handle datasets of varying scales, from a few samples to thousands, to address diverse industry-related challenges.

Prof. Stephani Joy Y. Macalino

De La Salle University, Philippines

Stephani Joy Y. Macalino received her undergraduate degree in Biochemistry from University of the Philippines-Manila and her doctorate degree in Pharmaceutical Sciences from Ewha Womans University (Seoul, South Korea). Her specialization lies in computer-aided drug discovery and application of molecular concepts to investigate structural and functional changes in proteins. This is evidenced by her undergraduate and doctoral thesis covering topics such as computational tuberculosis drug discovery, molecular mechanisms of a tetraspanin-like protein, and fragment-based optimization of natural products for anticancer drug discovery. Currently, she is working on computational researches and collaborations involving different diseases, such as cancer, HIV, dengue, and others.

Speech Title: "Understanding the Structure and Function of Glutathione Peroxidase 4 via In Silico Mutation Analysis"

Abstract: Glutathione Peroxidase 4 (GPx4) is a critical enzyme for survival as it is the only enzyme that can catalyze oxidative damage repair that accumulates in the membrane. GPx4 has a unique structure that bind to a variety of substrates, which can bind to different sites on the target and may lead to allosteric regulation of protein function. There are currently only a few studies available describing the GPx4 enzyme structure, and thus, this research aimed to characterize GPx4 using protein structural analysis techniques, particularly computational mutation and Protein Energy Networks (PENs). Three GPx4 variants were modeled - a) U46C (treated as the WT), b) D21A, and c) R152H – and subjected to 100 ns of molecular dynamics simulation using GROMACS. Centrality and shortest paths analyses were performed on the resulting trajectories using gRINN to determine potential critical residues for the internal communication of the protein structure. Residues in the established binding site and allosteric site were found to have higher centrality values relative to the rest of the residues. Moreover, Lys31, Asp101, Lys140 were determined to be critical, with the latter two frequently involved in the shortest paths communication (Asp101Leu71Val39Lys140) related to the protein function. Mutations of GPx4 lead to changes in electrostatic interactions within the protein structure, as well as significant changes in shortest paths communication, leading to the frequent use of Asp21 for site signaling within the protein. These results offer new information on the GPx4 structure, emphasizing the roles of several residues in site-to-site and residues-to-residue communication within the target structure and providing important insights into how the GPx4 enzyme can be modulated.