Karine Audouze is professor in Bioinformatics at the University Paris Cité, and she is leader the Inserm SysTox group in the center of Paris. KA is a bioinformatician with a background in computational chemistry. She has six years of experience as a computational chemist in the pharmaceutical industry in Denmark and over a decade of academic experience in systems biology across Denmark and France. Her expertise lies in developing computational systems biology and toxicology network models by integrating diverse data types.
Her work focuses on computational analysis and the creation of innovative models and tools to bridge wet- and dry-lab research. She employs various techniques, including artificial intelligence, natural language processing, statistics, data mining, and multi-omics, to explore potential links between environmental chemicals, adverse health effects, and gene-environment interactions.
She is actively involved in multiple national and EU projects, such as the PARC project (European Partnership for the Assessment of Risks from Chemicals), and previously coordinated the H2020 Oberon project. She also contributes to key initiatives, including the OECD Advisory Group on Emerging Science in Chemicals Assessment. Additionally, she co-leads the ELIXIR Toxicology Community and serves as Vice President of the French Society of Chemoinformatics.
Invited lecture: Deciphering the Chemical Exposome Effects on Human Health Through Adverse Outcome Pathways (AOPs) using artificial intelligence.
Computational methods play a crucial role in improving our understanding of the potential effects of various exposures on human health. While regulatory risk assessment (RA) still heavily relies on animal studies, new approach methodologies (NAMs)—including in vitro systems, non-mammalian alternative models, and computational techniques—are increasingly being adopted for chemical hazard evaluation.
This presentation will introduce AOP-helpFinder (https://aop-helpfinder.u-paris-sciences.fr), an innovative AI-based tool designed for the automated exploration, identification, and extraction of knowledge from scientific literature to support Adverse Outcome Pathway (AOP) development. AOPs provide a key framework within NAMs, mapping causal relationships between biological events triggered by stressors that may lead to adverse health outcomes.
An application of AI, combined with integrative systems biology, will be demonstrated through the development of an AOP for radiation-induced microcephaly. By leveraging AI to decode the effects of chemical exposures, this approach advances our understanding of toxicity mechanisms and strengthens predictive risk assessment.
Pantelis Bagos is Professor of Bioinformatics and Biostatistics at the Department of Computer Science and Biomedical Informatics of the University of Thessaly, Greece (https://dib.uth.gr/?lang=en). He studied Biology, Biostatistics and Bioinformatics at the University of Athens, Greece. He has been teaching several undergraduate and postgraduate classes of Bioinformatics, Biology, Genetics and Biostatistics in University of Athens, University of Peloponnese, University of Thessaly, and Hellenic Open University. His research interests include prediction of protein structure and function from sequence, Hidden Markov Models for biological sequence analysis, biological databases, development of methodology for GWAS and gene expression studies and methodology of meta-analysis. He is founding member and chairman of board of directors of the Hellenic Society for Computational Biology and Bioinformatics (HSCBB). He served as Dean of the School of Sciences. Currently he is a member of the board of directors of the University of Thessaly.
Invited lecture: GWAS summary statistics: Bioinformatics methods for post-GWAS analyses
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. In this talk I will try to review and summarize the various methods, software tools and databases, developed over the last years for the downstream analysis using GWAS summary data, the so-called “post-GWAS” analysis. I will also present key aspects of our work in the field, including methods for meta-analysis of GWAS, robust methods for GWAS, and imputation of GWAS summary statistics.
Vincenza Colonna (University of Tennesse Health and Science Center, US) is an Associate Professor in the Department of Genetics, Genomics and Informatics and Director of the Biorepository and Integrative Genomics in the Department of Pediatrics at UTHSC. She graduated in Evolutionary Biology from the University of Naples Federico II and did postdoctoral research at the University of Ferrara (Italy) and at Wellcome Trust Sanger Institute in Cambridge (UK). She was a lecture in Genetics and Bioinformatics at the University of Ferrara (Italy) and a Researcher at the National Research Council until 2023.
She is a genomicist and an expert in human evolutionary and population genomics and bioinformatics. Her research spans population genomics, pangenomics, and evolutionary genetics with a focus on understanding genetic diversity across diverse human populations and model organisms. She leads projects investigating admixture mapping, human and mouse pangenome development, and the genomics of early embryonic development to advance our understanding of natural selection and its impacts on health disparities and disease.
Invited lecture: Integrating genetic ancestry, environmental exposure and health equity
The Biorepository and Integrative Genomics (BIG) Initiative in Tennessee links genomic, phenotypic, and environmental data of 42k individuals from a diverse Mid-South population, including many from historically underrepresented groups. Analysis of 13,152 BIG genomes showed remarkable diversity, with 50% non-European or admixed ancestry and clear geographic stratification. Among European-African admixed individuals, 78% presented African maternal lineages compared to only 16% European, indicating strong historical sex-biased admixture patterns. We observed discrepancies between self-reported race and genetic ancestry, highlighting the limitations of race as a biomedical variable. Finally, we used identity-by-descent analysis to detect four major communities aligned with continental ancestries and mapped these to neighborhood-level geographical data, with some sub-communities overrepresented in areas with elevated environmental stressors, suggesting that genetic relatedness might serve as an indicator of shared environmental and social factors while capturing ancestral population structure, without using potentially biased reference populations.
Mario Nicodemi is Full Professor of Theoretical Physics at the University of Naples Federico II and Einstein Visiting Professor at the Max Delbrück Center in Berlin. For his scientific discoveries at the frontier between physics and biology he was awarded in 2016 the Einstein BIH Fellowship by the Einstein Foundation, and in 2022 the Occhialini Medal and Prize by the UK Institute of Physics and the Italian Physical Society. He had previously held a professorship at the UK Complexity Science Centre at the University of Warwick. He coordinates the Theoretical Physics group at the Italian Institute of Nuclear Physics (INFN) in Naples and the INFN National Project (IS) on Biological Physics. At Federico II, he leads the University's Task Force on Computational Biology. He is also a member of numerous international scientific consortia and institutions, including the National Institutes of Health (NIH) in the USA, the Berlin Institute of Health in Germany, and the Agence Nationale de la Recherche in France.
Invited lecture: Chromatin 3D architecture: mechanisms and functions
New experimental technologies and principled models from physics are revealing the mechanisms that control structure and function of the human genome, and the impact of large mutations (SVs) on gene regulation, in health and disease.
Romina Oliva (University of Naples "Parthenope") earned her PhD in Chemistry in 2002, at the University of Napoli “Federico II”.
Between 2003 and 2006, she was a post-doc at the University of Roma “La Sapienza”, in the Biocomputing group directed by Prof. Anna Tramontano.
In 2006 she joined the University of Napoli "Parthenope", where she is Associate Professor of Chemistry.
In the summers 2007 and 2009 she was a visiting scientist at the European Institute of Bioinformatics (EBI, Cambridge-UK), in the group directed by Prof. Janet Thornton, and in 2018-2019 she was a sabbatical visitor at the King Abdullah University of Science and Technology (KAUST, Thuwal - Saudi Arabia), hosted by the Computational Bioscience Research Center.
Romina Oliva is a structural bioinformatician. Her specialty is elucidating, on a structural basis, the mechanisms underlying biomolecular functions. She has contributed insights into the structure-function relationship of protein families, such as aquaporins, and into the structural effect of protein mutations associated with defective phenotypes, in collaboration with wet lab groups. Since her post-doc, she also carries on a research line on the structural characterization of RNA molecules, with a special focus on the role of post-transcriptional modifications.
In the last 15 years, she has been especially active in the structural analysis and prediction of biomolecular complexes.
Invited lecture: Tools for the analysis of biomolecular complexes
At the core of many of the most important molecular processes in the cell, including signal transduction, electron transfer, gene expression and immune response, are interactions between biomolecules. Increasing evidence reveals that perturbation of such interactions frequently leads to defective phenotypes. An efficient analysis of the interface in the 3D structure of a biomolecular complex is crucial for understanding associated biological functions and dysfunctions, as well as for formulating testable predictions for interface modification and targeting.
In this talk, I will present COCOMAPS-2.0 and NAPOLI, our novel web servers for visualizing the interface of biomolecular complexes and conducting in-depth analyses of the interactions. These servers are designed specifically for protein/nucleic acid assemblies and for complexes of protein/nucleic acid chains with small ligands. They are built upon our expertise in detecting and energetically characterizing non-covalent interactions in biomolecules, along with our decade-long experience as scorers of biomolecular complexes in the CAPRI (Critical Assessment of PRediction of Interactions) experiment.
Sylvia Richardson CBE (MRC Biostatistics Unit, University of Cambridge & Norwegian Centre for Knowledge-driven Machine Learning, University of Oslo), is Emeritus Professor of Biostatistics at the University of Cambridge and visiting professor at the University of Oslo. She was previously Director of the MRC Biostatistics Unit (2012-2021). She is a fellow of the Academy of Medical Sciences, the IMS, ISBA, and past President of the Royal Statistical Society.
From the mid-1990s, Sylvia has played a central role in the development of modern Biostatistics. Stimulated by her numerous scientific collaborations, her research and publications have focused on advancing statistical methodology to enrich discoveries in biomedicine, genomics, epidemiology and public health. Her recent research has focused on modelling and analysis of large data problems such as those arising in genomics.
Invited lecture: Uncovering latent structure in omics features: a strategy to enhance discoveries in biomedicine
By being creative in exploiting the rich data sets that are currently being collected in genomics, and in particular by elucidating their latent structures, data scientists can enhance discoveries in biomedicine. In this talk, I will illustrate the benefits of such a strategy through the discussion of two commonly encountered challenging analysis tasks: (i) estimating graphical network structures when confronted with large sets of related features and (ii) inferring latent time dynamics driving the time course of sets of biomarkers via functional PCA.
I will outline how each task can be formulated within a Bayesian framework, discuss key modelling ingredients, such as graphical networks and functional PCA, and briefly review computational strategies that can be adopted for making inference on such models scalable. These approaches will be illustrated on case studies.
This is joint work with Dr Helene Ruffieux (MRC Biostatistics Unit) and Dr Camilla Lingjaerde (Norwegian Centre for Knowledge-driven Machine Learning).
