Silvio Bicciato

University of Padova

Francesca Cicciarelli

Francis Crick Institute

Ivan G. Costa

RWTH Aachen University

Christine Orengo

University College London

Graziano Pesole

Università di Bari

Paul Thomas

University of Southern California & Swiss Institute of Bioinformatics

Christon Ouzunis

Centre for Research and Technology Hellas

Gábor Erdős

Eötvös Loránd University

Silvio Bicciato is a Professor of Industrial Bioengineering at the University of Padova. Trained as a Chemical Engineer, he earned his PhD in Chemical Engineering from the same institution. In 1996, he was awarded a NATO-CNR postdoctoral fellowship to join the Metabolic Engineering Laboratory led by Prof. Gregory N. Stephanopoulos at the MIT Department of Chemical Engineering (Cambridge, USA), where he deepened his expertise in systems and computational approaches to biological complexity.

Since 1997, his research has focused on the development and application of bioinformatics and computational methods for the analysis of high-throughput genomic data. In 2008, he founded the Bioinformatics Core Unit at the Center for Genome Research of the University of Modena and Reggio Emilia, which he directed until 2023, establishing it as a reference hub for integrative multi-omics analysis.

Over the past decade, his group has developed algorithms, computational frameworks, and analytical tools for the multidimensional integration of genomics, transcriptomics, epigenomics, proteomics, and phenotypic data. By combining multi-omics technologies with advanced computational modeling, his research has contributed to uncovering new layers of molecular connectivity between the genome and its functional outputs, with applications in oncogenomics, immunogenomics, and neuroscience.

His current research focuses on computational approaches to dissect the spatial, architectural, and functional heterogeneity of cellular ecosystems within tissues. In particular, he develops methods to characterize spatial tumor organization, reconstruct cellular neighborhoods, and model the dynamics of tissue architectures, with the goal of advancing mechanistically interpretable and clinically relevant computational oncology.

Lecture title and abstract: Computational analysis of single-cell spatial omics data: challenges and opportunities

The spatial organization of cells within tissues is a fundamental determinant of biological function and disease behavior. While single-cell transcriptomic and proteomic technologies have transformed our understanding of cell identity and state, dissociative approaches disrupt tissue architecture and obscure the higher-order spatial structures that shape multicellular ecosystems. Single-cell spatial omics technologies now enable highly multiplexed molecular profiling at subcellular resolution while preserving native tissue context, creating unprecedented opportunities to investigate cellular organization in situ.

The rapid expansion of these platforms has shifted the analytical bottleneck from data generation to data interpretation. Spatial omics datasets are inherently multi-layered, combining molecular measurements, spatial coordinates, and cell morphological features. Extracting biologically meaningful and reproducible insights from these data requires computational strategies that move beyond cell-level annotation toward principled modeling of tissue architecture.

This presentation will outline the key computational challenges and emerging opportunities in the analysis of imaging-based spatial omics data. These include robust quality control and artifact detection; accurate cell type identification; quantitative characterization of higher-order spatial organization; inference of cell-cell interactions; and the derivation of interpretable tissue-level representations linked to clinical outcomes. Each of these steps presents computational criticalities, yet they are essential for converting spatially resolved molecular measurements into mechanistic understanding of tissue organization and into clinically relevant, robust biomarkers.

Ivan G. Costa is a Professor for Computational Genomics at the RWTH Aachen. After graduating in computer science in 2003 at the Federal University of Pernambuco (Brazil), he joined the Max Planck Institute for Molecular Genetics (Germany) to pursue doctoral studies in bioinformatics. In 2017, he established a research group on computational genomics at the RWTH Aachen Medical Faculty (www.costalab.org). His research interest involves statistical machine learning approaches to dissect transcriptional and regulatory programs controlling cellular changes in cell differentiation and in the onset of diseases. He currently emphasizes computational methods to understand how cellular microenvironment changes cause or support disease processes by integrative analysis of transcriptional, chromatin and spatial status of single cells.

Paul Thomas. Trained in computational biology (specifically computational protein folding using statistical-mechanics based techniques with Dr. Ken Dill), Dr. Thomas turned to genomics as soon as the Human Genome Project began pilot work in 1995. The culmination of this early work was the publication of the paper describing the sequencing of the first human genome in 2001; Dr. Thomas led the work described in the 10-page section of the paper entitled "An overview of the predicted protein coding genes in the human genome." Since that time, Dr. Thomas's group has continued to innovate in the area of computational analysis of genomic data, with an emphasis on gene function and evolution. Recent advances in the work on defining human gene functions and their evolution were published in Nature. In addition to founding and continuing development on the PANTHER phylogenomics project, Dr. Thomas is a director of the Gene Ontology Consortium, one of the largest and best-known bioinformatics projects in the world.

Gábor Erdős is an Assistant Professor and Principal Investigator at the Department of Biochemistry at Eötvös Loránd University (ELTE), Hungary, where he leads the Protein Dynamics Research Group. Trained as a bioengineer, he holds a PhD in structural biochemistry and has a decade of experience in bioinformatics. His career spans ten years of academic research alongside specialized experience in the pharmaceutical industry, with professional activity in Argentina and extensive collaborations across Europe, particularly in Italy and Israel. His research expertise centers on the characterization of intrinsically disordered proteins through the integration of biophysical concepts and advanced computational approaches.

His work focuses on developing novel predictive methods for both general and functional protein disorder, with the goal of bridging artificial intelligence and biophysics. To this end, he employs a broad range of methodologies that includes machine learning, molecular dynamics simulations, and statistical mechanics to investigate the complex behavior and functional roles of intrinsically disordered proteins.

In addition to his research activities, he plays an active role in several major international initiatives. He serves as Co-lead of the ELIXIR IDP Community and as the Hungarian Representative for COST. He is also a core member of the ML4NGP COST action, IDP2Biomed twinning project and a contributor of the IDPFun2 project.