Functional Genomics, Multi-Omics & Systems Biology
Session Overview
The deluge of data from modern genomic technologies has ushered in a new era of biological inquiry focused on synthesis and mechanism. This session moves beyond cataloging molecular parts to explore the integrative frameworks that explain how these parts function together as dynamic systems. We will examine the convergence of multi-omic data integration, computational modeling, and advanced perturbation strategies that are transforming vast datasets into actionable understanding of gene regulation, cellular networks, and disease pathophysiology.
Why This Session Matters Now
As high-throughput technologies become routine, the central challenge in genomics has shifted from data generation to interpretation and causal validation. Isolated omics layers provide limited insight; their true power is unlocked through integration and systems-level analysis. This session addresses the critical need for advanced analytical and conceptual tools that can bridge the gap between correlation and causation, transforming associative findings into mechanistic models that can drive therapeutic discovery and precision medicine.
Key Scientific and Clinical Themes
Multi-omics integration and data harmonization
Exploring computational and statistical frameworks for merging diverse data types (genomic, transcriptomic, epigenomic, proteomic) to create a unified view of cellular states and biological processes.
Systems biology network modeling and pathway analysis
Discussing methods to reconstruct gene regulatory networks, signaling pathways, and metabolic circuits from large-scale data, revealing the organizational principles of cellular systems in health and disease.
Functional annotation of non-coding and regulatory elements
Focusing on strategies to assign biological meaning to the vast non-coding genome, linking genetic variants in regulatory regions to their target genes and phenotypic outcomes.
Causal inference and perturbation-based validation
Examining experimental-computational approaches, including CRISPR-based screens and other perturbations, to move from observational associations to established causal mechanisms in complex biological systems.
Single-cell and spatial systems approaches
Applying systems biology principles to single-cell and spatially resolved data to deconvolve cellular ecosystems, understand cell-type-specific networks, and map communication within tissue contexts.
Machine learning for integrative genomics
Leveraging advanced computational techniques to discover patterns, predict function, and generate hypotheses from complex, high-dimensional genomic datasets.
Nature of Research in This Field
Research in functional genomics and systems biology is inherently interdisciplinary and highly collaborative, requiring close synergy between experimental biologists, computational scientists, statisticians, and clinicians. The field is characterized by an iterative cycle of hypothesis generation from large datasets, computational model building, and rigorous experimental validation. This work prioritizes the development of robust, reusable analytical pipelines, open data standards, and reproducible frameworks to ensure that systems-level insights are both biologically grounded and broadly applicable.
Who Should Attend
This session is designed for:
- Computational biologists, bioinformaticians, and data scientists developing integrative models.
- Basic and translational researchers using multi-omic data to understand disease mechanisms.
- Clinical investigators seeking to interpret complex genomic findings in patient cohorts.
- Statisticians and machine learning specialists working with biological data.
- Core facility leads and analysts supporting systems-level projects.
- Trainees building expertise in the analysis and interpretation of next-generation genomic datasets.
Session Perspective
“Functional Genomics, Multi-Omics & Systems Biology” represents the essential next step in the genomic revolution: turning data into understanding. This session emphasizes that the complexity of biology demands not just more data, but better models and sharper causal reasoning. By fostering dialogue on integrative approaches, this session explores the pathways toward a more holistic, predictive, and mechanistic science of genomics—one that seeks to explain how genetic information flows through dynamic systems to influence phenotype, resilience, and disease.
If your research aligns with this session, we invite you to submit an abstract for consideration.
