Description
The aim of this symposium is to bring together researchers from different fields of computational and experimental biology, to discuss the use of biomolecular interaction networks to study cell function in both physiological and pathological contexts. These interaction maps, also known as interactomes, model protein-protein, protein-DNA and protein-small molecule interaction networks either within an organism or within specific cellular contexts. The function of proteins, nucleic acids, and other biomolecules can only be defined through their interactions in vivo. Such biochemical interactions including those involved in signal transduction, transcriptional and translational regulation, as well as in the assembly of large molecular complexes are astonishing in their magnitude and diversity. For instance, it has been shown that most proteins interact with multiple partners, forming intricate interaction networks. Similarly, individual transcription factor can bind to tens of thousands of genomic sites and regulate the expression of thousands of genes, both in isolation and in combinatorial fashion. Regulatory interactions play a key role in determining cellular differentiation, in maintaining cellular and organism homeostasis, and in triggering abnormal differentiation events leading to human disease including cancer. Not surprisingly, even slight genetic and epigenetic perturbations of these regulatory pathways can trigger macroscopic changes in normal cell physiology and lead to disease. Due to the abundance of experimental data, researchers are starting to uncover some general rules and principles underlying molecular interaction networks: their topological properties, the relationships between their components, evolutionary conservation and divergence, and their role in maintaining specific cellular functions and processes. Despite significant advances, however, knowledge about the distinct functional roles of many proteins is still elusive. Thus, interaction networks have emerged as exceedingly useful tools in predicting context-specific molecular function based on knowledge of upstream regulators, cognate binding partners, and downstream regulated targets. Furthermore, molecular interaction networks are starting to provide a unique integrative context to study additional disease-related genetic and epigenetic data, including single nucleotide mutations and polymorphisms, gene copy number alterations and complex, polygenic diseases.
