Systems biology is the study of interrelationships between molecular constituents of a living cell through the integration of experimental and computational approaches. Most biological characteristics arise from complex interactions between the cell's functional elements; to understand how genes determine phe¬notypes, both in physiology and disease, it is necessary to adopt quantitative methods that allows a holistic approach. The course describes the principles useful to explore interactions by introducing methods for the analysis of gene-regulatory and large-scale networks exploiting availability of big functional genomics data sets. Analysis and modelling of genetic and epigenetic determinants of gene expression regulation will be considered.
Risultati apprendimento attesi
At the end of the course, students will be able to apply systems biology techniques and pipelines to the analysis and characterization of regulatory (genetic and epigenetic) and interaction networks.
Gene regulation and transcriptional networks. Signalling networks. Protein interactomes. Biological networks and basic concepts of graphs. Scale-free nature of cellular networks. Biological small worlds. Dependencies estimation in co-expression networks. Weighted and unweighted networks. Adjacency matrix. Module detection. Network motifs. Negative and positive autoregulation. The feed-forward loop. Single input modules (SIMs) and dense overlapping regulons (DORs). Positive and negative feedback loops. Oscillators. Modelling of circadian rhythms and cellular networks. Network rewiring and comparative genomics to study gene essentiality. Identification of DNA sequence alterations: statistical genetics for linkage analysis and genome wide association studies (GWASs). Linkage disequilibrium. Expression quantitative trait loci (eQTLs). How chromatin organization acts on gene expression cell program to modulate phenotype and cell fate specification. Hi-C interaction maps. Identification of compartments, topologically associating domains (TADs), points of interaction between distant chromatin regions. Examples of multi-omics data analysis and integration.
The course Bioinformatics and Computational Biology must be successfully completed to take the exam of this class.
The course consists of 36 hours of lectures and 24 hours of practical lessons in Matlab environment.
Materiale di riferimento
The slides presented during the course along with additional material recommended for specific topics will be available on the "Be e-Poli" (BeeP) portal (https://beep.metid.polimi.it/welcome), accessible to students registered to the course.
Modalità di verifica dell’apprendimento e criteri di valutazione
The assessment is based on the development of an assignment that will be discussed during the final exam (oral) along with general questions regarding the topics covered during the course.