Basic concepts in systems biology. Techniques for the generation of –omics data. Analysis of –omics data. Integration of –omics data. In silico, predictive models for studying biological systems. Integration of –omics data and computational models. Case-study analysis.
- ALON, Uri. An Introduction to systems biology: design principles of biological circuits. Chapman & Hall/CRC, 2007.
- Introduction to Genomics 3rd Edition, Arthur Lesk. ISBN-13: 978-0198754831. Oxford University Press; 3 edition (May 23, 2017)
Learning Objectives
Knowledge acquired: The course will provides students with knowledge on the theoretical and technical concepts of systems biology.
Competence and skills acquired: students will acquire the competences on the most advanced computational methodologies for the study and the prediction of biological systems’ behavior.
The potential applications of such an approach will be illustrated through the analysis of real-world case-studies.
Prerequisites
Recommended Courses: Genetics; Molecular Biology; Bioinformatics with laboratory
Teaching Methods
The course includes lectures.
Contact hours for lectures: 48
Further information
Frequency of lectures:
The attendance of the lessons is strongly recommended.
Teaching tools: slides of the lessons; scientific publications.
Office hours: to be set according to the student needs.
Type of Assessment
Exam modality: written
Duration: 1h
Type of questions: open and closed questions
Aim: verify knowledge and competences (problem solving)
Course program
Introduction to systems biology and -omics data integration. History and philosophy of systems biology. Bioinformatics. Main –omics techniques and quality check. Massive sequencing analysis: trimming, assembly, scaffolding, gene calling. Post Genomics analysis (Pangenome, Genome Annotation). Phenotype-genotype relationship. Gene regulation analysis (in silico and in vivo). Networks: representation and properties (dynamics robustness stability). Protein-protein interaction networks. DNA-protein interaction. Regulatory networks. Models of gene regulation.Gene exchange networks. Metabolic network reconstruction (databank usage, KEGG, STRING, KBASE). Metabolic network reconciliation. Metabolic modelling techniques. Flux Balance Analysis: methods and applications. –omics integration with metabolic models. Metabolic engineering. Case-study analysis.