The class will provide the students with an overview of different topics, aiming at understanding the basic principles of Systems Biology applied to bio- medical science. The class will be initially focused on general aspects of Systems Biology, including the concepts of complexity, emergent properties, abstraction, mathematical modeling and biological networks. The course will provide a number of examples regarding general principles and methods typical of systems biology, data-bases and software widely used in the field. Furthermore, in the course we will introduce as propaedeutic background, some examples of complex systems in biology, including signal transduction and metabolic networks. This initial part will be critical to allow students to acquire critical skills and knowledge in the field. In the second part of the course we will proceed with the description of complex systems relevant to medicine, such as the immune systems, autoimmune diseases and cancer in the context of systems biology. Several examples will be explained and extensively illustrated. Moreover, a general view of systems biology in the context of a transition toward personalized medicine will be proposed. In the context of the degree, the class will provide the necessary bases to understand the interdisciplinary nature of biomedicine and bioinformatics, which will be propaedeutic to a future profession in the field. The class also will include 12 hours of bioinformatics laboratory to learn how to analyze "comics" data in the context of human pathologies, critical to acquire some practical skills, already usually required in basic science laboratories.
General concepts - foundations:
1) Complexity: definition, origin and nature of complexity in biology
2) The “emergent properties” of biological systems: the cellular and molecular circuits
3) Science based on thesis and the deductive method; science based on experimental data the inductive method
4) Systems Biology: definition and experimental connotation of Systems Biology
5) Why Systems Biology? The reductionist approach versus the holistic approach
6) The concept of model: predict the future in biology?
7) Static models: the network abstraction and the topological properties of biological networks
8) Dynamic models and biological kinetics
Methods in Systems Biology:
9) High-performance technologies (high throughput methods)
11) Biological database
12) Software for systems biology
13) Contexts of Systems Biology: transcriptomics, proteomics, metabolomics, etc.
Systems Biology in practice - applications of Systems Biology to biomedical contexts:
14) Networks and diseases
15) The immune system
16) Inflammatory mechanisms
18) Neurodegenerative diseases
19) Autoimmune diseases
20) Systems pharmacology and drug discovery
Teaching methods consist of frontal lessons devoted to the transmission of basic and applied notions, as well as in computer exercises with tasks that will be assigned to students in order to apply the notions learned during lessons. The exercises will be performed at the Center for Computational Biomedicine (CBMC) of the University.
|Masao Nagasaki • Ayumu Saito • Atsushi Doi Hiroshi Matsuno • Satoru Miyano||Foundations of Systems Biology||Springer||2007||978-1-84882-022-7|
Written with multiple answer questions. Score will be in thirty, 18 to 30. The exam will test theoretic land applied skills useful to the study of physio-pathplogical processes, also in the context of specific examples of human pathologies.