Degree | Type | Year | Semester |
---|---|---|---|
2500890 Genetics | OB | 3 | 2 |
Fundamental pre-grade knowledge in Algebra, Differential Calculus, Chemistry and Biochemistry.
Good comprehension of English written scientific publications and textbooks
Basic computer user skills (Windows, Word, Excel,...).
Be enrolled or have passed the Systems Biology module included in Integrated Laboratory VI.
Systems biology is a rapidly evolving field which fosters a new approach to solve biological problems through a combination of experimental data and the use of computer models with both predictive and explanatory power. The systems biology approach is centered in the integrated study of the network components (genes, enzymes, metabolites,…) and their interactions, revealing key emerging properties and complex dynamic behavior.
Historically, although it might be argued that the concept is much older, the systems biology approach evolves as a response to the enormous data accumulation from genomics, proteomics, transcriptomics, metabolomics,... and also due to the exponential increase in computer power allowing to go further in the analysis, interpretation and deeper understanding of the 'omics' data.
The first objective of the course will be to review the motives and origins of the discipline while offering a perspective of its relevance in the near future.
The second objective is to introduce the student to the tools and methods most commonly used. Thus the course will evolve from the mathematical description of the system, through the alternative methods of solution, towards the analysis of the resulting behavior. As a result the student will know and be able to use the most frequent basic tools used nowadays in the field.
The third objective will be to apply the acquired knowledge to model systems of the three most studied subsystems, namely metabolic, genetic and signal transduction networks. The emerging dynamics of those systems allows to see the main traits that arise in complex systems and understand the necessity of the ‘systems’ approach. An important part of this objective is performed as practical computer simulation sessions included in the Integrated Laboratory VI.
The fourth objective includes a firsthand appreciation of how this new approach is being applied in present day research. To this purpose the students will review real examples from scientific literature. Part of this objective will be fulfilled as a team work including the presentation of a reviewed paper to the rest of the students. This activity will favor a deeper understanding of the concepts learned, foster a wider view of its real impact as well as promoting the development of the student communication skills.
The subject is presented gradually, advancing from the basic concepts towards the description of more complex systems allowing for a thorough understanding of the necessity to study systems as integrated units.
The general objective is to allow the student to acquire the systems perspective of today’s biology.
Unless the requirements enforced by the health authorities demand a prioritization or reduction of contents these wil include:
1.- Introduction and definitions
1.1 The ‘systems’ perspective
1.2 Key general concepts. Emergence and robustness.
2.- Systems description and study
2.1 Top-down vs bottom-up approximations
2.2 Timescales
2.2 Deterministic vs. stochastic approaches
2.3 Dynamics vs steady state
2.4 Review of fundamental mathematical concepts
2.5 Introduction to system dynamics
2.6 Parameter determination
2.7 Structure, kinetics and thermodynamics
3. Networks and biological systems
3.1 Networks and genetic circuits
3.2 Metabolic networks in steady state
3.3 Metabolic control analysis
3.4 Signal transduction networks
The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Along the learning process, the teaching methodology will be fundamentally based on the student’s work and the professor will guide the student either in the process of acquisition and interpretation of the most relevant information as well as in the student’s personal work. The student will collect as much learning evidences as possible in the student portfolio as detailed in the evaluation paragraphs. In this context, and according to the learning objectives of the course, the type of learning activities will include theory classes, exercise and problem solving classes, practical computer exercises and tutor sessions.
Theory classes: Were the main basic conceptual topics and the most relevant information will be provided so that the student can develop its autonomous learning. Computer slides (ppt or pdf format) will be available to the student in the virtual campus.
Seminar and problem solving sessions: These sessions will be done in a reduced subgroup of students of the class. Exercises, previously provided, will be explained and/or solved so that they contribute to learn and clarify the knowledge provided along the course. In those sessions, the students will also explain their peers the solution proposed and the pathway and difficulties encountered while solving them, so that the experience is shared among all of them. The exercises will be delivered through the virtual platform before the exercises are solved in class.
Team work: The students will develop a short essay based on a scientific publication of the field and will present it to their peers in class. This activity provides the opportunity to personally contribute for example by doing some directly related genetic, metabolic or signal transduction computer simulations. This information can be included in the tests to assess the student’s learning progress.
Practical computer sessions: Part of the learning outcomes will be acquired through practical computer sessions. Those sessions are formallyincluded in the Molecular Systems Biology module within Integrated Laboratory VI and therefore evaluated separately. Nevertheless they are necessary to achieve the learning outcomes of this topic. Those practical sessions will be done using ad-hoc software. Those exercises will allow the student to become familiar with the models and type of data most common in the field and their use. Those exercises will be done using existing free software.
Tutoring: It will be possible to perform a few tutor sessions, individually or in a group of students, if it is requested by the students. The main objective will to solve doubts, review basic concepts or guide in the process of selecting additional sources of information.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Seminars and problem solving sessions | 15 | 0.6 | |
Theoretical lessons | 30 | 1.2 | |
Type: Supervised | |||
Support sessions to individual or team work | 2 | 0.08 | |
Type: Autonomous | |||
Solve exercises individually with or without a computer | 35 | 1.4 | |
Study, review references, , ... | 45 | 1.8 | |
Team work | 19 | 0.76 |
Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Progres test | 48% | 4 | 0.16 | 2, 1, 5, 6, 3 |
Solving exercises | 26% | 0 | 0 | 2, 1, 5, 7, 6, 9 |
Team work | 26% | 0 | 0 | 2, 1, 4, 5, 8, 9, 3, 10 |
Primary references
Alon, U. An Introduction to Systems Biology. Design principles of biological circuits. Second edition. Chapman & Hall/CRC. 2019.
Klipp, E., R. Herwig, A. Kowald, C. Wierling, i H. Lehrach. Systems Biology in Practice. Concepts implementation and application. Weinheim: Wiley-VCH, 2005.
Klipp, E., W. Liebermeister, C. Wierling, A. Kowald; Systems Biology. A textbook 2nd. Weinheim: Wiley-VCH, 2016.
Klipp, E., W. Liebermeister, C. Wierling, A. Kowald, H. Lehrach, Herwig R. Systems Biology. A textbook. Weinheim: Wiley-VCH, 2009.
Voit E. A First Course in Systems Biology. 2nd edition. Garland Science. 2017
Complementary references
Helms, V. Principles of Computational Cell Biology. From protein complexes to cellular networks. Weinheim: Wiley-VCH, 2008.
Ingalls B.P. Mathematical Modeling in Systems Biology: An Introduction. MIT Press. 2013
Konopka, A.K. Systems Biology. Principles, methods and concepts. Boca raton: CRC Press, 2007.
Kriete, A., i R. Eils, . Computational Systems Biology. Burlington: Elsevier Academic Press, 2006.
Kriete, A., i R. Eils, . Computational Systems Biology. 2nd Edition. Elsevier Academic Press, 2014.
Nielsen J., Hohmann S., Lee S. Y. Systems Biology (Advanced Biotechnology) 1st Edition.Wiley-Blackwell , 2017.
Palsson, B.O. Systems Biology. Properties of reconstructed networks. Cambridge: Cambridge University Press, 2006.
Palsson, B.O. Systems Biology. Simulation of dynamic network states. Cambridge: Cambridge University Press, 2011.
Stephanopoulos G.N. Aristidou A.A. Nielsen J. Metabolic Engineering. Principles and Methodologies. Academic Press. San Diego. USA, 1998
Szallasi, Z., V. Periwal, i J. Stelling, . System Modeling in Cellular Biology: From Concepts to Nuts and Bolts. The MIT Press, 2006.