Syllabus Information

1. General information

1.1. Aims of the course

 This course explains in detail the main theoretical and computational tools required to model biological systems. From this point of view it enlarges and deepens the description that the students received from 63100-Systems Biology and 63101-Simulation of Biomolecules. It has been designed in order:

• To present the main theoretical/computational tools required to explain the experimental data obtained during the study of biological systems.

• To present the dynamical and thermodynamical description of biological systems, focusing on the the most relevant theories and equations.

• To introduce the most frequent models for the different scales and processes in the description of biological systems and the computational tools that allow to relate them with the experimental data.

• To provide the student with an educated criterion to choose the most suitable method to be used in each particular situation, and to be able to evaluate and criticize the results from an experiment or obtained from the Literature.

1.2. Context and importance of this course in the degree

This course covers, with the course in Biostatistics and Bioinformatics, the most theoretical aspects of the program and it is designed to provide those students with interests in the theoretical and computational aspects of Biotechnology with the background required to begin a research career. 

1.3. Recommendations to take this course

 Students should have a basic mathematical background in algebra and differential equations, and must have followed the three subjects of the first semester.

2. Learning goals

2.1. Competences

Basic and General

01 - To order, analyze critically, interpret and synthesize information

02 - To obtain information from different types of sources and evaluate their reliability.

03 - To acquire a significant degree of independence.

04 - To formulate, analyze, evaluate and compare new or alternative solutions to different problems.

05 - To communicate results in a clear and unambiguous way, using suitable presentation tools and with the limitations imposed by time or space.

Specific

1. To learn the basic aspects of the theory of statistical mechanics used in the mathematical description of biological systems.

2.- To learn the basic aspects of the theory of stochastic dynamical systems used in the mathematical description of biological systems.

3. To learn to build a theoretical model adapted to the description of experimental data.

4. To learn to use the most relevant simulation techniques and the main approximation methods.

2.2. Learning goals

At a general level, the main  goal of the course is to provide the student with the ability to analyze, critically, the use of computational methods in the description of a biological system. At the end of the course, he/she will be able to design, independently or as part of a team, a simulation of a biological system with the most adequate tools for the application.

2.3. Importance of learning goals

Mastering the different simulation techniques is a basic skill which is essential for a future career as a researcher within this field. 

3. Assessment (1st and 2nd call)

3.1. Assessment tasks (description of tasks, marking system and assessment criteria)

1: (45% of the final grade). Continuous evaluation of the student's progress by direct interaction in the classroom, rewarding active participation during the lectures, and solution of the homework proposed by the teacher during the practice sessions.

2: (10% of the final grade). Seminars on papers related to the topics studied

3: (45% of the final grade) Written exam.

4. Methodology, learning tasks, syllabus and resources

4.1. Methodological overview

The learning process designed for this course is based on a combination of lectures, exercises and practice sessions in the computer laboratory. The virtual platform Moodle will be used to distribute lecture notes, as well as to propose exercises and tests. Students are encouraged to present a short seminar so as to train their organization and presentation skills. Students are expected to participate actively throughout the semester.  We will promote the debate and the active participation of the students throughout all the activities. 

4.2. Learning tasks

The course includes the following learning tasks:

- Lectures. The lecturer provides theorems and examples, organized according to the syllabus of the course.

- Practice sessions. Students can apply and consolidate the theoretical understanding by means of relevant examples and problems.

- Computer programming of problems. They extend the scope of the classroom exercises to the cases where computations become too heavy.

- Assignments for the students interested in deepening their understanding in specific topics.

Course material: Notes written by the teachers will be available on the course's Moodle page.

4.3. Syllabus

The course will address the following topics:

1.  Statistical mechanics models I: canonical and microcanonical ensembles.

2.  Statistical mechanics models II: Cooperativity  and the Helix-Coil transition.

3.  Statistical mechanics models III: Models of biopolymers.

4.  Statistical mechanics models IV:  Coarse graining and force fields.

5.  Stochastic models I: Brownian motion and diffusion.

6.  Stochastic models II: Langevin and Fokker-Planck equations.

7. Stochastic models III: Chemical master equation and Gillespie algorithm.

8. Stochastic models IV: Kramers theory in chemical kinetics.

9. Simulation techniques: Monte Carlo methods and applications.

4.4. Course planning and calendar

The course takes place during the second semester of the academic year. Lectures will be held on Tuesdays and Practice Sessions on Thursdays, from 15:30 to 17:30.

Homework and other assessment tests will be proposed to the students throughout the semester.

The course is taught throughout the second semester (February-June)

Examinations: one exam at the end of the semester

4.5. Bibliography and recommended resources

Teachers will provide some original research papers to work with and original course notes. Other useful references may be, among others:

• Mathematical Modeling in Systems Biology: An Introduction
  Brian Ingalls, MIT Press 2013
• Notes on Mathematical Systems Biology, E. Sonntag, Rutgers University (http://sites.math.rutgers.edu/~sontag/FTPDIR/systems_biology_notes.pdf)
• Differential Equations and Mathematical Biology, 2nd Edition, CRC Press, 2009