Biophysics

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
UB
With this course the student acquire practical and theoretical knowledge about Molecular Biophysics and its relevance within Bioinformatics. The course includes:
-Concepts of Thermodynamics and Kinetics. Statistical thermodynamics
-Macromolecules: Energetics, Folding, Conformational dynamics
-Macromolecular processes: Binding energetics and structure of complexes, Enzymes and catalysis, Molecular Transport.

Teachers

Person in charge

Others

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Competences

Learning Outcomes

Knowledge

  • K1 - Recognize the basic principles of biology, from cellular to organism scale, and how these are related to current knowledge in the fields of bioinformatics, data analysis, and machine learning; thus achieving an interdisciplinary vision with special emphasis on biomedical applications.
  • K6 - Recognize the ethical problems that arise from advances in the knowledge and in the application of biological concepts and their computational processing.

    • Skills

  • S6 - Identify and interpret relevant data, within the area of study, to make judgments that include social, scientific or ethical reflections.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.
  • S9 - Exploit biological and biomedical information to transform it into knowledge; in particular, extract and analyze information from databases to solve new biological and biomedical problems.

    • Competences

  • C6 - Detect deficiencies in the own knowledge and overcome them through critical reflection and the choice of the best action to expand this knowledge.
  • C7 - Detect, from within the scope of the degree, inequalities based on sex and gender in society; integrate the different needs and preferences based on sex and gender in the design of solutions and problem solving.

    • Objectives

    1. To acquire basic knowledge from the scope and tools of molecular biophysics, and how bioinformatics can help to its development
      Related competences: K1, S6,
    2. To apply mathematical foundations, algorithmic principles and computational theories in the modeling and design of biophysics experiments
      Related competences: K6, S8, S9,
    3. To identify meaningful and reliable sources of scientific information to substantiate the state of arts of a biophysics problem and to address its resolution.
      Related competences: K6, S6, S8, S9, C7, C6,

    Contents

    1. Part 0. Introduction. Molecular biophysics from bioinformatics perspective
      Definition of molecular biophysics. Interaction with other subjects. Reference data. Experimental data and associated problem. Calculable magnitudes and limitations. Model Systems. Limitations and approximations. Validation and experimental design
    2. Part 1. Advanced concepts of thermodynamics and kinetics
      Thermodynamics and Statistical thermodynamics. Chemical kinetics: Transition State theory. Activation Energies, rate equations. Relaxation processes. Diffusion.
    3. Part 2: Macromolecules. Energetics and dynamics
      Macromolecular energetics: Stability. Energy components. Enthalpic and Entropic terms. Solvation. Methods for Energy evaluation. Folding of Macromolecules: Energy landscape, Folding models, Intrinsical disordered proteins. Dynamics of Macromolecules: Concept of conformational ensemble. Generation of ensembles. Biomolecular simulation
    4. Part 3: Biomolecular processes
      Macromolecular recognition and binding: Structure of complexes. Energetics of binding. Thermodynamic cycles. Alchemical cycles. Catalysis: Strategies of catalysis. Enzyme kinetics and mechanism. Energy coupling. Evaluation of kinetic constants. Transport: Biological Membranes, Transport models. Electrophysiology. Energy coupling.

    Activities

    Activity Evaluation act


    Final Exam

    Final exam including all contents
    Objectives: 1 2 3
    Week: 1 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Mid Term Exam


    Objectives: 1 2 3
    Week: 9
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Theoretical presentations

    (4h) Part 0. Introduction. Molecular biophysics from bioinformatics perspective Definition of molecular biophysics. Interaction with other subjects. Reference data. Experimental data and associated problem. Calculable magnitudes and limitations. Model Systems. Limitations and approximations. Validation and experimental design (6h) Part 1. Advanced concepts of thermodynamics and kinetics 1.1. Thermodynamics and Statistical thermodynamics. 1.2. Chemical kinetics: Transition State theory. Activation Energies, rate equations. Relaxation processes. Diffusion. (8h) Part 2: Macromolecules. Energetics and dynamics 2.1. Macromolecular energetics: Stability. Energy components. Enthalpic and Entropic terms. Solvation. Methods for Energy evaluation. 2.2. Folding of Macromolecules: Energy landscape, Folding models, IDPs. 2.3. Dynamics of Macromolecules: Concept of conformational ensemble. Generation of ensembles. Simulation tools. (8h) Part 3: Biomolecular processes 3.1. Macromolecular recognition and binding: Structure of complexes. Energetics of binding. Thermodynamic cycles. Alchemical cycles. 3.2. Catalysis: Strategies of catalysis. Enzyme kinetics and mechanism. Energy coupling. Evaluation of kinetic constants. 3.3. Transport: Biological Membranes, Transport models. Electrophysiology. Energy coupling.

    Theory
    27h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    20h

    Guided Problem Solving



    Theory
    0h
    Problems
    10h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    30h

    Guided scripting programming



    Theory
    0h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    10h

    Programming project in Biophysics



    Theory
    0h
    Problems
    8h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    20h

    Seminars



    Theory
    0h
    Problems
    6h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    10h

    Teaching methodology

    - Theoretical lectures will be expository with the aid of graphics materials (slides, videos, computer demonstrations)
    - Problem solving session will detail the methodology of solving selected problems. Will include expository and hands-on sessions
    - Guided programming sessions will done in groups work in a "Hackathon" style to solve steps of the development of the desired goal. Programming language will be Python with the aid of the appropriate libraries as Biopython.

    Evaluation methodology

    For the evaluation of the subject, the grade of the mid term (MTE) and final (FE) and the grade of the practical sessions and programming project (Pract)
    will be taken into account according ot he following formula:

    Grade = MTE * 0.2 + FE * 0.6 + Pract * 0.2

    A grade equal or superior to 5 is required to pass.
    Students that have failed witha grade equal or superior to 3 may take the re-evaluation exam (RT), In this case the grade for the subject will be 0.2 * Pract + RT * 0.8.

    Bibliography

    Basic