COMPUTATIONAL BIOPHYSICS

PROSPECTIVE  PROGRAM

 (CB_22_23)

doing physical  biology with models and computers: from the
Born-Oppenheimer approximation

to molecular dynamics; from the space of
biological sequences to integrative modelling of systems biology; the role of
machine learning and bayesian inference

 

Academic Year 2022-2023, fall semester,
6 ECTS.

 

The 20022-2023 Computational Physics
Course is dedicated to the memory of Jacques Monod and Giuseppe Briganti

 

This is a course
for the Master Program in Physics given in the 1st semester as a part of an integrated set of courses in
biosystems,  comprehending: BIOCHEMISTRY, MOLECULAR
BIOLOGY, BIOPHYSICS, THEORETICAL BIOPHYSICS, SOFT AND BIOLOGICAL MATTER.

The
course starts on the 26th of 
September 2022. The class meets in presence, three times per week in the
Rasetti Room (2nd floor,Marconi
Building): i) mondays, from 8 am to 9 am ; ii) tuesdays, from 2 pm to 4 pm ; iii)
Fridays, from 8 am to 10 am.

Instructor:
prof.Andrea Giansanti, office room n. 211
(2nd floor, Marconi Building) tel. 0649914367 (cell. 3385075611) andrea.giansanti@uniroma1.it

Description/Objectives. The
course providesa compact
introduction to modern computational (in
silico, as opposed to in vivo/in vitro)
biophysics/biology, in an evolutionary perspective. Expected audience: physics students
enrolled in the biosystems and theoretical curricula. Students from other
curricula: chemistry, mathematics, engineering. The course requires from the
students an active participation, through questions, statements, written essays
and collaborative projects. The style of teaching will be mainly by
illustration and only partly by exhaustive demonstration. The main pedagogical
intention is, besides competences, to discuss, correct and propagate ideas.
Ideas are the wings of innovation, competences implement novelties (…elementay,
Watson). Extensive reference and critical introductions to the literature and
to many specialized texts will be offered as a thread for personal study. An
effort will be made to locate each discussed topic in a clear framework of references,
useful to prepare the final exam. In a nutshell, the objective of this course
is to narrow the gap between the institutional level of training and that of research.
Guest invited lectures by young researchers and reknown experts will be offered
alongside.

Requirements. Enrolled
students should have taken the basic courses of a BA program in physics,
mathematics and engineering. In particular, basic competence in classical mechanics,
thermodynamics, chemical equilibrium and quantum mechanics is required together
with basic programming skills (possibly using Python). Biological facts will be
discussed as needed along the course.

Evaluation: based
onwritten essays, written tests,
home-works and participation to projects and  discussions: 40%. Final oral exam: 60%.

 

Recommended
reference texts and textbooks of  impact.

[MON]
Jacques
Monod, Chance and Necessity: An Essay on the Natural
Philosophy of Modern Biology, New York, Alfred A. Knopf, 1971.

[CH] N Cristianini and MW
Hahn, Introduction to Computational
genomics, a case studies approach,Cambridge University Press (CUP), 2006.

[F] D Forsdyke, Evolutionary bioinformatics, Springer
2016.

[HA]
PG
Higgs and TK Attwood, Bioinformatics and
Molecular Evolution, Blackwell, 2006.

[DU] R Durbin, Eddy,
Krogh, Michison. Biological Sequence Analysis. CUP, 1999.

[V] E Voit, A First Course in Systems Biology,
Garland Science, 2012.

[PPF] T Parr, G Pezzulo
and LJ Friston, Active Inference: the
free energy principle in mind, brain and behaviour, MIT press, 2022.



THEMES

  1. PHYSICS, BIOLOGY, MODELLING, COMPUTATION: SETTING THE STAGE
  2. APPROXIMATIONS: FROM THE SCHROEDINGER EQUATION TO MOLECULAR DYNAMICS
  3. COMPLEXITY: ELEMENTS OF NETWORKS
  4. INTRODUCTION TO THE SYSTEMIC MODELLING OF BIOLOGICAL SYSTEMS
  5. PROBABILISTIC REASONING (BAYES REDUX)
  6. INTRODUCTION TO DATA SCIENCE (CLASSIFICATION: CLUSTERING AND NETWORKS)
  7. ELEMENTS OF PROTEIN STRUCTURES AND DATABASES
  8. INTRODUCTION TO MOLECULAR DYNAMICS OF PROTEINS
  9. MODELS OF SEQUENCE EVOLUTION
  10. SEQUENCE ALIGNMENT ALGORITMS
  11. MACHINE LEARNING METHODS
  12. ACTIVE INFERENCE AND THE BAYESIAN BRAIN