COMPUTATIONAL BIOPHYSICS

(CB_23_24)

SYLLABUS

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

Academic Year 2023-2024, fall semester, 6 ECTS.

This is a course for the Master Program
in Physics given in the 1^st semester.
The course is  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 25^th
of  September 2023. The class meets in
presence, three times per week:  mondays  in the Careri Room (Marconi Building) from 6 to 7 pm; tuesdays in Room 8 (Fermi Building) from 12am to 2pm; thursdays in room 8 (Fermi Building).

Instructor:
prof.Andrea Giansanti, office room n. 211
(2^nd 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 to
discuss, correct and propagate ideas. Ideas are the wings of innovation,
competences implement novelties. Extensive reference and critical introductions
to the literature and to several specialised 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 both young and senior researchers 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%.

 General reference texts  for self-study 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.

[B]
Dennis Bray, “Wetware”, Yale University Press,2009

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

[BB]
P.
Baldi and S. Brunak, Bioinformatics: the
machine learning approach, MIT Press 2001.

CORE THEMES OF THE COURSE

physics/biology, modelling,
computation; from the
Schroedinger equation to molecular dynamics; complexity, elements of networks; systemic modelling of biological systems;probabilistic
reasoning (bayes redux); data science
(PCA, classification: clustering and networks); protein
structures and databases; molecular
dynamics of proteins; models of
sequence evolution; sequence alignment
algoritms; machine learning methods; hidden Markov Models; active inference and the bayesian brain; max ent inference (the Tolomeo platform).