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 1st 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 25th 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 (2nd floor, Marconi Building) tel. 0649914367 (cell. 3385075611) andrea.giansanti@uniroma1.it

Description/Objectives. The course provides a 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 on written 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).