Course Unit Page

Academic Year 2018/2019

Learning outcomes

At the end of the course the student will acquire a general picture of modern Biology and related problems. He/she will be able to determine the various levels of complexity of biological systems, as well as use physical and quantitative tools aimed at tackling the “systemic” aspects of Biology (Systems Biology), with emphasis on Immune and Neural Systems (Learning and Memory). The students will learn some experimental techniques for: - the study of biological systems, such as electrophysiology, cellular and subcellular imaging; - large scale analysis of gene expression data (Big Biological Data) aimed to build multiscale predictive models of biological functions.

Course contents

Basic of Biological Systems

Cell Chemistry, Biomolecules and introductory biochemistry.

Organelles, Cells, Tissues, Organs and Systems.

Systems Biology and Systems Medicine

Deterministic modeling of Biological Systems

Law of Mass Action and Chemical Kinetics

Enzymatic kinetics and Receptor-ligand interactions

Cooperativity and nonlinearity

Introduction to "omic" techniques and relation with Big Biological Data

Ecological modeling

Stochastic Modeling: the Chemical Master Equation (CME)

Numerical methods for solving the CME

The stationary distribution and relation with other probability distribution

Thermodinamic connections (Detailed Balance)

Electrical properties of cells and tissues

Nernst-Planck equation and electrochemical equilibrium


The nervous system

The ion channels and the patch clamp techniques

Hodgkin and Huxley model

neural networks and learning rules






Lecture notes
Cellular Biophysics Weiss MIT Press

Teaching methods

Slides and blackboard

Assessment methods


Oral exam

Teaching tools

Practical work on PC and visit to the lab

Office hours

See the website of Gastone Castellani