July 10th- 21st, 2017 | IFOM, Milan - Italy
Cells receive messages from the external environment and react accordingly. They can 'decide' to produce the components required for using certain substances, or to shut off completely the synthesis of molecules that they do not need. In particular, cells modulate the production of enzymes and other classes of proteins by controlling the expression of the relative genes. The physiological state of a cell is largely defined by the set of genes that are switched on and off at a certain time. The activation state of genes is a function of external stimuli, but also of the activation state of other genes. In other words, cells’ behavior is determined by complex network whose nodes are genes that respond to external and internal cues.
When we talk generically about 'cells', however, we are making a big assumption. A cell population is formed by thousands of cells (organisms even more). The behavior of each individual cell is quite hard to follow, while it is much easier to study the average behavior. Indeed, a large part of the experiments performed in the laboratories are done in the assumption that the average behavior of a cell population also represents the behavior of its individual components. The average assumption is many times correct, but it can also be very wrong. Typically, phenomena like sustained oscillations of gene expression, bistable systems, and so on can easily go unnoticed when one looks at the average behavior of a cell population.
Recent technical advances have allowed monitoring gene expression in individual cells in real time. This opened the way to the study of the dynamical behavior of gene networks within cells. In this module, we propose to analyze some basic properties of gene networks by combining mathematical models and live cell imaging. In particular, we will analyze how gene expression differs in different cells of a same population.
