Mediator: Tsvi Tlutsy (Weizmann Institute, Princeton Institute For Advanced Studies)

Dates: 4*3h - Friday May 4th, Wednesday May 9th, Friday May 11th, Monday May 14th

The concept of information is omnipresent in biology: information is stored in DNA, transmitted in a signaling pathway or along a neuron, and translated by the ribosome. In this short course, we will formalize and quantify these intuitive notions of biological information. We will start from the basic definitions of Shannon's information theory, which will be linked to statistical mechanics. We will then apply this framework to examine a variety of living information systems, starting from molecular channels, through neural networks, to population dynamics and evolution. Coherent discussion in terms of information theory reveals common principles of noisy living information systems which we will discussed.

Details of the program :

Part I: Information and Statistical Mechanics
- Shannon's information - fundamental properties (channel, capacity, noisy channels).
- Relation to statistical mechanics: Maxwell's demon (Szilard, Jaynes) similarity and difference between entropy and information.

Part II: Overview: Living Information: molecules, neurons, population and evolution.
- Living systems as information processors.
- Sources of information in Life: (modernized) central dogma; environment; cell composition.
- Living information channels: Receptors - signaling pathways; codes; replication; population dynamics; quorum sensing.
- Information processing: circuits and their elements; neural networks; genetic networks.
- Information output: transcription; decisions; cell fate; differentiation; development; how output feeds back and affects sources (information loop).

Part III: Molecular information
- The biophysical constraints on information processing by molecules.
- Genetic code as a noisy information channel.
- More examples: Operons, genetic networks, transcription factors...

Part IV: Neurons
- How neurons transfer information. Basic physiology.
- Single neuron (Laughlin).
- Neural networks (Hopfield).

Part V: Population dynamics, social interaction and sensing
- The fitness value of information (Kelly's horserace)...