From statistical mechanics to information theory: understanding biophysical information-processing systems

G. Tkacik, ArXiv q-MN (2010) 1–52.

Preprint | Published
Abstract
These are notes for a set of 7 two-hour lectures given at the 2010 Summer School on Quantitative Evolutionary and Comparative Genomics at OIST, Okinawa, Japan. The emphasis is on understanding how biological systems process information. We take a physicist's approach of looking for simple phenomenological descriptions that can address the questions of biological function without necessarily modeling all (mostly unknown) microscopic details; the example that is developed throughout the notes is transcriptional regulation in genetic regulatory networks. We present tools from information theory and statistical physics that can be used to analyze noisy nonlinear biological networks, and build generative and predictive models of regulatory processes.
Publishing Year
Date Published
2010-06-22
Journal Title
ArXiv
Volume
q-bio.MN
Page
1 - 52
IST-REx-ID

Cite this

Tkacik G. From statistical mechanics to information theory: understanding biophysical information-processing systems. ArXiv. 2010;q-MN:1-52.
Tkacik, G. (2010). From statistical mechanics to information theory: understanding biophysical information-processing systems. ArXiv. ArXiv.
Tkacik, Gasper. “From Statistical Mechanics to Information Theory: Understanding Biophysical Information-Processing Systems.” ArXiv. ArXiv, 2010.
G. Tkacik, “From statistical mechanics to information theory: understanding biophysical information-processing systems,” ArXiv, vol. q-MN. ArXiv, pp. 1–52, 2010.
Tkacik G. 2010. From statistical mechanics to information theory: understanding biophysical information-processing systems. ArXiv. q-MN, 1–52.
Tkacik, Gasper. “From Statistical Mechanics to Information Theory: Understanding Biophysical Information-Processing Systems.” ArXiv, vol. q-MN, ArXiv, 2010, pp. 1–52.

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