This lecture focuses on models and methods used in information theory and
their application to real world problems at the example of molecular biology
(but not restricted to). In particular we cover

• Review Proability Theory
• Fundamentals of information theory
  • Typical sequences
  • Entropy and mutual information
  • Source and channel coding theorem
• Fundamentals of molecular biology
  • DNA
  • Gene expression/regulation
• Compression Distances
  • Metric based on MI
  • Lempel/Ziv (LZ)
  • Proof of LZ
• Estimation of discrete densities/entropy/mutual information
  • Maximum Likelihood
  • Baysian Techniques
• DNA and RNA sequencing
  • DNA sequencing: Capacity
  • RNA sequencing: Multiplexing  and Codes
  • RNA sequencing: Amplification Noise and Codes