1. An intuitive introduction to information theory Ivo Grosse Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben Bioinformatics Centre Gatersleben-Halle

2. 2 Outline  Why information theory?  An intuitive introduction

3. 3 History of biology St. Thomas Monastry, Brno

4. 4 Genetics Gregor Mendel 1822 – 1884 1866 Mendel‘s laws Foundation of Genetics Ca. 1900: Biology becomes a quantitative science

5. 5 50 years later … 1953 James Watson & Francis Crick

6. 6 50 years later … 1953

7. 7

8. 8 DNA Watson & Crick 1953 Double helix structure of DNA 1953: Biology becomes a molecular science

9. 9 1953 – 2003 … 50 years of revolutionary discoveries

10. 10 1989

11. 11 1989 Goals:  Identify all of the ca. 30.000 genes  Identify all of the ca. 3.000.000.000 base pairs  Store all information in databases  Develop new software for data analysis

12. 12 2003 Human Genome Project officially finished 2003: Biology becomes an information science

13. 13 2003 – 2053 … biology = information science

14. 14 2003 – 2053 … biology = information science SystemsBiology

15. 15 What is information?  Many intuitive definitions  Most of them wrong  One clean definition since 1948  Requires 3 steps -Entropy -Conditional entropy -Mutual information

16. 16 Before starting with entropy … Who is the father of information theory? Who is this? Claude Shannon 1916 – 2001 A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379–423 & 623–656, 1948

17. 17 Before starting with entropy … Who is the grandfather of information theory? Simon bar Kochba Ca. 100 – 135 Jewish guerilla fighter against Roman Empire (132 – 135)

18. 18 Entropy  Given a text composed from an alphabet of 32 letters (each letter equally probable)  Person A chooses a letter X (randomly)  Person B wants to know this letter  B may ask only binary questions  Question: how many binary questions must B ask in order to learn which letter X was chosen by A  Answer: entropy H(X)  Here: H(X) = 5 bit

19. 19 Conditional entropy (1)  The sky is blu_  How many binary questions?  5?  No!  Why?  What’s wrong?  The context tells us “something” about the missing letter X

20. 20 Conditional entropy (2)  Given a text composed from an alphabet of 32 letters (each letter equally probable)  Person A chooses a letter X (randomly)  Person B wants to know this letter  B may ask only binary questions  A may tell B the letter Y preceding X  E.g.  L_  Q_  Question: how many binary questions must B ask in order to learn which letter X was chosen by A  Answer: conditional entropy H(X|Y)

21. 21 Conditional entropy (3)  H(X|Y) <= H(X)  Clear!  In worst case – namely if B ignores all “information” in Y about X – B needs H(X) binary questions  Under no circumstances should B need more than H(X) binary questions  Knowledge of Y cannot increase the number of binary questions  Knowledge can never harm! (mathematical statement, perhaps not true in real life )

22. 22 Mutual information (1)  Compare two situations:  I: learn X without knowing Y  II: learn X with knowing Y  How many binary questions in case of I?  H(X)  How many binary questions in case of II?  H(X|Y)  Question:How many binary questions could B save in case of II?  Question:How many binary questions could B save by knowing Y?  Answer:I(X;Y) = H(X) – H(X|Y)  I(X;Y) = information in Y about X

23. 23 Mutual information (2)  H(X|Y) = 0  In worst case – namely if B ignores all information in Y about X or if there is no information in Y about X – then I(X;Y) = 0  Information in Y about X can never be negative  Knowledge can never harm! (mathematical statement, perhaps not true in real life )

24. 24 Mutual information (3)  Example 1: random sequence composed of A, C, G, T (equally probable)  I(X;Y) = ?  H(X) = 2 bit  H(X|Y) = 2 bit  I(X;Y) = H(Y) – H(X|Y) = 0 bit  Example 2: deterministic sequence … ACGT ACGT ACGT ACGT …  I(X;Y) = ?  H(X) = 2 bit  H(X|Y) = 0 bit  I(X;Y)=H(Y) – H(X|Y)=2 bit

25. 25 Mutual information (4)  I(X;Y) = I(Y;X)  Always! For any X and any Y!  Information in Y about X = information in X about Y  Examples:  How much information is there in the amino acid sequence about the secondary structure? How much information is there in the secondary structure about the amino acid sequence?  How much information is there in the expression profile about the function of the gene? How much information is there in the function of the gene about the expression profile?  Mutual information

26. 26 Summary  Entropy  Conditional entropy  Mutual information  There is no such thing as information content  Information not defined for a single variable  2 random variables needed to talk about information  Information in Y about X  I(X;Y) = I(Y;X)  info in Y about X = info in X about Y  I(X;Y) >= 0  information never negative  knowledge cannot harm  I(X;Y) = 0 if and only ifX and Y statistically independent  I(X;Y) > 0 otherwise