1. Joost N. Kok Artificial Intelligence: from Computer Science to Molecular Informatics

2. Artificial Intelligence Movie Artificial Intelligence by Steven Spielberg Five year studies at universities of Utrecht, Amsterdam, Groningen and Maastricht

3. Artificial Intelligence The concept that machines can be improved to assume some capabilities normally thought to be like human intelligence such as learning, adapting, self-correction, etc. The extension of human intelligence through the use of computers, as in times past physical power was extended through the use of mechanical tools.

4. Artificial Intelligence On May 11, 1997, an IBM computer named Deep Blue whipped world chess champion Garry Kasparov in the deciding game of a six- game match

5. Artificial Intelligence First Robot World Cup Soccer Games held in Nagoya, Japan in 1997 Goal: team of robots beats the FIFA World Cup champion in 2050

6. Artificial Intelligence Alan Turing Turing Award Turing Machine Turing Test

7. Artificial Intelligence Turing Test

8. Artificial Intelligence Natural language processing: it needs to be able to communicate in a natural language like English Knowledge representation: it needs to be able to have knowledge and to store it somewhere Automated reasoning: it needs to be able to do reasoning based on the stored knowledge Machine learning: it needs to be able to learn from its environment

9. Artificial Intelligence Turing Machine

10. Time Complexity Turing machine gives notion of computability Time complexity: how many steps does it take to find an answer? Combinatorial Explosion Problems that are computable in polynomial time (class P) Problems that are verifiable in polynomial time (class NP) P equals NP ? P equals NP

11. Natural Computing Computing carried on or inspired by (gleaned from) nature

12. Natural Computing Computers are to Computer Science as Comic Books to Literature (Joosen)

13. Natural Computing –Evolutionary Computing –Molecular Computing –Gene Assembly in Ciliates

14. Evolutionary Computing

15. select mating partners mutate evaluate select survivors recombine (terminate) Initialize population, evaluate Evolutionary Computing

16. Examples Evolutionary Art Nozzle

17. Example: Discrete Representation Genotype: 8 bits Phenotype: 1*2 7 + 0*2 6 + 1*2 5 + 0*2 4 + 0*2 3 + 0*2 2 + 1*2 1 + 1*2 0 = 163 –integer 1*2 7 + 0*2 6 + 1*2 5 + 0*2 4 + 0*2 3 + 0*2 2 + 1*2 1 + 1*2 0 = 163 2.5 + 163/256 (20.5 - 2.5) = 13.9609 –a real number between 2.5 and 20.5 2.5 + 163/256 (20.5 - 2.5) = 13.9609 –schedule

18. Example: Mutation 1 1 1 1 1 1 1 before 1 1 1 0 1 1 1 after Mutation happens with probability p m for each bit mutated bit

19. . 1 1 1 1 1 1 10 0 0 0 0 0 0 parents cut 1 1 1 0 0 0 00 0 0 1 1 1 1 offspring Example: Recombination Each chromosome is cut into 2 pieces which are recombined

20. Example: Fitness proportionate selection Expected number of times f i is selected equals f i / average fitness Better (fitter) individuals have: –more space –more chance to be selected Best Worst

21. select mating partners mutate evaluate select survivors recombine (terminate) Initialize population, evaluate Evolutionary Computing

25. Molecular Computing

26. Implementation of algorithms in biological hardware, e.g. using DNA molecules and enzymes Power lies in massive parallel search Test tube may contain easily 10 15 strands of DNA Compared to computers very efficient in energy consumption, storage density and number of operations per second

27. Molecular Computing

28. DNA: sequence of nucleotides linked together by strong backbone Nucleotides have attached bases A, T, C, G: –Adenine –Thymine –Guanine –Cytosine Watson-Crick complementarity A-T C-G

29. Molecular Computing Hamiltonian path problem in out

30. Molecular Computing Algorithm –generate random paths through graph –keep only paths from the initial to the final node –keep only paths that enter exactly n nodes –keep only paths that enter all nodes –if any paths remain, the graph contains a Hamiltonian path

31. Molecular Computing For each node, take unique random sequence over A, C, T, G For each node, the sequence is of the same length

32. Molecular Computing For every connection, construct a sequence from the sequences of the two nodes –Node 1: TATCGGATCGGTATATCCGA –Node 2: GCTATTCGAGCTTAAAGCTA Inverse: GTATATCCGAGCTATTCGAG Sequence: CATATAGGCTCGATAAGCTC

33. Molecular Computing Generate random paths through graph –Mix strings for all nodes with strings for all arrows, together with Ligase enzyme

34. Molecular Computing Apply PCR (Polymerase Chain Reaction) amplification using as primers string for in and complement for string out

35. Molecular Computing Select molecules that encode paths that enter exactly n nodes by running contents of test tube through agarose gel and save DNA strands of the right length

36. Molecular Computing Create single strands by melting For each node, select those sequences that anneal to the string of that node

37. Molecular Computing Result: implementation of algorithm in DNA –First experiment took seven days –Now possible in seven seconds

38. Molecular Computing Operations: denaturing, annealing, separation, selection, multiplying Simulation of Turing Machine is possible Problems: –PCR and separation procedures are error prone –DNA may form non-existing pseudo-paths –DNA may form hairpin loops –Scalability

39. Molecular Computing Combine Evolutionary Computing with Molecular Computing (EDNA project) –Use potential errors as feature –Huge population sizes –Automation of DNA processing necessary Many more techniques from molecular biology can be used –Plasmids –Restriction Enzymes –Fluorescence

40. Evolutionary Molecular Computing

41. Gene Assembly in Ciliates

42. Ciliates Very ancient ( ~ 2. 10 9 years ago) Very rich group ( ~ 10000 genetically different organisms) Very important from the evolutionary point of view

43. Ciliates micronucleus macronucleus

44. Ciliates DNA molecules in micronucleus are very long (hundreds of kilo bps) DNA molecules in macronucleus are gene- size, short (average ~ 2000 bps)

45. Gene Assembly in Ciliates

47. Micronucleus: cell mating Macronucleus: RNA transcripts (expression) Micro: I 0 M 1 I 1 M 2 I 2 M 3 … I k M k I k+1 M = P 1 N P 2 Macro: permutation of (possibly rotated) M 1,…, M k and I 0,…, I k+1 are removed

48. Molecular Operators

62. Gene Assembly Pointer structures Linked Lists

63. Natural Computing Computing carried on or inspired by (gleaned from) nature –Evolutionary Computing –Neural Computing –Molecular Computing –Quantum Computing –Ant Computing