Joost%20N.%20Kok

1

Artificial Intelligencefrom Computer Science
to Molecular Informatics

• Joost N. Kok

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 ?

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

• Natural Computing
• Evolutionary Computing
• Molecular Computing
• Gene Assembly in Ciliates

14
Evolutionary Computing
15
Evolutionary Computing
Initialize population, evaluate
(terminate)
select mating partners
selectsurvivors
recombine
evaluate
mutate
16
Examples

• Evolutionary Art
• Nozzle

17
Example Discrete Representation

• Genotype 8 bits
• Phenotype
• integer 127 026 125 024 023 022
  121 120 163
• a real number between 2.5 and 20.52.5 163/256
  (20.5 - 2.5) 13.9609
• schedule

18
Example Mutation
1 1 1 1 1 1 1
before
mutated bit
Mutation happens with probability pm for each bit
19
Example Recombination

• Each chromosome is cut into 2 pieces which are
  recombined

offspring
.
20
Example Fitness proportionate selection

• Expected number of times fi is selected equals
  fi / average fitness

• Better (fitter) individuals have
• more space
• more chance to be selected

Best
Worst
21
Evolutionary Computing
Initialize population, evaluate
(terminate)
select mating partners
selectsurvivors
recombine
evaluate
mutate
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
Molecular Computing
26
Molecular Computing

• Implementation of algorithms in biological
  hardware, e.g. using DNA molecules and enzymes
• Power lies in massive parallel search
• Test tube may contain easily 1015 strands of DNA
• Compared to computers very efficient in energy
  consumption, storage density and number of
  operations per second

27
Molecular Computing
28
Molecular Computing

• 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 . 109 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
46
Gene Assembly in Ciliates
47
Gene Assembly in Ciliates

• Micronucleus cell mating
• Macronucleus RNA transcripts (expression)
• Micro I0 M1 I1 M2 I2 M3 Ik Mk Ik1
• M P1 N P2
• Macro permutation of (possibly rotated)M1,, Mk
  and I0 ,, Ik1are removed

48
Molecular Operators
49
Molecular Operators
50
Molecular Operators
51
Molecular Operators
52
Molecular Operators
53
Molecular Operators
54
Molecular Operators
55
Molecular Operators
56
Molecular Operators
57
(No Transcript)
58
(No Transcript)
59
(No Transcript)
60
(No Transcript)
61
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