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

Joost N. Kok

Artificial Intelligence:Artificial Intelligence:from Computer Science from Computer Science to Molecular Informatics to Molecular Informatics

Artificial IntelligenceArtificial Intelligence

Movie Artificial Intelligence by Steven Spielberg

Five year studies at universities of Utrecht, Amsterdam, Groningen and Maastricht


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.


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


First Robot World Cup Soccer Games held in Nagoya, Japan in 1997

Goal: team of robots beats the FIFA World Cup champion in 2050


Alan Turing Turing Award Turing Machine Turing Test


Turing Test


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


Turing Machine

Time ComplexityTime 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 ?

Natural ComputingNatural Computing

Computing carried on or inspired by (gleaned from)nature


Computers are to Computer Science as Comic Books to Literature (Joosen)


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

Evolutionary ComputingEvolutionary Computing

select mating partners

mutateevaluate

selectsurvivors

recombine

(terminate)

Initialize population, evaluate


ExamplesExamples

Evolutionary Art Nozzle

Example: Discrete RepresentationExample: Discrete Representation

Genotype: 8 bits

Phenotype: – integer

1*21*27 7 + 0*2+ 0*26 6 + 1*2+ 1*25 5 + 0*2+ 0*24 4 + 0*2+ 0*23 3 + 0*2+ 0*22 2 + 1*2+ 1*21 1 + 1*2+ 1*200 = 163= 163– a real number between 2.5 and 20.5

2.5 + 163/256 (20.5 - 2.5) = 13.96092.5 + 163/256 (20.5 - 2.5) = 13.9609– scheduleschedule

Example: MutationExample: Mutation

1 1 1 1 1 1 1 before

1 1 1 0 1 1 1 after

Mutation happens with probability pm for each bit

mutated bit

.

1 1 1 1 1 1 1 0 0 0 0 0 0 0 parentscut cut

1 1 1 0 0 0 0 0 0 0 1 1 1 1 offspring

Example: RecombinationExample: Recombination

Each chromosome is cut into 2 pieces which are recombined

Example: Fitness proportionate selectionExample: 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

select mating partners

mutateevaluate

selectsurvivors

recombine

(terminate)

Initialize population, evaluate


Molecular ComputingMolecular 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



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


Hamiltonian path problem

in

out


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


For each node, take unique random sequence over A, C, T, G

For each node, the sequence is of the same length


For every connection, construct a sequence from the sequences of the two nodes– Node 1: TATCGGATCGGTATATCCGA

– Node 2: GCTATTCGAGCTTAAAGCTA Inverse: GTATATCCGAGCTATTCGAG Sequence: CATATAGGCTCGATAAGCTC


Generate random paths through graph– Mix strings for all nodes with strings for all

arrows, together with Ligase enzyme


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


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


Create single strands by melting For each node, select those sequences that

anneal to the string of that node


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


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


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

Evolutionary Molecular ComputingEvolutionary Molecular Computing

Gene Assembly in CiliatesGene Assembly in Ciliates

CiliatesCiliates

Very ancient ( ~ 2 . 109 years ago) Very rich group ( ~ 10000 genetically different

organisms) Very important from the evolutionary point of

view

CiliatesCiliates

micronucleus

macronucleus

CiliatesCiliates

DNA molecules in micronucleus are very long (hundreds of kilo bps)

DNA molecules in macronucleus are gene-size, short (average ~ 2000 bps)




Micronucleus: cell mating Macronucleus: RNA transcripts (expression) Micro: I0 M1 I1 M2 I2 M3 … Ik Mk Ik+1

M = P1 N P2

Macro: permutation of (possibly rotated)M1,…, Mk and I0 ,…, Ik+1are removed

Molecular OperatorsMolecular Operators










Gene AssemblyGene Assembly

Pointer structures Linked Lists


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

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