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Bioinformation Technology (BIT) and Biointelligence (BI)
[바이오정보기술(BIT)과 바이오지능(Biointelligence)]
A tutorial to be presented at 2001 Spring Conference of Korea Information Science Society (KISS)
Byoung-Tak ZhangSchool of Computer Science and Engineering
Seoul National University
E-mail: [email protected]://scai.snu.ac.kr./~btzhang/
This material is available at http://scai.snu.ac.kr/~btzhang/
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Outline
? Introduction
? Bioinformation Technology (BIT) = BT + IT
? Bioinformatics, Biocomputing, Biochips
? Biointelligence = BT + AI
? Concept, Methodology, Technology
? Applied Biointelligence
? Summary
? Further Information
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Introduction
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Biotechnology Revolution
Year
2000
Biotechnology Age
1950
Information Age
AD 1760
Industrial Age
Econom
ical Value
Agricultural Age
BC 6000
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Human Genome Project
Genome Health Implications
A New DiseaseEncyclopedia
New Genetic Fingerprints
NewDiagnostics
NewTreatments
Goals•Identify the approximate 100,000 genesin human DNA
•Determine the sequences of the 3 billionbases that make up human DNA
•Store this information in database•Develop tools for data analysis•Address the ethical, legal and social issues that arise from genome research
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Bioinformation Technology (BIT)= BT + IT
BTIT
In silico Biology (e.g. Bioinformatics)
In vivo Informatics (e.g. Biocomputing)
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Bioinformation TechnologyBioinformaticsBiocomputing
Biochips
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Bioinformatics
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What is Bioinformatics?
? Bioinformatics vs. Computationl Biology? Bioinformatik (in German): Biology-based computer
science as well as bioinformatics (in English)
Informatics – computer scienceBio – molecular biology
Bioinformatics – solving problems arising from biology using methodology from computer science.
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What is DNA?
AACCTGCGGAAGGATCATTACCGAGTGCGGGTCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCGTTGCTTCG
GCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCTCTGCCCCCCGGGCCCGTGCCCGCCGGA GACCCCAACAC
GAACACTGTCTGAAAGCGTGCAGTCTGAGTTGATTGAATGCAATCAGTTAAAACTTTCAACAATGGATCTCTTGGTTCCGGCATGCAATCAGTCCCGTTGCTTCGGCACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCG
TTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGCTATTGTACCCGTTGCTTCGGATCTCTTGGGGATCTCTTGGTTCCGGCATGCAATCAGTCCCGTTGCTTCGGC
ACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCACCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGG
CGGCCGCCGGGGGCACTGTCTGAAAGCTCGGCCGCC
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The Structure of DNASugar-phosphate
backbone
HydrogenbondsBase
?RNA consists of A, C, G, and U, where U plays the same role as T ?Watson-Crick complementary pairs:
?A and T (or A and U) ?C and G
?Hybridization: when 2 strands of complementary DNA (or one strand of DNA and one strand of complementary RNA) stick together
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Molecular Biology: Flow of Information
DNA RNA Protein Function
DNA
PheCysLysCysAspCysArgSerA
laLeu
Protein
AC
TG
GA A
GCT
TA
TC
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DNA (gene) RNA Protein
controlstatement
TATA start
Terminationstop
controlstatement
Ribosomebinding
gene
Transcription (RNA polymerase)
mRNA
Protein
Transcription (Ribosome)
5’ utr 3’ utr
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Nucleotide and Protein Sequence
aacctgcgga aggatcattaccgagtgcgg gtcctttgggcccaacctcc catccgtgtctattgtaccc tgttgcttcggcgggcccgc cgcttgtcggccgccggggg ggcgcctctgccccccgggc ccgtgcccgccggagacccc aacacgaacactgtctgaaa gcgtgcagtctgagttgatt gaatgcaatcagttaaaact ttcaacaatggatctcttgg ttccggctgc tattgtaccc tgttgcttcggcgggcccgc cgcttgtcggccgccggggg ggcgcctctgccccccgggc ccgtgcccgccggagacccc tgttgcttcggcgggcccgc cgcttgtcggccgccggggg cggagacccc
gcgggcccgc cgcttgtcggccgccggggg ggcgcctctgccccccgggc ccgtgcccgcaacctgcgga aggatcattaccgagtgcgg gtcctttgggcccaacctcc catccgtgtctattgtaccc tgttgcttcggcgggcccgc cgcttgtcggagttaaaact ttcaacaatggatctcttgg ttccggctgc tattgtaccc tgttgcttcggcgggcccgc cgcttgtcggccgccggggg ggcgcctctgccccccgggc ccgtgcccgccggagacccc tgttgcttcggcgggcccgc cgcttgtcggccgccggggg cggagacccc gcgggcccgc cgcttgtcggccgccggggg ggcgcctctg
cgcttgtcgg ccgccgggggccccccgggc ccgtgcccgccggagacccc aacacgaacactgtctgaaa gcgtgcagtctgagttgatt gaatgcaatcagttaaaact ttcaacaatggatctcttgg aacctgcggaccgagtgcgg gtcctttgggcccaacctcc catccgtgtctattgtaccc tgttgcttcggcgggcccgc cgcttgtcggccgccggggg ggcgcctctgagttaaaact ttcaacaatggatctcttgg ttccggctgc tattgtaccc tgttgcttcggcgggcccgc cgcttgtcggccgccggggg ggcgcctctgccccccgggc ccgtgcccgccggagacccc tgttgcttcg
SQ sequence 1344 BP; 291 A; C; 401 G; 278 T; 0 other
DNA (Nucleotide) Sequence
CG2B_MARGL Length: 388 April 2, 1997 14:55 Type: P Check: 9613 .. 1
MLNGENVDSR IMGKVATRAS SKGVKSTLGT RGALENISNV ARNNLQAGAK KELVKAKRGM TKSKATSSLQ SVMGLNVEPM EKAKPQSPEP MDMSEINSAL EAFSQNLLEG VEDIDKNDFD NPQLCSEFVN DIYQYMRKLE REFKVRTDYM TIQEITERMR SILIDWLVQV HLRFHLLQET LFLTIQILDR YLEVQPVSKN KLQLVGVTSM LIAAKYEEMY PPEIGDFVYI TDNAYTKAQI RSMECNILRR LDFSLGKPLC IHFLRRNSKA GGVDGQKHTM AKYLMELTLP EYAFVPYDPS EIAAAALCLS SKILEPDMEW GTTLVHYSAY SEDHLMPIVQ KMALVLKNAP TAKFQAVRKK YSSAKFMNVS TISALTSSTV MDLADQMC
Protein (Amino Acid) Sequence
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Some Facts
? 1014 cells in the human body.? 3.109 letters in the DNA code in every cell in your
body.? DNA differs between humans by 0.2%, (1 in 500
bases).? Human DNA is 98% identical to that of
chimpanzees.? 97% of DNA in the human genome has no known
function.
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EMBL Database Growth
0
1
2
3
4
5
6
7
8
9
10
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000year
millio
ns o
f record
s
total number of records (millions)
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Bioinformatics Is About:
? Elicitation of DNA sequences from genetic material
? Sequence annotation (e.g. with information from experiments)
? Understanding the control of gene expression (i.e. under what circumstances proteins are transcribed from DNA)
? The relationship between the amino acid sequence of proteins and their structure.
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Background of Bioinformatics
? Biological information infra? Biological information management systems? Analysis software tools? Communication networks for biological research
? Massive biological databases? DNA/RNA sequences? Protein sequences
? Genetic map linkage data? Biochemical reactions and pathways
? Need to integrate these resources to model biological reality and exploit the biological knowledge that is being gathered
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Extension of BioinformaticsConcept ? Genomics
? Functional genomics? Structural genomics
? Proteomics: large scale analysis of the proteins of an organism
? Pharmacogenomics: developing new drugs that will target a particular disease
? Microarry: DNA chip, protein chip
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Applications of Bioinformatics
? Drug design? Identification of genetic risk factors? Gene therapy? Genetic modification of food crops and animals? Biological warfare, crime etc.
? Personal Medicine?? E-Doctor?
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SNP (Single Nucleotide Polymorphism)Finding single nucleotide changes at specific regions of genes
?Diagnosis of hereditary diseases?Personal drug?Finding more effective drugs and
treatments
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Problems in Bioinformatics
Structure analysis? Protein structure comparison? Protein structure prediction ? RNA structure modeling
Pathway analysis? Metabolic pathway? Regulatory networks
Sequence analysis? Sequence alignment? Structure and function prediction? Gene finding
Expression analysis? Gen expression analysis? Gene clustering
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The Complete Microarray Bioinformatics Solution
DataManagement
Databases
StatisticalAnalysis
ImageProcessing
Automation
DataMining
ClusterAnalysis
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Bioinformatics as Information Technology
Bioinformatics
InformationRetrieval
GenBank
SWISS-PROT
Hardware
Agent
MachineLearning
Algorithm
Supercomputing
Information filtering
Monitoring agent
ClusteringRule discovery
Pattern recognition
Sequence alignment
Biomedical text analysis
Database
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Bioinformatics on the Web
sample
array
hybridization
scanner
relational
database
Data management
The experimental process
webinterface
image analysis results andsummaries
links to otherinformation resources
downloaddata to otherapplications
Data analysis and interpretation
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Biocomputing
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Biocomputing vs. Bioinformatics
BTIT
Bioinformatics
Biocomputing
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Traveling Salesman Problem
The traveling salesman problem: as the number of cities grows, even supercomputers have difficulty finding the shortest path.
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3
2 5
6
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Adleman’s Molecular Computer: A Brute Force Method
Each city (vertex) is represented by a
different sequence of nucleotides (6
here, but Adlemanused 20).
A DNA linker (edge) joining two city
(vertex) strands.
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AGCTTAGG
ATGGCATG
ATCCTACC
Vertex 1 Vertex 2
Edge 1? 2
Step 1 : Hybridization
AGCTTAGG ATGGCATGATCC TACC
AGCTTAGGATCCTACC
Step 2 : Ligation
AGCTTAGGATGGCATGGAATCCGATGCATGGCTCGAATCC ACGTACCG
Vertex 1
ATGGCATG
Vertex 4
Step 3 : PCR
32 bp 16 bp
Step 4 : Gel Electrophoresis
AGCTTAGGATGGCATGGAATCCGA…TCGAATCC
Bead for vertex 1
Step 5 : Magnetic Bead Affinity Separation
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Molecular Operators for DNA Computing
•Hybridization: complementary pairing of two single-stranded polynucleotides
5’- AGCATCCA –3’
3’- TCGTAGGT –5’
+5’- AGCATCCA –3’3’- TGCTAGGT –5’
•Ligation: attaching sticky ends to a blunt-ended molecule
TGACTACGACTG
ATGCATGCTACG
+ ATGCATGCTGACTACGTACGTGAC
sticky end
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DNA finds a solution!
A Hamiltonian path with all vertices included is isolated and recovered
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Why DNA Computing?
? 6.022 ? 1023 molecules / mole? Immense, Brute Force Search of All Possibilities
? Desktop: 109 operations / sec? Supercomputer: 1012 operations / sec? 1 ? mol of DNA: 1026 reactions
? Favorable Energetics: Gibb’s Free Energy
? 1 J for 2 ? 1019 operations? Storage Capacity: 1 bit per cubic nanometer
-1mol 8kcalG ???
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DNA Computers vs. Conventional Computers
electronic data are vulnerable but can be backed up easily
DNA is sensitive to chemical deterioration
setting up only requires keyboard input
setting up a problem may involve considerable preparations
smaller memorycan provide huge memory in small space
can do substantially fewer operations simultaneously
can do billions of operationssimultaneously
fast at individual operationsslow at individual operations
Microchip-based computersDNA-based computers
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Research Groups
?MIT, Caltech, Princeton University, Bell Labs ? EMCC (European Molecular Computing
Consortium) is composed of national groups from 11 European countries
? BioMIP Institute (BioMolecular Information Processing) at the German National Research Center for Information Technology (GMD)
?Molecular Computer Project (MCP) in Japan? Leiden Center for Natural Computation (LCNC)
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Applications of BiomolecularComputing? Massively parallel problem solving? Combinatorial optimization? Molecular nano-memory with fast associative search? AI problem solving? Medical diagnosis? Cryptography? Drug discovery? Further impact in biology and medicine :
? Wet biological data bases ? Processing of DNA labeled with digital data ? Sequence comparison ? Fingerprinting
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Biochips
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DNA Chip
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DNA Chip Technology
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Classification of DNA Chip Technology
Photolithography
Inkjetting
Mechanical micro-spotting
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How DNA Chips Are Made
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Photolithography Chip
.Light-directed Oligonucleotide Synthesis
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Microarray Robot
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DNA Chip Applications
? Gene discovery: gene/mutated gene? Growth, behavior, homeostasis …
? Disease diagnosis? Drug discovery: Pharmacogenomics? Toxicological research: Toxicogenomics
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Protein Chips
? A new paradigm in protein molecular mapping strategies
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Bioelectronic Devices
Au Coated Glass
Bio-Memory Device
Au
Cyt c
GFP
Glass
Electron Sensitizer
Electron Acceptor
Patterned Bio-Film
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History of Lab-on-a-Chip
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Integrates sample handling, separation and detection and data analysis for: DNA, RNA and protein solutions using LabChip technology.
Lab-on-a-chip Technology
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Biointelligence
Concept and HistoryMethodologyTechnologyApplications
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Concept and History
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Biointelligence (BI)
? Study of artificial intelligence based on biotechnology
? Biointelligence as a new technology? Solving AI problems using biotechnology (BT) or BIT? Using BT to solve AI problems
? Biointelligence as a new application? Using AI techniques to solve BT problems
? Biointelligence as a new research field? Biochemistry = Biology + Chemistry? Bioinformatics = Biology + Informatics? Biointelligence (BI) = Biology (BT) + Intelligence (AI)
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Relationships to Existing Research Areas
Information Technology
(IT)
AIBioinformationTechnology (BIT)
Biotechnology(BT)
Biointelligence(BI)
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Related Research Fields
Artificial Intelligence
BiointelligenceBioinformatics Biocomputing
Biochips Bioinformation Technology
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Biological AI: History
Symbolic AI
• 1943: Production rules • 1956: “Artificial Intelligence”• 1958: LISP AI language• 1965: Resolution theorem
proving
• 1970: PROLOG language• 1971: STRIPS planner• 1973: MYCIN expert system• 1982-92: Fifth generation
computer systems project• 1986: Society of mind
• 1994: Intelligent agents
Biological AI
• 1943: McCulloch-Pitt’s neurons • 1959: Perceptron• 1965: Cybernetics• 1966: Simulated evolution• 1966: Self-reproducing automata
• 1975: Genetic algorithm
• 1982: Neural networks• 1986: Connectionism• 1987: Artificial life
• 1992: Genetic programming• 1994: DNA computing
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Paradigm Shift in AI Research
?Symbolic Subsymbolic
?Knowledge-based
Learning-based
?Deduction Induction
?Model-driven Data-driven
?Top-down Bottom-up
?High-level Low-level
?Reflective Reflexive
?Individual Collective
?Deep-thought Reactive behavior
?Syntactic Semantic
?Discrete Continuous
?Deterministic Stochastic
?Logic Probabilistic
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Computers and Biosystems
(Moravec, 1988)
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Biointelligence Methodology
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Four Levels of Biointelligence
Molecular Intelligence
Cellular Intelligence
Organismic Intelligence
Ecological Intelligence
<= Focus of classical AI
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Comparison of BiointelligenceTechnologies
evolvablehardware
neurochipsembryonic chipslab-on-a-chip
DNA chipsprotein chips
Chips
evolutionaryalgorithms
neural netssemantic nets
cell-automataimmune nets
DNA/molecularcomputing
Computationalmodels
cooperationcompetition
excitationinhibition
cell divisiondifferentiation
ligationhybridization
Basic operation
audiovisual,symbolic
neuro-transmitters
electrochemicalsignals
lock-keymechanism
Communication
yearsmonthsdayssecondsTime (typical)
evolutionlearningdevelopmentself-assemblyPhenomenon
ecologyneurobiologycell biologyMolecularbiology
Biology
populationorganismcellsmoleculesBasic unit
EcologicalIntelligence
OrganismicIntelligence
CellularIntelligence
MolecularIntelligence
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Biomolecular Information Processing
DNA Sequence
mRNA Sequence
Protein Sequence
Folded Protein
Transcription
Translation
Folding
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Features
? Stochastic (vs. deterministic)?Massively parallel (vs. sequential)? Self-assembly (vs. programming)? Liquid rather than solid-state? Biochemical (vs. electronic)? Biomolecule-based (vs. silicon-based)
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Principles and Theoretical Toolsfor Biointelligence Research
? Self-Assembly? Self-Reproduction? Uncertainty Principle? Occam’s Razor Principle
? Information Theory? Probability Theory? Thermodynamics? Statistical Physics
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Biology-Based AI Models: Existing Examples
Evolutionary Computation:
computational method
simulating natural selection
DNA Computing: information
processing based on
biomolecules
Neural Networks: computation
model imitating brain structure
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Neural Computation: The Brain as Computer
1. 1011 neurons with1014 synapses
2. Speed: 10-3 sec3. Distributed processing4. Nonlinear processing5. Parallel processing
1. A single processor with complex circuits
2. Speed: 10 –9 sec 3. Central processing4. Arithmetic operation (linearity) 5. Sequential processing
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From Biological Neurons to Artificial Neurons
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“Owing to this struggle for life, any variation, however slight and from whatever cause proceeding, if it be in any degree profitable to an individual of any species, in its infinitely complex relations to other organic beings and to external nature, will tend to the preservation of that individual, and will generally be inherited by its offspring.”
Origin of Species “Charles Darwin (1859)”
Evolutionary Computation: Nature as Computer
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Variation and Selection: The Principle
solutions
1100101010101110111000110110011100110001
110010 1110
101110 1110110010 1010
crossover
mutation
00110
101110 1010
10011
00110 10010
evaluation
110010111010111010100011001001
solutions
fitnesscomputation
roulettewheel
selectionnew population
encoding
chromosomes
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DNA Computing: BioMoleculesas Computer
011001101010001 ATGCTCGAAGCT
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HPP
...
......
...ATG
ACG
TGC
CGA
TAA
GCA
CGT...
...
...
...... ...
...
...
10
3
2 56
4
Solution
ATGTGCTAACGAACG
ACGCGAGCATAAATGTGCCGT
TAAACG
CGACGT
TAAACGGCAACG
...
...
...
...
CGACGTAGCCGT
...
...
...
ACGCGAGCATAAATGTGCCGTACGCGTAGCCGT
ACGCGT
......
...
...
...
ACGGCATAAATGTGCACGCGTACGCGAGCATAAATGCGATGCCGT
ACGCGAGCATAAATGTGCCGT
...... ......
...
ACGCGAGCATAAATGTGCCGT
...
.........
...
Decoding
Ligation
Encoding
Gel Electrophoresis
Affinity Column
ACGCGAGCATAAATGTGCACGCGT
ACGCGAGCATAAATGCGATGCACGCGT
ACGCGAGCATAAATGTGCACGCGT
ACGCGAGCATAAATGCGATGCACGCGT
2
0 13 4
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Node 0: ACG Node 3: TAANode 1: CGA Node 4: ATGNode 2: GCA Node 5: TGC
Node 6: CGT
Flow of DNA Computing
PCR(Polymerase
Chain Reaction)
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Biointelligence Technology
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Biointelligence on a Chip?
Biological Computer
MolecularElectronics
BioinformationTechnology
Computing Models:The limit of conventional computing models
Computing Devices : The limit of siliconesemiconductor technology
Information Technology
Biotechnology
Biointelligence Chip
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Intelligent BiomolecularInformation Processing
Bio-Memory Biocomputing
Theoretical Models
S
GFP
Cytochrome c
S
GFP
Cytochrome c
Bio-Processor
Input AInput AController
OutputReaction Chamber
(Calculating)
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분자 컴퓨터 모델
Bio-diode 소자
• 단일 전자 소자• Bio-transistor 구성• Bio-memory
Bio-logic gate 소자
• 단일 전자 소자• 직렬 processor• Thz급 처리속도
One-chip 적용
분자 연산 소자
• 병렬 processor• Thz급 처리속도
(CPU)
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Evolvable Biomolecular Hardware
? Sequence programmable and evolvable molecular systems have been constructed as cell-free chemical systems using biomolecules such as DNA and proteins.
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Molecular Storage for Massively Parallel Information Retrieval
Trillions of DNA
경기도 구리시 아천동 246-2648-7921원 빈
인천시 남구 주안5동 23-1352-4730송승헌
서울송파구 잠실본동 211419-1332홍길동
주 소전화번호성 명
서울시 영등포구신길 2동 11418-9362송혜교
…
전화번호부
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The ‘Knight Problem’
? Given an n x n chess board, what position can a knight occupy such that no knight can attack another knight.
? An example of SAT? NP-complete for infinite boards? Example: 3 x 3 Board
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Three Solutions to the ‘Knight Problem’
? Problem solved: 3 of the 31 solutions to the knight conundrum found by the RNA-based machine
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Solving Logic Problems by Molecular Computing
? Satisfiability Problem? Find Boolean values for
variables that make the given formula true
? 3-SAT Problem? Every NP problems can be
seen as the search for a solution that simultaneously satisfies a number of logical clauses, each composed of three variables.
)or or ( AND )or or ()or or ( AND )or or (
321321
654321
xxxxxxxxxxxx
)()()( 324431 xxxxxx ?????
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DNA Chips for DNA Computing
I. Make: oligomer synthesis
II. Attach (Immobilized): 5’HS-C6-T15-CCTTvvvvvvvvTTCG-3’
III. Mark: hybridization
IV. Destroy: Enzyme rxn (ex.EcoRI)
V. Unmark* 문제를 만족시키지 않는 모든 strand
제거
VI. Readout: N cycle의 마지막 단계에 해가 남게 되면, PCR로 증폭하여 확인!
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Variable Sequences and the Encoding Scheme
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Tree-dimensional Plot and Histogram of the Fluorescence
? S3: w=0, x=0, y=1, z=1? S7: w=0, x=1, y=1, z=1? S8: w=1, x=0, y=0, z=0? S9 : w=1, x=0, y=0, z=1
? y=1: (w V x V y) 만족
? z=1: (w V y V z) 만족
? x=0 or y=1: (x V y) 만족
? w=0: (w V y) 만족
? Four spots with high fluorescence intensity correspond to the four expected solutions.
? DNA sequences identified in the readout step via addressed array hybridization.
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Applied Biointelligence
Bio-based AI Methods for Solving Bio-problems
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Spillover of Biointelligence
Understanding information flow in biological construction
HealthcareDrugs Foods
Analysis, modeling and management tools
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Multilayer Perceptrons for Gene Finding and Prediction
Coding potential value
GC Composition
Length
Donor
Acceptor
Intron vocabulary
basesDiscrete
exon score
0
1
sequence
score
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Self-Organizing Maps for DNA Microarray Data Analysis
Two-dimensional arrayof postsynaptic neurons
Bundle of synapticconnections
Winningneurons
Input
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Biological Information ExtractionText Data
DB
LocationDate
DB Record
Database TemplateFilling
Data Analysis &Field Identify
Data Classify &Field Extraction
Information Extraction
Field PropertyIdentify & Learning
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Medical Biointelligence
Automation of genome expressionanalysis
Integration ofmolecular data
Inference andmodeling systems
Molecular classification of cancer
Diagnosissystems
Organismmodeling
Drug design
Key aspects addressed Goal
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E-Doctor
Diagnosis Expert System
Self-diagnosisPharmacy
Hospital
Personal Medicine
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Biorobotics
? Robot = Mechanical + Electronic (+ Biological)? Biorobot = Biological + (Mechanical + Electronic)? Biological Robots with Biointelligence
? Self-reproduction? Evolution? Learning
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Conclusions
? IT gets a growing importance in the advancement of BT (e.g., bioinformatics).
? IT can benefit much from BT (e.g., biocomputing and biochips)
? Bioinformation technology (BIT) is essential as a next-generation information technology.
? From the AI point of view, biosystems are existing proofs of intelligent systems.
? Biointelligence defined as a study of artificial intelligence based on biotechnology is a new technology and application area at the intersection of BT and IT.
? Biological AI technologies can provide a short cut for building AI machines.
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“The interface between biological systems and computational systems will become blurred, allowing powerful computational control of biological systems and implantation of computer interfaces into the human brain. Biology will be become the dominant metaphor for computer science, providing a framework for understanding and constructing complex computations.”
- Mark Gerstein
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Further Information
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Journals & Conferences? Journals
? Biological Cybernetics (Springer)? BioSystems(Elsevier)? Artificial Intelligence in Medicine? Bioinformatics (Oxford University Press)? Computer Applications in the Bioscience (Oxford University Press)? Computers in Biology and Medicine (Elsevier)? IEEE Transactions on Biomedical Engineering? IEEE Transactions on Evolutionary Computation
? Conferences? International Conference on Intelligent Systems for Molecular Biology (ISMB)? Pacific Symposium on Biocomputing (PSB)? International Conference on Computational Molecular Biology (RECOMB)? IBC’s Annual Conference on Biochip Technologies? International Meeting on DNA Based Computers? IEEE Bioinformatics and Bioengineering Symposium (BIBE)? International Symposium on Medical Data Analysis (ISMDA)
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Web Resources: Bioinformatics
? ANGIS - The Australian National Genomic Information Service: http://morgan.angis.su.oz.au/
? Australian National University (ANU) Bioinformatics: http://life.anu.edu.au/? BioMolecular Engineering Research Center (BMERC): http://bmerc-www.bu.edu/ ? Brutlag bioinformatics group: http://motif.stanford.edu/? Columbia University Bioinformatics Center (CUBIC): http://cubic.bioc.columbia.edu/? European Bioinformatics Institute (EBI): http://www.ebi.ac.uk/? European Molecular Biology Laboratory (EMBL): http://www.embl-heidelberg.de/ ? Genetic Information Research Institute: http://www.girinst .org/? GMD-SCAI: http://www.gmd.de/SCAI/scai_home.html ? Harvard Biological Laboratories: http://golgi.harvard.edu/ ? Laurence H. BakerCenter for Bioinformatics and Biological Statistics:
http://www.bioinformatics.iastate.edu/? NASA Center for Bioinformatics: http://biocomp.arc.nasa.gov/? NCSA Computational Biology: http://www.ncsa.uiuc.edu/Apps/CB/? Stockholm Bioinformatics Center: http://www.sbc.su.se/? USC Computational Biology: http://www-hto.usc.edu/? W. M. Keck Center for Computational Biology : http://www-bioc.rice.edu/
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Web Resources: Biocomputing
? European Molecular Computing Consortium (EMCC): http://www.csc.liv.ac.uk/~emcc/
? BioMolecular Information Processing (BioMip): http://www.gmd.de/BIOMIP
? Leiden Center for Natural Computation (LCNC): http://www.wi.leidenuniv.nl/~lcnc/
? Biomolecular Computation (BMC): http://bmc.cs.duke.edu/
? DNA Computing and Informatics at Surfaces: http://www.corninfo.chem.wisc.edu/writings/DNAcomputing.html
? SNU Molecular Evolutionary Computing (MEC) Project:http://scai.snu.ac.kr/Research/
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Web Resources: Biochips
? DNA Microarry (Genome Chip): http://www.gene-chips.com/
? Large-Scale Gene Expression and Microarray Link and Resources: http://industry.ebi.ac.uk/~alan/MicroArray/
? The Microarray Centre at The Ontario Cancer Institute: http://www.oci.utoronto.ca/services/microarray/
? Lab-on-a-Chip resources: http://www.lab-on-a-chip.com/
?Mailing List: [email protected]
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Books: Bioinformatics
? Cynthia Gibas and Per Jambeck, Developing Bioinformatics Computer Skills, O’REILLY, 2001.
? Peter Clote and Rolf Backofen, Computational Molecular Biology: An Introduction, A John Wiley & Sons, Inc., 2000.
? Arun Jagota, Data Analysis and Classification for Bioinformatics, 2000.
? Hooman H. Rashidi and Lukas K. Buehler, Bioinformatics Basics Applications in Biological Science and Medicine, 1999.
? Pierre Baldi and Soren Brunak, Bioinformatics: The Machine Learning Approach, MIT Press, 1998.
? Andreas Baxevanis and B. F. Francis Ouellette, Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, A John Wiley & Sons, Inc., 1998.
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Books: Biocomputing
? Cristian S, Calude and Gheorghe Paun, Computing with Cells and Atoms: An introduction to quantum, DNA and membrane computing , Taylor & Francis, 2001.
? Pâun, G., Ed., Computing With Bio-Molecules: Theory and Experiments, Springer, 1999.
? Gheorghe Paun, Grzegorz Rozenberg and Arto Salomaa, DNA Computing, New Computing Paradigms, Springer, 1998.
? C. S. Calude, J. Casti and M. J. Dinneen, Unconventional Models of Computation, Springer, 1998.
? Tono Gramss, Stefan Bornholdt, Michael Gross, Melanie Mitchell and thomas Pellizzari, Non-Standard Computation: Molecular Computation-Cellular Automata-Evolutionary Algorithms-Quantum Computers, Wiley-Vch, 1997.
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For more information:
http://scai.snu.ac.kr/