바이오정보기술바이오정보기술 (BIT)(BIT) 과 과 바이오지능바이오지능 (Biointelligence)(Biointelligence)

장 병 탁서울대 컴퓨터공학부

E-mail: btzhang@cse.snu.ac.krhttp://scai.snu.ac.kr./~btzhang/

Byoung-Tak ZhangSchool of Computer Science and Engineering

Seoul National University

2

OutlineOutline

Introduction

Bioinformation Technology (BIT) = BT + IT

Bioinformatics, Biocomputing, Biochips

Biointelligence = BT + AI

Concept, Methodology, Technology

Applied Biointelligence

Summary

Further Information

3

IntroductionIntroduction

4

Biotechnology RevolutionBiotechnology Revolution

Year

2000

Biotechnology Age

1950

Information Age

AD 1760

Industrial Age

Econom

ical V

alue

Agricultural Age

BC 6000

5

Human Genome ProjectHuman Genome Project

Genome Health Implications

A New

Disease

Encyclopedia

New Genetic

Fingerprints

New

Diagnostics

New

Treatments

Goals• Identify the approximate 100,000 genes in human DNA• Determine the sequences of the 3 billion bases 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

6

Bioinformation Technology (BIT)Bioinformation Technology (BIT)= BT + IT= BT + IT

BTBTITIT

In silico Biology (e.g. Bioinformatics)

In vivo Informatics (e.g. Biocomputing)

7

Bioinformation TechnologyBioinformation TechnologyBioinformaticsBioinformaticsBiocomputingBiocomputing

BiochipsBiochips

8

BioinformaticsBioinformatics

9

What is Bioinformatics?What is Bioinformatics?

Bioinformatics vs. Computationl Biology Bioinformatik (in German): Biology-based computer scien

ce as well as bioinformatics (in English)

Bioinformatics vs. Computationl Biology Bioinformatik (in German): Biology-based computer scien

ce as well as bioinformatics (in English)

Informatics – computer science

Bio – molecular biology

Bioinformatics – solving problems arising from biology using methodology from computer science.

10

What is DNA?What is DNA?

AACCTGCGGAAGGATCATTACCGAGTGCGGGTCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCTCTGCCCCCCGGGCCCGTGCCCGCCGGAGACCCCAACACGAACACTGTCTGAAAGCGTGCAGTCTGAGTTGATTGAATGCAATCAGTTAAAACTTTCAACAATGGATCTCTTGGTTCCGGCATGCAATCAGTCCCGTTGCTTCGGCACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGCTATTGTACCCGTTGCTTCGGATCTCTTGGGGATCTCTTGGTTCCGGCATGCAATCAGTCCCGTTGCTTCGGCACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCACCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGGCCGCCGGGGGCACTGTCTGAAAGCTCGGCCGCC

11

The Structure of DNAThe 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

12

Molecular Biology: Flow of Molecular Biology: Flow of Information Information

DNA RNA Protein Function

DNA

Phe Cys LysCysAspCys ArgSerAla

Leu

Protein

AC

TG

GAAGCT

TATC

13

DNA (gene) RNA ProteinDNA (gene) RNA Protein

controlstatement

TATA start

Termination stop

controlstatement

Ribosomebinding

gene

Transcription (RNA polymerase)

mRNA

Protein

Transcription (Ribosome)

5’ utr 3’ utr

14

Nucleotide and Protein SequenceNucleotide 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

15

Some FactsSome 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.

16

EMBL Database GrowthEMBL 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)

17

Bioinformatics Is About: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.

18

Background of BioinformaticsBackground 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

19

Extension of Bioinformatics ConcExtension of Bioinformatics Concept ept 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

20

Applications of BioinformaticsApplications 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?

21

SNP (Single Nucleotide PolymorpSNP (Single Nucleotide Polymorphism)hism)

Finding single nucleotide changes at specific regions of genes

Diagnosis of hereditary diseases Personal drug Finding more effective drugs and

treatments

22

Problems in BioinformaticsProblems in Bioinformatics

Structure analysisStructure analysis Protein structure comparison Protein structure prediction RNA structure modeling

Pathway analysisPathway analysis Metabolic pathway Regulatory networks

Sequence analysisSequence analysis Sequence alignment Structure and function prediction Gene finding

Expression analysisExpression analysis Gen expression analysis Gene clustering

23

The Complete Microarray BioinforThe Complete Microarray Bioinformatics Solutionmatics Solution

DataManagement

Databases

StatisticalAnalysis

ImageProcessing

Automation

DataMining

ClusterAnalysis

24

Bioinformatics as Information TecBioinformatics as Information Technologyhnology

Bioinformatics

InformationRetrieval

GenBankSWISS-PROT

Hardware

Agent

Machine Learning

Algorithm

Supercomputing

Information filteringMonitoring agent

ClusteringRule discoveryPattern recognition

Sequence alignment

Biomedical text analysis

Database

25

Bioinformatics on the WebBioinformatics on the Web

sample

array

hybridization

scanner

relationaldatabase

Data management

The experimental process

webinterface

image analysis results andsummaries

links to otherinformation

resources

downloaddata to otherapplications

Data analysis and interpretation

26

BiocomputingBiocomputing

27

Biocomputing vs. BioinformaticsBiocomputing vs. Bioinformatics

BTBTITIT

Bioinformatics

Biocomputing

28

Traveling Salesman ProblemTraveling Salesman Problem

The traveling salesman problem: as the number of cities grows, even supercomputers have difficulty finding the shortest path.

1

0

3

2 5

6

4

29

Adleman’s Molecular Computer: Adleman’s Molecular Computer: A Brute Force MethodA Brute Force Method

Each city (vertex) is represented by a different sequence of nucleotides (6 here, but Ad

leman used 20).

A DNA linker (edge) joining two

city (vertex) strands.

30

AGCTTAGG

ATGGCATG

ATCCTACC

Vertex 1 Vertex 2

Edge 12

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

31

Molecular Operators for DNA Molecular Operators for DNA ComputingComputing

• 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

32

DNA finds a solution!DNA finds a solution!

A Hamiltonian path with all vertices included is isolated and recovered

33

Why DNA Computing?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

34

DNA Computers vs. Conventional DNA Computers vs. Conventional ComputersComputers

DNA-based computers Microchip-based computers

slow at individual operations fast at individual operations

can do billions of operations simultaneously

can do substantially fewer operations simultaneously

can provide huge memory in small space

smaller memory

setting up a problem may involve considerable preparations

setting up only requires keyboard input

DNA is sensitive to chemical deterioration

electronic data are vulnerable but can be backed up easily

35

Research GroupsResearch Groups

MIT, Caltech, Princeton University, Bell Labs EMCC (European Molecular Computing Consorti

um) 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)

36

Applications of Biomolecular ComApplications of Biomolecular Computingputing 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

37

BiochipsBiochips

38

DNA ChipDNA Chip

39

DNA Chip TechnologyDNA Chip Technology

40

Classification of DNA Chip Classification of DNA Chip TechnologyTechnology

Photolithography

Inkjetting

Mechanical micro-spotting

41

How DNA Chips Are MadeHow DNA Chips Are Made

42

Photolithography ChipPhotolithography Chip

.Light-directed Oligonucleotide Synthesis

43

Microarray RobotMicroarray Robot

44

DNA Chip ApplicationsDNA Chip Applications

Gene discovery: gene/mutated gene Growth, behavior, homeostasis …

Disease diagnosis Drug discovery: Pharmacogenomics Toxicological research: Toxicogenomics

45

Protein ChipsProtein Chips

A new paradigm in protein molecular mapping strategies

46

Bioelectronic Devices Bioelectronic Devices

Au Coated Glass

Bio-Memory Device

Au

Cyt c

GFP

Glass

Electron Sensitizer

Electron Acceptor

Patterned Bio-Film

47

History of Lab-on-a-ChipHistory of Lab-on-a-Chip

48

Integrates sample handling, separation and detection and data analysis for: DNA, RNA and protein solutions using LabChip technology.

Lab-on-a-chip TechnologyLab-on-a-chip Technology

49

BiointelligenceBiointelligence

Concept and HistoryConcept and HistoryMethodologyMethodologyTechnologyTechnologyApplicationsApplications

50

Concept and HistoryConcept and History

51

Biointelligence (BI)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)

52

Relationships to Existing Relationships to Existing Research AreasResearch Areas

Information Information TechnologyTechnology

(IT)(IT)

Information Information TechnologyTechnology

(IT)(IT)

AIAIAIAIBioinformationBioinformationTechnology (BIT)Technology (BIT)BioinformationBioinformationTechnology (BIT)Technology (BIT)

BiotechnologyBiotechnology(BT)(BT)

BiotechnologyBiotechnology(BT)(BT)

BiointelligenceBiointelligence(BI)(BI)

BiointelligenceBiointelligence(BI)(BI)

53

Related Research FieldsRelated Research Fields

Artificial IntelligenceArtificial Intelligence

BiointelligenceBiointelligenceBioinformaticsBioinformatics BiocomputingBiocomputing

BiochipsBiochipsBioinformation Bioinformation

TechnologyTechnology

Bioinformation Bioinformation

TechnologyTechnology

54

Biological AI: HistoryBiological AI: History

Symbolic AISymbolic 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

Symbolic AISymbolic 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 AIBiological 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

Biological AIBiological 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

55

Paradigm Shift in AI ResearchParadigm 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

56

Computers and BiosystemsComputers and Biosystems

(Moravec, 1988)(Moravec, 1988)

57

Biointelligence MethodologyBiointelligence Methodology

58

Four Levels of BiointelligenceFour Levels of Biointelligence

Molecular IntelligenceMolecular Intelligence

Cellular IntelligenceCellular Intelligence

Organismic IntelligenceOrganismic Intelligence

Ecological IntelligenceEcological Intelligence

<= Focus of classical AI

59

Comparison of Biointelligence TeComparison of Biointelligence Technologieschnologies

MolecularMolecular

IntelligenceIntelligenceCellularCellular

IntelligenceIntelligenceOrganismicOrganismicIntelligenceIntelligence

EcologicalEcological

IntelligenceIntelligence

Basic unitBasic unit molecules cells organism population

BiologyBiology Molecularbiology

cell biology neurobiology ecology

PhenomenonPhenomenon self-assembly development learning evolution

Time (typical)Time (typical) seconds days months years

CommunicatioCommunicationn

lock-keymechanism

electrochemicalsignals

neuro-transmitters

audiovisual,symbolic

Basic Basic operationoperation

ligationhybridization

cell divisiondifferentiation

excitationinhibition

cooperationcompetition

ComputationalComputational

modelsmodelsDNA/molecularcomputing

cell-automataimmune nets

neural netssemantic nets

evolutionaryalgorithms

ChipsChips DNA chipsprotein chips

embryonic chipslab-on-a-chip

neurochips evolvablehardware

60

Biomolecular Information ProcesBiomolecular Information Processingsing

DNA SequenceDNA SequenceDNA SequenceDNA Sequence

mRNA SequencemRNA SequencemRNA SequencemRNA Sequence

Protein SequenceProtein SequenceProtein SequenceProtein Sequence

Folded ProteinFolded ProteinFolded ProteinFolded Protein

Transcription

Translation

Folding

61

FeaturesFeatures

Stochastic (vs. deterministic) Massively parallel (vs. sequential) Self-assembly (vs. programming) Liquid rather than solid-state Biochemical (vs. electronic) Biomolecule-based (vs. silicon-based)

62

Principles and Theoretical ToolsPrinciples and Theoretical Toolsfor Biointelligence Researchfor Biointelligence Research

Self-Assembly Self-Reproduction

Uncertainty Principle Occam’s Razor Principle

Information Theory Probability Theory Thermodynamics Statistical Physics

63

Biology-Based AI Models: Biology-Based AI Models: Existing ExamplesExisting Examples

Evolutionary ComputationEvolutionary Computation:

computational method

simulating natural selection

DNA ComputingDNA Computing: information pro

cessing based on biomolecules

Neural NetworksNeural Networks: computation

model imitating brain structure

64

Neural ComputationNeural Computation: The Brain : The Brain as Computeras Computer

1. 1011 neurons with 1014 synapses2. 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

65

From Biological Neurons to From Biological Neurons to Artificial NeuronsArtificial Neurons

66

“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 ComputationEvolutionary Computation: : Nature as ComputerNature as Computer

67

Variation and Selection: The Variation and Selection: The Principle Principle

solutions

1100101010101110111000110110011100110001

1100101110

10111011101100101010

crossovercrossover

mutationmutation

00110

1011101010

10011

00110 10010

evaluationevaluation

110010111010111010100011001001

solutions

fitnesscomputation

roulettewheel

selectionselectionnew

population

encoding

chromosomes

68

DNA ComputingDNA Computing: BioMolecules a: BioMolecules as Computers Computer

011001101010001 ATGCTCGAAGCT

69

HPPHPP

...

......

...ATGATG

ACGACG

TGCTGC

CGACGA

TAATAA

GCAGCA

CGTCGT...

...

...

...... ...

...

...

10

3

2 5

6

4

SolutionSolution

ATGTGCTAACGAACG

ACGCGAGCATAAATGTGCCGTACGCGAGCATAAATGTGCCGT

TAAACG

CGACGT

TAAACGGCAACG

...

...

...

...

CGACGTAGCCGT

...

...

...

ACGCGAGCATAAATGTGCCGTACGCGAGCATAAATGTGCCGTACGCGTAGCCGT

ACGCGT

......

...

...

...

ACGGCATAAATGTGCACGCGTACGCGAGCATAAATGCGATGCCGT

ACGCGAGCATAAATGTGCCGTACGCGAGCATAAATGTGCCGT

...... ......

...

ACGCGAGCATAAATGTGCCGTACGCGAGCATAAATGTGCCGT

...

.........

...

Decoding

Ligation

Encoding

Gel Electrophoresis

Affinity Column

ACGCGAGCATAAATGTGCACGCGT

ACGCGAGCATAAATGCGATGCACGCGT

ACGCGAGCATAAATGTGCACGCGT

ACGCGAGCATAAATGCGATGCACGCGT

2

0 13 4

56

Node 0: ACG Node 3: TAANode 0: ACG Node 3: TAANode 1: CGA Node 4: ATGNode 1: CGA Node 4: ATGNode 2: GCA Node 5: TGCNode 2: GCA Node 5: TGC

Node 6: CGTNode 6: CGT

Flow of DNA ComputingFlow of DNA Computing

PCR(Polymerase

Chain Reaction)

70

Biointelligence TechnologyBiointelligence Technology

71

Biointelligence on a Chip?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

72

Intelligent Biomolecular InformatioIntelligent Biomolecular Information Processingn Processing

Bio-Memory Biocomputing

Theoretical Models

S

GFP

Cytochrome c

S

GFP

Cytochrome c

Bio-Processor

Input AInput AController

OutputReaction Chamber

(Calculating)

73

분자 컴퓨터 모델분자 컴퓨터 모델

Bio-diode 소자Bio-diode 소자• 단일 전자 소자• Bio-transistor 구성• Bio-memory

• 단일 전자 소자• Bio-transistor 구성• Bio-memory

Bio-logic gate 소자Bio-logic gate 소자• 단일 전자 소자• 직렬 processor• Thz 급 처리속도

• 단일 전자 소자• 직렬 processor• Thz 급 처리속도

One-chip 적용

분자 연산 소자분자 연산 소자• 병렬 processor• Thz 급 처리속도 (CPU)

• 병렬 processor• Thz 급 처리속도 (CPU)

74

Evolvable Biomolecular HardwarEvolvable Biomolecular Hardwaree

Sequence programmable and evolvable molecular systems have been constructed as cell-free chemical systems using biomolecules such as DNA and proteins.

75

Molecular Storage for Massively Molecular Storage for Massively Parallel Information RetrievalParallel Information Retrieval

Trillions of DNA

성 명 전화번호 주 소홍길동 419-1332 서울 송파구 잠실본동 211

송승헌 352-4730 인천시 남구 주안 5 동 23-1

원 빈 648-7921 경기도 구리시 아천동 246-2

송혜교 418-9362 서울시 영등포구 신길 2 동 11

…

전화번호부

76

The ‘Knight Problem’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

77

Three Solutions to the ‘Knight Three Solutions to the ‘Knight Problem’Problem’

Problem solved: 3 of the 31 solutions to the knight conundrum found by the RNA-based machine

78

Solving Logic Problems by Solving Logic Problems by Molecular ComputingMolecular Computing Satisfiability Problem

Find Boolean values for variables that make the given formula true

3-SAT Problem Every NP problems can be see

n 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

xxxxxx

xxxxxx

)()()( 324431 xxxxxx

79

DNA Chips for DNA ComputingDNA 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 stran

d 제거

VI. Readout: N cycle 의 마지막 단계에 해가 남게

되 면 , PCR 로 증폭하여 확인 !

80

Variable Sequences and the Variable Sequences and the Encoding SchemeEncoding Scheme

81

Tree-dimensional Plot and Tree-dimensional Plot and Histogram of the FluorescenceHistogram 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.

82

Applied BiointelligenceApplied Biointelligence

Bio-based AI Methods for Solving Bio-problemsBio-based AI Methods for Solving Bio-problems

83

Spillover of BiointelligenceSpillover of Biointelligence

Understanding information flow in biological construction

HealthcareHealthcareDrugsDrugs FoodsFoods

Analysis, modeling and management tools

84

Multilayer Perceptrons for Gene Multilayer Perceptrons for Gene Finding and PredictionFinding and Prediction

Coding potential valueCoding potential value

GC CompositionGC Composition

LengthLength

DonorDonor

AcceptorAcceptor

Intron vocabularyIntron vocabulary

basesDiscrete

exon score

0

1

sequence

score

85

Self-Organizing Maps for DNA MiSelf-Organizing Maps for DNA Microarray Data Analysiscroarray Data Analysis

Two-dimensional arrayof postsynaptic neurons

Bundle of synapticconnections

Winning neurons

Input

86

Biological Information ExtractionBiological Information ExtractionText Data

DB

LocationDate

DB Record

Database TemplateFilling

Data Analysis &Field Identify

Data Classify &Field Extraction

Information Extraction

Field PropertyIdentify & Learning

87

Medical BiointelligenceMedical Biointelligence

Automation of genome expressionanalysis

Integration ofmolecular data

Inference andmodeling systems

Molecular classification of cancer

Diagnosissystems

Organismmodeling

Drug design

Key aspects addressed Goal

88

E-DoctorE-Doctor

Diagnosis Expert System

Self-diagnosis

Pharmacy

Hospital

Personal Medicine

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BioroboticsBiorobotics

Robot = Mechanical + Electronic (+ Biological) Biorobot = Biological + (Mechanical + Electronic) Biological Robots with Biointelligence

Self-reproduction Evolution Learning

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ConclusionsConclusions

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 InformationFurther Information

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Journals & ConferencesJournals & 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: Web Resources: BioinformaticsBioinformatics

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: BiocomputingWeb 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: BiochipsWeb 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: listserv@listserv.ucsf.edu

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Books: BioinformaticsBooks: 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: BiocomputingBooks: 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:For more information:

http://scai.snu.ac.kr/http://scai.snu.ac.kr/