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-TakZhang School of Computer Science and Engineering Seoul National University E-mail: [email protected] http://scai.snu.ac.kr./~btzhang/
This material is available at http://scai.snu.ac.kr/~btzhang/
Outline
? Introduction
? BioinformationTechnology (BIT) = BT + IT ? Bioinformatics, Biocomputing, Biochips
? Biointelligence = BT + AI ? Concept, Methodology, Technology
? Applied Biointelligence
? Summary
? Further Information
2
1 Introduction
3
Biotechnology Revolution Economical Value
Biotechnology Age
Information Age
Industrial Age Agricultural Age
BC 6000 AD 1760 1950 2000
Year 4
2 Human Genome Project
A New Disease Encyclopedia
New Genetic Genome Fingerprints Goals Health •Identify the approximate 100,000 genes Implications in human DNA New •Determine the sequences of the 3 billion Diagnostics bases that make up human DNA •Store this information in database New •Develop tools for data analysis Treatments •Address the ethical, legal and social issues that arise from genome research 5
Bioinformation Technology (BIT) = BT + IT
In silico Biology (e.g. Bioinformatics)
IT BT
In vivo Informatics (e.g. Biocomputing)
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3 Bioinformation Technology Bioinformatics Biocomputing Biochips
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Bioinformatics
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4 What is Bioinformatics?
Bio – molecular biology Informatics – computer science
Bioinformatics – solving problems arising from biology using methodology from computer science.
? Bioinformatics vs. Computationl Biology ? Bioinformatik (in German): Biology-based computer science as well as bioinformatics (in English)
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What is DNA?
AACCTGCGGAAGGATCATTACCGAGTGCGGGTCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCGTTGCTTCG GCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCTCTGCCCCCCGGGCCCGTGCCCGCCGGA GACCCCAACAC GAACACTGTCTGAAAGCGTGCAGTCTGAGTTGATTGAATGCAATCAGTTAAAACTTTCAACAATGGATCTCTTGGTTCCGG CATGCAATCAGTCCCGTTGCTTCGGCACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCG TTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCG CCGGGGCTATTGTACCCGTTGCTTCGGATCTCTTGGGGATCTCTTGGTTCCGGCATGCAATCAGTCCCGTTGCTTCGGC ACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCACCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGG CGGCCGCCGGGGGCACTGTCTGAAAGCTCGGCCGCC 10
5 The Structure of DNA
Sugar-phosphate backbone
Hydrogen Base bonds
? 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 A C T GG
Leu Ala A
A Ser
G PheCysLysCysAspCysArg C DNA T T Protein A T C
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6 DNA (gene) RNA Protein
control TATA Termination control statement start stop statement
gene
Ribosome binding Transcription (RNA polymerase)
5’ utr mRNA 3’ utr
Transcription (Ribosome)
Protein
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Nucleotide and Protein Sequence
DNA (Nucleotide) Sequence
SQ sequence 1344 BP; 291 A; C; 401 G; 278 T; 0 other aacctgcgga aggatcatta gcgggcccgc cgcttgtcgg cgcttgtcgg ccgccggggg Protein (Amino Acid) Sequence ccgagtgcgg gtcctttggg ccgccggggg ggcgcctctg ccccccgggc ccgtgcccgc cccaacctcc catccgtgtc ccccccgggc ccgtgcccgc cggagacccc aacacgaaca tattgtaccc tgttgcttcg aacctgcgga aggatcatta ctgtctgaaa gcgtgcagtc CG2B_MARGL Length: 388 April 2, 1997 14:55 Type: P Check: gcgggcccgc cgcttgtcgg ccgagtgcgg gtcctttggg tgagttgatt gaatgcaatc 9613 .. 1 ccgccggggg ggcgcctctg cccaacctcc catccgtgtc agttaaaact ttcaacaatg MLNGENVDSR IMGKVATRAS SKGVKSTLGT RGALENISNV ccccccgggc ccgtgcccgc tattgtaccc tgttgcttcg gatctcttgg aacctgcgga ARNNLQAGAK KELVKAKRGM TKSKATSSLQ SVMGLNVEPM cggagacccc aacacgaaca gcgggcccgc cgcttgtcgg ccgagtgcgg gtcctttggg EKAKPQSPEP MDMSEINSAL EAFSQNLLEG VEDIDKNDFD ctgtctgaaa gcgtgcagtc agttaaaact ttcaacaatg cccaacctcc catccgtgtc NPQLCSEFVN DIYQYMRKLE REFKVRTDYM TIQEITERMR tgagttgatt gaatgcaatc gatctcttgg ttccggctgc tattgtaccc tgttgcttcg SILIDWLVQV HLRFHLLQET LFLTIQILDR YLEVQPVSKN agttaaaact ttcaacaatg tattgtaccc tgttgcttcg gcgggcccgc cgcttgtcgg KLQLVGVTSM LIAAKYEEMY PPEIGDFVYI TDNAYTKAQI gatctcttgg ttccggctgc gcgggcccgc cgcttgtcgg ccgccggggg ggcgcctctg RSMECNILRR LDFSLGKPLC IHFLRRNSKA GGVDGQKHTM tattgtaccc tgttgcttcg ccgccggggg ggcgcctctg agttaaaact ttcaacaatg AKYLMELTLP EYAFVPYDPS EIAAAALCLS SKILEPDMEW gcgggcccgc cgcttgtcgg ccccccgggc ccgtgcccgc gatctcttgg ttccggctgc GTTLVHYSAY SEDHLMPIVQ KMALVLKNAP TAKFQAVRKK YSSAKFMNVS TISALTSSTV MDLADQMC ccgccggggg ggcgcctctg cggagacccc tgttgcttcg tattgtaccc tgttgcttcg ccccccgggc ccgtgcccgc gcgggcccgc cgcttgtcgg gcgggcccgc cgcttgtcgg cggagacccc tgttgcttcg ccgccggggg cggagacccc ccgccggggg ggcgcctctg gcgggcccgc cgcttgtcgg gcgggcccgc cgcttgtcgg ccccccgggc ccgtgcccgc ccgccggggg cggagacccc ccgccggggg ggcgcctctg cggagacccc tgttgcttcg
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7 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
10
9 total number of records (millions) 8
7 millions of records 6
5
4
3
2
1
0 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 year 16
8 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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9 Extension of Bioinformatics Concept ? 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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10 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
Sequence analysis ? Sequence alignment ? Structure and function prediction ? Gene finding Structure analysis ? Protein structure comparison ? Protein structure prediction ? RNA structure modeling
Expression analysis ? Gen expression analysis ? Gene clustering
Pathway analysis ? Metabolic pathway ? Regulatory networks 22
11 The Complete Microarray Bioinformatics Solution
Databases
Data Cluster Management Analysis
Statistical Data Analysis Mining
Image Automation Processing
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Bioinformatics as Information Technology
GenBank SWISS-PROT Database
Information Hardware Retrieval Supercomputing
Biomedical text analysis Bioinformatics
Algorithm Agent Information filtering Monitoring agent Sequence alignment Machine
Learning Clustering Rule discovery Pattern recognition 24
12 Bioinformatics on the Web
The experimental process sample hybridization array scanner
Data management
relational database
web interface
image analysis results and links to other download summaries information data to other resources applications Data analysis and interpretation 25
Biocomputing
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13 Biocomputing vs. Bioinformatics
Bioinformatics
IT BT
Biocomputing
27
Traveling Salesman Problem
The traveling salesman problem: as 3 4 the number of cities grows, even 1 supercomputers have difficulty 0 finding the shortest path. 6
2 5
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14 Adleman’s Molecular Computer: A Brute Force Method
Each city (vertex) is represented by a different sequence of nucleotides (6 here, but Adleman used 20).
A DNA linker (edge) joining two city (vertex) strands.
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Vertex 1 Vertex 2 AGCTTAGG ATGGCATG
ATCCTACC 32 bp 16 bp Edge 1? 2 Step 4 : Gel Electrophoresis AGCTTAGG ATGGCATG ATCC TACC Step 1 : Hybridization AGCTTAGGATGGCATGGAATCCGA… AGCTTAGG ATGGCATG TCGAATCC ATCCTACC Bead for vertex 1 Step 2 : Ligation Step 5 : Magnetic Bead Vertex 1 Vertex 4 Affinity Separation AGCTTAGGATGGCATGGAATCCGATGCATGGC TCGAATCC ACGTACCG Step 3 : PCR
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15 Molecular Operators for DNA Computing
•Hybridization: complementary pairing of two single- stranded polynucleotides
5’- AGCATCCA –3’ 5’- AGCATCCA –3’ + 3’- TGCTAGGT –5’ 3’- TCGTAGGT –5’
•Ligation: attaching sticky ends to a blunt-ended molecule
ATGCATGC ATGCATGCTGAC + TGAC TACG TACGACTG TACGTACGTGAC
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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16 Why DNA Computing?
? 6.022 ? 1023 molecules / mole ? Immense, Brute Force Search of All Possibilities ? Desktop: 109 operations / sec ? Supercomputer: 1012 operations / sec ? 1 ? molof DNA: 1026 reactions ? Favorable Energetics: Gibb’s Free Energy ?G ? ?8kcal mol-1 ? 1 J for 2 ? 1019 operations ? Storage Capacity: 1 bit per cubic nanometer
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DNA Computers vs. Conventional Computers
DNA-based computers Microchip-based computers slow at individual operations fast at individual operations can do billions of operations can do substantially fewer simultaneously operations simultaneously can provide huge memory in small smaller memory space setting up a problem may involve setting up only requires keyboard considerable preparations input DNA is sensitive to chemical electronic data are vulnerable but deterioration can be backed up easily
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17 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 Biomolecular Computing ? 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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18 Biochips
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DNA Chip
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19 DNA Chip Technology
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Classification of DNA Chip Technology Photolithography
Mechanical micro-spotting
Inkjetting
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20 How DNA Chips Are Made
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Photolithography Chip
Light.-directed Oligonucleotide Synthesis
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21 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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22 Protein Chips
? A new paradigm in protein molecular mapping strategies
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Bioelectronic Devices
Bio-Memory Device
Electron Sensitizer Patterned Bio-Film
GFP
Electron Acceptor Cyt c
Au Coated Glass Au
Glass
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23 History of Lab-on-a-Chip
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Lab-on-a-chip Technology
Integrates sample handling, separation and detection and data analysis for: DNA, RNA and protein solutions using LabChip technology.
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24 Biointelligence
Concept and History Methodology Technology Applications
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Concept and History
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25 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
Bioinformation Technology (BIT) AI Biotechnology Information (BT) Technology (IT) Biointelligence (BI)
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26 Related Research Fields
Artificial Intelligence
Bioinformatics Biointelligence Biocomputing
Biochips Bioinformation Technology
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Biological AI: History
Symbolic AI Biological AI
• 1943: Production rules • 1943: McCulloch-Pitt’s neurons • 1956: “Artificial Intelligence” • 1959: Perceptron • 1958: LISP AI language • 1965: Cybernetics • 1965: Resolution theorem • 1966: Simulated evolution proving • 1966: Self-reproducing automata
• 1970: PROLOG language • 1975: Genetic algorithm • 1971: STRIPS planner • 1973: MYCIN expert system • 1982: Neural networks • 1982-92: Fifth generation • 1986: Connectionism computer systems project • 1987: Artificial life • 1986: Society of mind • 1992: Genetic programming • 1994: Intelligent agents • 1994: DNA computing 54
27 Paradigm Shift in AI Research
?Symbolic Subsymbolic ?Deep-thought Reactive behavior Learning ?Knowledge ?Individual Collective -based -based ?Deduction Induction ?Syntactic Semantic
?Model-driven Data-driven ?Discrete Continuous ?Top-down Bottom-up ?Deterministic Stochastic ?High-level Low-level ?Logic Probabilistic ?Reflective Reflexive
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Computers and Biosystems
(Moravec, 1988) 56
28 Biointelligence Methodology
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Four Levels of Biointelligence
Molecular Intelligence
Cellular Intelligence
Organismic Intelligence <= Focus of classical AI
Ecological Intelligence
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29 Comparison of Biointelligence Technologies
Molecular Cellular Organismic Ecological Intelligence Intelligence Intelligence Intelligence Basic unit molecules cells organism population Biology Molecular cell biology neurobiology ecology biology Phenomenon self-assembly development learning evolution
Time (typical) seconds days months years Communication lock-key electrochemical neuro- audiovisual, mechanism signals transmitters symbolic Basic operation ligation cell division excitation cooperation hybridization differentiation inhibition competition Computational DNA/molecular cell-automata neural nets evolutionary models computing immune nets semantic nets algorithms Chips DNA chips embryonic chips neurochips evolvable protein chips lab-on-a-chip hardware
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Biomolecular Information Processing
DNA Sequence Transcription mRNA Sequence Translation Protein Sequence Folding Folded Protein
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30 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 Tools for Biointelligence Research
? Self-Assembly ? Self-Reproduction ? Uncertainty Principle ? Occam’s Razor Principle
? Information Theory ? Probability Theory ? Thermodynamics ? Statistical Physics
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31 Biology-Based AI Models: Existing Examples
Neural Networks: computation model imitating brain structure
Evolutionary Computation: computational method simulating natural selection
DNA Computing: information processing based on biomolecules
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Neural Computation: The Brain as Computer
1. 1011 neurons with 1. A single processor with 1014 synapses complex circuits 2. Speed: 10-3 sec 2. Speed: 10 –9 sec 3. Distributed processing 3. Central processing 4. Nonlinear processing 4. Arithmetic operation (linearity) 5. Parallel processing 5. Sequential processing
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32 From Biological Neurons to Artificial Neurons
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Evolutionary Computation: Nature as Computer “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)”
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33 Variation and Selection: The Principle crossover
110010 1010 101110 1110 chromosomes encoding 1100101010 1011101110 110010 1110 solutions 0011011001 101110 1010 1100110001 mutation
00110 1 1001 new selection evaluation population 00110 0 1001 1100101110 1011101010 0011001001 roulette wheel solutions
fitness computation 67
DNA Computing: BioMolecules as Computer
011001101010001 ATGCTCGAAGCT 68
34 Flow of DNA Computing Encoding Node 0: ACG Node 3: TAA Node 1: CGA Node 4: ATG HPP Node 2: GCA Node 5: TGC Node 6: CGT ... TAAACG ... 4 Ligation ... 3 ATG ...... ATGTGCTAACGAACG CGA ACGCGAGCATAAATGTGCACGCGT 0 1 ACG GCA...... TAAACGGCAACG TAA... ACGCGAGCATAAATGTGCCGT CGT TGC... 6 ...... ACGCGAGCATAAATGCGATGCACGCGT ...... 2 5 CGACGTAGCCGT... CGACGT ...... Gel Electrophoresis PCR ACGCGAGCATAAATGTGCCGT ACGGCATAAATGTGCACGCGT (Polymerase Solution ACGCGAGCATAAATGCGATGCCGT Chain Reaction) Decoding 3 4 1 Affinity Column ... ACGCGTAGCCGT ACGCGAGCATAAATGTGCCGT 0 ...... ACGCGAGCATAAATGTGCACGCGT... 6 ...... ACGCGAGCATAAATGTGCCGT...... ACGCGT 2 5 ... ACGCGAGCATAAATGCGATGCACGCGT 69
Biointelligence Technology
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35 Biointelligence on a Chip?
Bioinformation Biological Technology Computer Information Technology Biointelligence Chip Computing Models: The limit of conventional computing models Molecular Computing Devices: Biotechnology Electronics The limit of silicone semiconductor technology
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Intelligent Biomolecular Information Processing
Theoretical Models
InputInput A AController GFP
Cytochrome c Reaction Output Chamber (Calculating) S
Bio-Memory Bio-Processor Biocomputing
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36 분자 컴퓨터 모델
분자 연산 소자
• 병렬 processor • Thz급 처리속도 One-chip 적용 (CPU)
Bio-logic gate 소자 • 단일 전자 소자 • 직렬 processor • Thz급 처리속도 Bio-diode 소자 • 단일 전자 소자 • Bio -transistor 구성 • Bio -memory
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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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37 Molecular Storage for Massively Parallel Information Retrieval
Trillions of DNA
성 명 전화번호 주 소 홍길동 419-1332 서울송파구 잠실본동 211 송승헌 352-4730 인천시 남구 주안5동 23-1 전화번호부 원 빈 648-7921 경기도 구리시 아천동 246-2 …
송혜교 418-9362 서울시 영등포구신길 2동 11
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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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38 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 (x ? x ? x ) ? (x ) ? (x ? x ) variables that make the given 1 3 4 4 2 3 formula true (x or x or x ) AND (x or x or x ) ? 3-SAT Problem 1 2 3 4 5 6 (x or x or x ) AND (x or x or x ) ? Every NP problems can be 1 2 3 1 2 3 seen as the search for a solution that simultaneously satisfies a number of logical clauses, each composed of three variables.
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39 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 되 면, PCR로 증폭하여 확인!
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Variable Sequences and the Encoding Scheme
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40 Tree-dimensional Plot and Histogram of the Fluorescence
? S 3: w=0, x=0, y=1, z=1
? S 7: w=0, x=1, y=1, z=1
? S 8: w=1, x=0, y=0, z=0
? S 9 : 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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41 Spillover of Biointelligence
Drugs Foods Healthcare
Analysis, modeling and management tools
Understanding information flow in biological construction
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Multilayer Perceptrons for Gene Finding and Prediction
Coding potential value GC Composition Length Discrete bases Donor exon score Acceptor Intron vocabulary
1 score
0 sequence 84
42 Self-Organizing Maps for DNA Microarray Data Analysis
Two-dimensional array of postsynaptic neurons
Winning Bundle of synaptic neurons connections
Input 85
Biological Information Extraction
Text Data Data Analysis & Data Classify & Field Identify Field Extraction
Field Property Identify & Learning
Database Template Filling DB Record Location Date
Information Extraction
DB 86
43 Medical Biointelligence
Key aspects addressed Goal
Automation of Molecular genome expression classification analysis of cancer
Diagnosis Integration of systems molecular data Drug design
Inference and Organism modeling systems modeling
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E-Doctor
Hospital
Diagnosis Expert System
Personal Medicine Pharmacy Self-diagnosis 88
44 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) ? Bioinformationtechnology (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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45 “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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46 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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47 Web Resources: Biocomputing
? European Molecular Computing Consortium (EMCC): http://www.csc.liv.ac.uk/~emcc/ ? BioMolecular Information Processing (BioMip): http://www.gmd.de/BIOMIP ? LeidenCenter 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/DNAcomputi ng.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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48 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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49 For more information:
http://scai.snu.ac.kr/
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50