Transcript PowerPoint 프레젠테이션

바이오정보기술(BIT)과
바이오지능(Biointelligence)
장병탁
서울대 컴퓨터공학부
E-mail: [email protected]
http://scai.snu.ac.kr./~btzhang/
Byoung-Tak Zhang
School of Computer Science and Engineering
Seoul National University
Outline

Introduction

Bioinformation Technology (BIT) = BT + IT
 Bioinformatics, Biocomputing, Biochips

Biointelligence = BT + AI
 Concept, Methodology, Technology

Applied Biointelligence

Summary

Further Information
2
Introduction
3
Biotechnology Revolution
Economical Value
Biotechnology Age
Information Age
Industrial Age
Agricultural Age
BC 6000
AD 1760
1950
2000
Year
4
Human Genome Project
A New
Disease
Encyclopedia
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
Genome
Health
Implications
New Genetic
Fingerprints
New
Diagnostics
New
Treatments
5
Bioinformation Technology (BIT)
= BT + IT
In silico Biology (e.g. Bioinformatics)
IT
BT
In vivo Informatics (e.g. Biocomputing)
6
Bioinformation Technology
Bioinformatics
Biocomputing
Biochips
7
Bioinformatics
8
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)
9
What is DNA?
AACCTGCGGAAGGATCATTACCGAGTGCGGGTCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCGTTGCTTCG
GCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCTCTGCCCCCCGGGCCCGTGCCCGCCGGAGACCCCAACAC
GAACACTGTCTGAAAGCGTGCAGTCTGAGTTGATTGAATGCAATCAGTTAAAACTTTCAACAATGGATCTCTTGGTTCCGG
CATGCAATCAGTCCCGTTGCTTCGGCACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCATCCGTGTCTATTGTACCCG
TTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCG
CCGGGGCTATTGTACCCGTTGCTTCGGATCTCTTGGGGATCTCTTGGTTCCGGCATGCAATCAGTCCCGTTGCTTCGGC
ACTGTCTGAAAGCGCCTTTGGGCCCAACCTCCCACCGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGG
CGGCCGCCGGGGGCACTGTCTGAAAGCTCGGCCGCC
10
The Structure of DNA
Sugar-phosphate
backbone
Base
Hydrogen
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
11
Molecular Biology: Flow of
Information
DNA
RNA
Protein
Function
ACTGG
AAGCT
T
A
TC
DNA
Phe Cys
Cys
Protein
12
DNA (gene)
control
statement
RNA
TATA
start
Protein
Termination
stop
control
statement
gene
Ribosome
binding
5’ utr
Transcription (RNA polymerase)
mRNA
3’ utr
Transcription (Ribosome)
Protein
13
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
ccgagtgcgg gtcctttggg ccgccggggg ggcgcctctg ccccccgggc
cccaacctcc catccgtgtc ccccccgggc ccgtgcccgc cggagacccc
tattgtaccc tgttgcttcg aacctgcgga aggatcatta ctgtctgaaa
gcgggcccgc cgcttgtcgg ccgagtgcgg gtcctttggg tgagttgatt
ccgccggggg ggcgcctctg cccaacctcc catccgtgtc agttaaaact
ccccccgggc ccgtgcccgc tattgtaccc tgttgcttcg gatctcttgg
cggagacccc aacacgaaca gcgggcccgc cgcttgtcgg ccgagtgcgg
ctgtctgaaa gcgtgcagtc agttaaaact ttcaacaatg cccaacctcc
tgagttgatt gaatgcaatc gatctcttgg ttccggctgc tattgtaccc
agttaaaact ttcaacaatg tattgtaccc tgttgcttcg gcgggcccgc
gatctcttgg ttccggctgc gcgggcccgc cgcttgtcgg ccgccggggg
tattgtaccc tgttgcttcg ccgccggggg ggcgcctctg agttaaaact
gcgggcccgc cgcttgtcgg ccccccgggc ccgtgcccgc gatctcttgg
ccgccggggg ggcgcctctg cggagacccc tgttgcttcg tattgtaccc
ccccccgggc ccgtgcccgc gcgggcccgc cgcttgtcgg gcgggcccgc
cggagacccc tgttgcttcg ccgccggggg cggagacccc ccgccggggg
gcgggcccgc cgcttgtcgg gcgggcccgc cgcttgtcgg ccccccgggc
ccgccggggg cggagacccc ccgccggggg ggcgcctctg cggagacccc
ccgccggggg
ccgtgcccgc
aacacgaaca
gcgtgcagtc
gaatgcaatc
ttcaacaatg
aacctgcgga
gtcctttggg
catccgtgtc
tgttgcttcg
cgcttgtcgg
ggcgcctctg
ttcaacaatg
ttccggctgc
tgttgcttcg
cgcttgtcgg
ggcgcctctg
ccgtgcccgc
tgttgcttcg
Protein (Amino Acid) Sequence
CG2B_MARGL Length: 388 April 2, 1997 14:55 Type: P Check:
9613 .. 1
MLNGENVDSR
ARNNLQAGAK
EKAKPQSPEP
NPQLCSEFVN
SILIDWLVQV
KLQLVGVTSM
RSMECNILRR
AKYLMELTLP
GTTLVHYSAY
YSSAKFMNVS
IMGKVATRAS
KELVKAKRGM
MDMSEINSAL
DIYQYMRKLE
HLRFHLLQET
LIAAKYEEMY
LDFSLGKPLC
EYAFVPYDPS
SEDHLMPIVQ
TISALTSSTV
SKGVKSTLGT RGALENISNV
TKSKATSSLQ SVMGLNVEPM
EAFSQNLLEG VEDIDKNDFD
REFKVRTDYM TIQEITERMR
LFLTIQILDR YLEVQPVSKN
PPEIGDFVYI TDNAYTKAQI
IHFLRRNSKA GGVDGQKHTM
EIAAAALCLS SKILEPDMEW
KMALVLKNAP TAKFQAVRKK
MDLADQMC
14
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.
15
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
year
1994
1996
1998
2000
16
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.
17
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
18
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
19
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?
20
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
21
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
The Complete Microarray
Bioinformatics Solution
Databases
Data
Management
Cluster
Analysis
Statistical
Analysis
Data
Mining
Image
Processing
Automation
23
Bioinformatics as Information
Technology
GenBank
SWISS-PROT
Database
Information
Retrieval
Hardware
Supercomputing
Biomedical text analysis
Bioinformatics
Algorithm
Agent
Information filtering
Monitoring agent
Sequence alignment
Machine
Learning
Clustering
Rule discovery
Pattern recognition
24
Bioinformatics on the Web
The experimental process
sample
hybridization
array
scanner
Data management
relational
database
web
interface
image analysis
results and
summaries
links to other
information
resources
Data analysis and interpretation
download
data to other
applications
25
Biocomputing
26
Biocomputing vs. Bioinformatics
Bioinformatics
IT
BT
Biocomputing
27
Traveling Salesman Problem
The traveling salesman problem: as
the number of cities grows, even
supercomputers have difficulty
0
finding the shortest path.
4
3
1
6
2
5
28
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.
29
Vertex 1
AGCTTAGG
Vertex 2
ATGGCATG
ATCCTACC
32 bp
Edge 12
Step 4 : Gel Electrophoresis
AGCTTAGG
ATGGCATG
ATCC
TACC
Step 1 : Hybridization
AGCTTAGG ATGGCATG
ATCCTACC
Step 2 : Ligation
Vertex 1
16 bp
AGCTTAGGATGGCATGGAATCCGA…
TCGAATCC
Bead for vertex 1
Vertex 4
AGCTTAGGATGGCATGGAATCCGATGCATGGC
TCGAATCC
ACGTACCG
Step 3 : PCR
Step 5 : Magnetic Bead
Affinity Separation
30
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
ATGCATGC
TACG
+
TGAC
TACGACTG
to a blunt-ended molecule
ATGCATGCTGAC
TACGTACGTGAC
sticky end
31
DNA finds a solution!
A Hamiltonian path with all vertices included is
isolated and recovered
32
Why DNA Computing?
6.022  1023 molecules / mole
 Immense, Brute Force Search of All Possibilities

Desktop: 109 operations / sec
Supercomputer: 1012 operations / sec
1 mmol of 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
33
DNA Computers vs. Conventional
Computers
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
34
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)
35
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
36
Biochips
37
DNA Chip
38
DNA Chip Technology
39
Classification of DNA Chip
Technology
Photolithography
Mechanical micro-spotting
Inkjetting
40
How DNA Chips Are Made
41
Photolithography Chip
.
Light-directed
Oligonucleotide Synthesis
42
Microarray Robot
43
DNA Chip Applications

Gene discovery: gene/mutated gene
Growth, behavior, homeostasis …

Disease diagnosis
 Drug discovery: Pharmacogenomics
 Toxicological research: Toxicogenomics
44
Protein Chips

A new paradigm in protein molecular mapping
strategies
45
Bioelectronic Devices
Bio-Memory Device
Patterned Bio-Film
Electron Sensitizer
GFP
Cyt c
Electron Acceptor
Au Coated Glass
Au
Glass
46
History of Lab-on-a-Chip
47
Lab-on-a-chip Technology
Integrates sample handling,
separation and detection
and data analysis for: DNA,
RNA and protein solutions
using LabChip technology.
48
Biointelligence
Concept and History
Methodology
Technology
Applications
49
Concept and History
50
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)
51
Relationships to Existing
Research Areas
Bioinformation
Technology (BIT)
AI
Information
Technology
(IT)
Biotechnology
(BT)
Biointelligence
(BI)
52
Related Research Fields
Artificial Intelligence
Bioinformatics
Biointelligence
Biochips
Biocomputing
Bioinformation
Technology
53
Biological AI: History
Biological AI
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
•
•
•
•
•
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
54
Paradigm Shift in AI Research

Symbolic
Knowledge
-based
 Deduction

Subsymbolic

Deep-thought
Learning
-based

Individual
Reactive
behavior
Collective
Induction

Syntactic
Semantic

Model-driven
Data-driven

Discrete
Continuous

Top-down
Bottom-up

Deterministic
Stochastic

High-level
Low-level

Logic
Probabilistic

Reflective
Reflexive
55
Computers and Biosystems
(Moravec, 1988)
56
Biointelligence Methodology
57
Four Levels of Biointelligence
Molecular Intelligence
Cellular Intelligence
Organismic Intelligence
<= Focus of classical AI
Ecological Intelligence
58
Comparison of Biointelligence
Technologies
Molecular
Intelligence
Cellular
Intelligence
Organismic
Intelligence
Ecological
Intelligence
Basic unit
molecules
cells
organism
population
Biology
Molecular
biology
cell biology
neurobiology
ecology
Phenomenon
self-assembly
development
learning
evolution
Time (typical)
seconds
days
months
years
Communication
lock-key
mechanism
electrochemical
signals
neurotransmitters
audiovisual,
symbolic
Basic operation
ligation
hybridization
cell division
differentiation
excitation
inhibition
cooperation
competition
Computational
models
DNA/molecular
computing
cell-automata
immune nets
neural nets
semantic nets
evolutionary
algorithms
Chips
DNA chips
protein chips
embryonic chips
lab-on-a-chip
neurochips
evolvable
hardware
59
Biomolecular Information
Processing
DNA Sequence
Transcription
mRNA Sequence
Translation
Protein Sequence
Folding
Folded Protein
60
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)
61
Principles and Theoretical Tools
for Biointelligence Research

Self-Assembly
 Self-Reproduction
 Uncertainty Principle
 Occam’s Razor Principle

Information Theory
 Probability Theory
 Thermodynamics
 Statistical Physics
62
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
63
Neural Computation: The Brain
as Computer
1.
2.
3.
4.
5.
1011 neurons with
1014 synapses
Speed: 10-3 sec
Distributed processing
Nonlinear processing
Parallel processing
1.
2.
3.
4.
5.
A single processor with
complex circuits
Speed: 10 –9 sec
Central processing
Arithmetic operation (linearity)
Sequential processing
64
From Biological Neurons to
Artificial Neurons
65
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)”
66
Variation and Selection: The
Principle
crossover
chromosomes
encoding
solutions
1100101010
1011101110
0011011001
1100110001
110010 1010
101110 1110
110010 1110
101110 1010
mutation
00110 1 1001
new
population
selection
evaluation
1100101110
1011101010
0011001001
roulette
wheel
00110 0 1001
solutions
fitness
computation
67
DNA Computing: BioMolecules
as Computer
011001101010001
ATGCTCGAAGCT
68
Flow of DNA Computing
Encoding
HPP
Node 0: ACG Node 3: TAA
Node 1: CGA Node 4: ATG
Node 2: GCA Node 5: TGC
Node 6: CGT
4
3
ATG ...
... ...
CGA
ACG GCA
...
...
...
...
TAA
...
... ...
... CGT... TGC
1
0
6
2
...
Ligation
ACGCGAGCATAAATGTGCACGCGT
...
...
...
...
ACGCGAGCATAAATGCGATGCACGCGT
... CGACGTAGCCGT
...
CGACGT
...
ACGCGAGCATAAATGTGCCGT
ACGGCATAAATGTGCACGCGT
...
PCR
(Polymerase
Chain
Reaction)
Decoding
4
Affinity Column
1
0
5
... ACGCGTAGCCGT
ACGCGAGCATAAATGTGCCGT
6
2
Gel Electrophoresis
ACGCGAGCATAAATGCGATGCCGT
Solution
3
TAAACGGCAACG
ACGCGAGCATAAATGTGCCGT
5
...
TAAACG ...
...
ATGTGCTAACGAACG
...
...
...
ACGCGAGCATAAATGTGCACGCGT...
...
ACGCGAGCATAAATGTGCCGT
...
...
...
... ACGCGT
ACGCGAGCATAAATGCGATGCACGCGT
69
Biointelligence Technology
70
Biointelligence on a Chip?
Bioinformation
Technology
Biological
Computer
Information
Technology
Biointelligence
Chip
Computing Models:
The limit of conventional
computing models
Computing Devices:
The limit of silicone
semiconductor technology
Biotechnology
Molecular
Electronics
71
Intelligent Biomolecular
Information Processing
Theoretical Models
InputInput
A
A Controller
GFP
Cytochrome c
Output
Reaction
Chamber
(Calculating)
S
Bio-Memory
Bio-Processor
Biocomputing
72
분자 컴퓨터 모델
분자 연산 소자
• 병렬 processor
• Thz급 처리속도
(CPU)
One-chip 적용
Bio-logic gate 소자
• 단일 전자 소자
• 직렬 processor
• Thz급 처리속도
Bio-diode 소자
• 단일 전자 소자
• Bio-transistor 구성
• Bio-memory
73
Evolvable Biomolecular
Hardware

Sequence programmable and evolvable molecular systems have been
constructed as cell-free chemical systems using biomolecules such as
DNA and proteins.
74
Molecular Storage for Massively
Parallel Information Retrieval
Trillions of DNA
전화번호부
전화번호
주 소
홍길동
419-1332
서울 송파구 잠실본동 211
송승헌
352-4730
인천시 남구 주안5동 23-1
원 빈
648-7921
경기도 구리시 아천동 246-2
송혜교
418-9362
…
성 명
서울시 영등포구 신길 2동 11
75
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
76
Three Solutions to the ‘Knight
Problem’

Problem solved: 3 of the 31 solutions to the knight
conundrum found by the RNA-based machine
77
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.
( x1  x3  x4 )  ( x4 )  ( x2  x3 )
( x1 or x2 or x3 ) AND ( x4 or x5 or x6 )
( x1 or x2 or x3 ) AND ( x1 or x2 or x3 )
78
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!
79
Variable Sequences and the
Encoding Scheme
80
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)
z=1:
(w V y V z)
x=0 or y=1: (x V y)
w=0:
(w V y)
만족
만족
만족
만족

Four spots with high fluorescence
intensity correspond to the four
expected solutions.

DNA sequences identified in the
readout step via addressed array
hybridization.
81
Applied Biointelligence
Bio-based AI Methods for Solving Bio-problems
82
Spillover of Biointelligence
Drugs
Foods
Healthcare
Analysis, modeling and management tools
Understanding information flow in biological construction
83
Multilayer Perceptrons for Gene
Finding and Prediction
Coding potential value
GC Composition
bases
Length
Discrete
Donor
exon score
Acceptor
Intron vocabulary
1
score
0
sequence
84
Self-Organizing Maps for DNA
Microarray Data Analysis
Two-dimensional array
of postsynaptic neurons
Winning
neurons
Bundle of synaptic
connections
Input
85
Biological Information Extraction
Data Analysis &
Field Identify
Text Data
Data Classify &
Field Extraction
Field Property
Identify & Learning
Database Template
DB Record
Filling
Location
Date
Information Extraction
DB
86
Medical Biointelligence
Key aspects addressed
Automation of
genome expression
analysis
Integration of
molecular data
Goal
Molecular
classification
of cancer
Diagnosis
systems
Drug design
Inference and
modeling systems
Organism
modeling
87
E-Doctor
Hospital
Diagnosis Expert System
Personal Medicine
Pharmacy
Self-diagnosis
88
Biorobotics

Robot = Mechanical + Electronic (+ Biological)
 Biorobot = Biological + (Mechanical + Electronic)
 Biological Robots with Biointelligence
Self-reproduction
Evolution
Learning
89
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 nextgeneration 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.
90
“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
91
Further Information
92
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)
93
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/
94
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/DNAcomputi
ng.html
SNU Molecular Evolutionary Computing (MEC) Project:
http://scai.snu.ac.kr/Research/
95
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-achip.com/
Mailing List: [email protected]
96
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.
97
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.
98
For more information:
http://scai.snu.ac.kr/
99