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Molecular Nanobiointelligence Computers · 2015-11-24 · Molecular Nanobiointelligence Computers...
Transcript of Molecular Nanobiointelligence Computers · 2015-11-24 · Molecular Nanobiointelligence Computers...
Molecular Molecular NanobiointelligenceNanobiointelligence ComputersComputers
National Cancer Center, June 21, 2005National Cancer Center, June 21, 2005
Byoung-Tak Zhang
Center for Bioinformation Technology (CBIT) &
Biointelligence Laboratory
School of Computer Science and Engineering
Seoul National University
http://bi.snu.ac.kr/
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Humans and ComputersHumans and Computers
Silicon Computers
What Kind of
Computers?
Human Computers
The Entire Problem Space
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Mind, Brain, Cells, MoleculesMind, Brain, Cells, Molecules
Brain
Cell
Molecule
Mind
Mind
1011 cells
1010 mol.
∞ memory
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Molecular Mechanisms of Synaptic LearningMolecular Mechanisms of Synaptic Learning
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Two Faces of the Brain:Two Faces of the Brain: Electrical Waves Electrical Waves
or Chemical Particles?or Chemical Particles?
Brain as a network of
neurons and synapses
(a) Neuron-oriented cellular
view (“electrical” waves)
(b) Synapse-oriented molecular view
(“chemical” particles)
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Large Numbers CountLarge Numbers Count
� 3 x 109 DNA bases in the human genome
� 3.5 x 109 years since first living cells
� 4.5 x 109 years since origin of Earth
� 1.5 x 1010 years since origin of universe (Big Bang)
� 1011 neurons in the human brain
“1014 synapses/brain”� 1014 cells in the human body
� 3 x 1023 DNA bases in the human body
or 1014 copies of 3 x 109 bases
� 6 x 1023molecules/mole or
“> 1014 molecules/nanomole”
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Levels of ComputationLevels of Computation
� Mind = y(Symbols) “Symbolic”
� Mind = f(Brain)
= f(g(Cells)) “Connectionist”
= f(g(h(Molecules)))
= y(Molecules) “Interactionist”
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Talk OutlineTalk Outline
� Why Nanobiointelligence Computers (NBIC)?
� Molecular Computing Technology for NBIC
� Biomedical Applications
� The Probabilistic Library Model (PLM)
� Future of NBIC
Molecular Computing for NBICMolecular Computing for NBIC
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/[Adleman, Science 1994; Scientific American 1998]
DNA Computation of Hamiltonian PathsDNA Computation of Hamiltonian Paths
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
DNA as Computing MaterialDNA as Computing Material
� 초고집적도:
♦ 106 Gbits per cm2 (1 bit per nm3)
♦ 반도체기술: 1 Gbits per cm2
� 초병렬탐색:
♦ 1026 reactions per 1 mmol of DNA
♦ Desktop: 109 operations / sec
♦ Supercomputer: 1012 operations / sec
� 에너지효율: 1019 operations per
Joule
♦ 반도체기술: 109 operations per Joule
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Characteristics of DNA MoleculesCharacteristics of DNA Molecules
Self-assembly
Heat
Cool
Polymer
Repeat
Self-replication
Molecular recognition
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
DNA Based ComputersDNA Based Computers
[Braich et al., Science 2002]
DNA Computer by Olympus
DNA Computer by Adleman’s Group
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Issues in DNA ComputingIssues in DNA Computing
� BT
♦ In Vivo Diagnosis
♦ Smart Drugs
♦ Therapeutics
� IT
♦DNA Processors
♦DNA Memory
♦DNA Electronics
� NT
♦ DNA Nanoassembly
♦ DNA Nanorobots
♦ DNA Motors
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
DNA as Smart DrugsDNA as Smart Drugs
[Benenson et al., Nature 2001 & Nature, 2004]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/[Braich et al., Science 2002]
Solving a 20Solving a 20--var 3var 3--CNF ProblemCNF Problem
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
DNA NanostructuresDNA Nanostructures
� Molecular Tweezers
� DNA nanostructure � Information
processing
methods
[Chen and Seeman, Nature 1991]
[Yurke et al., Nature 2000]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Molecular Memory and SelfMolecular Memory and Self--AssemblyAssembly
� DNA self-assembly as information processing utilizing
♦ Parallel-interaction
♦ Molecular recognition
♦ Self-organization
[Caltech and Duke]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
NanoparticleNanoparticle--Based Theorem Based Theorem Proving for Medical DiagnosisProving for Medical Diagnosis
I
II
Au Au
A B DNA linker
Au
AI
Au
B
∆∆∆∆
5’ ¬
QTS ¬P R
¬RPQ¬S ¬T
5’ 3’¬Q
TS R
¬RPQ¬S
R¬S 5’II 3’ 3’5’
3’
S-
Au
Au-SS-
Au
Au-S
S-
Au
Au-S
a
b
c
[Park et al., in preparation]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
DNA Computing Chip for Medical DiagnosisDNA Computing Chip for Medical Diagnosis
[Lee et al., in preparation]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Molecular Computers vs. Silicon ComputersMolecular Computers vs. Silicon Computers
DeterministicProbabilisticReproducibility
Very highUltrahighDensity
HighLowReliability
Ultra-fast (nanosec)Fast (millisec)Speed
SequentialMassively parallelParallelism
Fixed (synchronous)Amorphous (asynchronous)Configuration
Communication
Medium
Processing
2D switching3D collision
Solid (dry)Liquid (wet) or Gaseous (dry)
HardwiredBallistic
Silicon ComputersMolecular Computers
Diagnosis by DNADiagnosis by DNA--Based Based Theorem ProvingTheorem Proving
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Diagnosis SchemeDiagnosis Scheme
Gene expression data
Clustering
• Refine logical rules from clustered data
• Implement logical inference by DNA
computing
[Bittner et al., Nature, 406,
536-540, 2000]
[Zhang et al., Private discussion, 2004]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Diagnosis by DNA ComputingDiagnosis by DNA Computing
1. Transformation of Gene Expression
Information into DNA Signal
1. Transformation of Gene Expression
Information into DNA Signal
2. Autonomous Logical Inference
from DNA Signal
2. Autonomous Logical Inference
from DNA Signal
3. Detection of Inference Results3. Detection of Inference Results
OR
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Diagnosis by DNA Computing:Diagnosis by DNA Computing:Logical InferenceLogical Inference
If gene A is expressedA and gene C is expressedC, he (she) has a lung cancerL.
If gene H is expressedH, then gene A is expressedA.
In sample, gene HH and CC are expressed.
Does he (she) have a Lung cancerL?
?
, , ,
L
CHAHLCA →→∧
LCHAHLCA ¬∧∧∧∨¬∧∨¬∨¬ )( )(
Transform into CNF
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Diagnosis by DNA Computing:Diagnosis by DNA Computing:Logical InferenceLogical Inference
LCHAHLCA ¬∧∧∧∨¬∧∨¬∨¬ )( )(
Resolution
5’-GACTTGCAACGT-3’
5’-GTTA-3’
HGTTA
CACGT
¬LTGCA
¬A ¬C LGACT TGCA ACGT
¬H ACAAT CTGA
¬H ACAAT CTGA ¬C L
GACT TGCA ACGT
CACGT
HGTTA
¬LTGCA
GACT TGCA ACGT
CACGT
GTTA
¬LTGCA
¬H ACAAT CTGA
[Lee et al., Lecture Notes in Computer Science, 2003]
¬¬¬¬A ∨∨∨∨ ¬¬¬¬C ∨∨∨∨ L ¬¬¬¬H ∨∨∨∨ A H C ¬¬¬¬L
¬¬¬¬H ∨∨∨∨ ¬¬¬¬C ∨∨∨∨ L
¬¬¬¬C ∨∨∨∨ L
L
nil
¬¬¬¬A ¬¬¬¬C L
H
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Diagnosis by DNA Computing:Diagnosis by DNA Computing:DetectionDetection
� DNA-Based nanoparticle assembly strategy
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
(a) The 10-nm gold particles
(b) The solutions of DNA-linked full assembly
(c) Aqueous solution of the addition of NaCl
(a) (b) (c)
Color Change of Color Change of DNADNA--Induced AssemblyInduced Assembly
Destroyed by S1
[J.-Y. Park, Ph.D. Thesis, 2004]
The Probabilistic Library Model The Probabilistic Library Model (PLM)(PLM)
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Probabilistic Library Model (PLM)Probabilistic Library Model (PLM)
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
PLM (Probabilistic Library Model):PLM (Probabilistic Library Model):Learning Probability Distributions with DNALearning Probability Distributions with DNA
Library of combinatorialmolecules
+
Library Example
Select the library elements matching the example
Amplify the matched library elements by PCR
Next generation
i
i
Hybridize
[Zhang, DNAC-2004]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Application to Leukemia DiagnosisApplication to Leukemia Diagnosis
120 samples from
60 leukemia patients
Diagnosis
[Cheok et al., Nature Genetics, 2003]
Gene expression data
Training with
6-fold validation
Class: ALL/AML
&
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Initial Library Initial Library LL00
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
(x1=0, x2=0, x3=1, y=0)
(x1=0, x2=1, x3=1, y=1)
AAAACCAATTGGAATTGGATGCGG
(x2=1, y=0)
AATTGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
(x1=0, y=1)
AAAACCATGCGG
AAAACCATGCGG
AAAACCATGCGG
(x1=0, y=0)
AAAACCATGCCC
AAAACCATGCCC
AAAACCATGCCC
(x2=0, y=1)
AATTCCATGCGG
AATTCCATGCGG
AATTCCATGCGG
(x2=0, y=0)
AATTCCATGCCC
AATTCCATGCCC
AATTCCATGCCC
…
(x1=0, x2=0, y=0)
AAAACCAATTCCATGCCC
AAAACCAATTCCATGCCC
AAAACCAATTCCATGCCC
(x1=0, x2=0, y=1)
AAAACCAATTCCATGCGG
AAAACCAATTCCATGCGG
AAAACCAATTCCATGCGG
(x1=0, x2=1, y=0)
AAAACCAATTGGATGCCC
AAAACCAATTGGATGCCC
AAAACCAATTGGATGCCC
(x1=0, x2=1, y=1)
AAAACCAATTGGATGCGG
AAAACCAATTGGATGCGG
AAAACCAATTGGATGCGG
… (x1=0, x2=0, x3=0, y=0)
AAAACCAATTCCAAGGCCATGCCC
AAAACCAATTCCAAGGCCATGCCC
AAAACCAATTCCAAGGCCATGCCC
(x1=0, x2=0, x3=0, y=1)
AAAACCAATTCCAAGGCCATGCGG
AAAACCAATTCCAAGGCCATGCGG
AAAACCAATTCCAAGGCCATGCGG
(x1=0, x2=0, x3=1, y=0)
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
(x1=0, x2=0, x3=1, y=1)
AAAACCAATTCCAAGGGGATGCGG
AAAACCAATTCCAAGGGGATGCGG
AAAACCAATTCCAAGGGGATGCGG
(x1=0, x2=1, x3=0, y=0)
AAAACCAATTGGAAGGCCATGCCC
AAAACCAATTGGAAGGCCATGCCC
AAAACCAATTGGAAGGCCATGCCC
(x1=0, x2=1, x3=0, y=1)
AAAACCAATTGGAAGGCCATGCGG
AAAACCAATTGGAAGGCCATGCGG
AAAACCAATTGGAAGGCCATGCGG
…
x1
x2
x3
y
0
1
where
AAGG
AATT
AAAA ATGC
CC
GG
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
+
Amplify
Library Example 1
(x1=0, x2=1, x3=0, y=0)
TACGGGTTCCGGTTAACCTTTTGG
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTGGAATTGGATGCGG
AATTGGATGCCC
TTTTGG
TTTTGG
TTAACC
TTAACC
TTAACC
TTAACC
TTCCGG
GGTTGG
GGTTGG
GGTTGG
Hybridization
(x1=0, x2=1, x3=1, y=1)
(x1=0, x2=0, x3=1, y=0)
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
(x2=1, y=0)
TACGGGTTCCGGTTAACCTTTTGG
TACGGGTTCCGGTTAACCTTTTGG(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
(x1=0, x2=0, x3=1, y=0)
AAAACCAATTCCAAGGGGATGCCC
(x1=0, x2=1, x3=1, y=1)
AAAACCAATTGGAATTGGATGCGG
(x2=1, y=0)
AATTGGATGCCC
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Updated Library Updated Library LL11
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
(x1=0, x2=0, x3=1, y=0)
(x1=0, x2=1, x3=1, y=1)
AAAACCAATTGGAATTGGATGCGG
(x2=1, y=0)
AATTGGATGCCC
AATTGGAAGGCCATGCCC
AATTGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
(x1=0, y=1)
AAAACCATGCGG
AAAACCATGCGG
AAAACCATGCGG
(x1=0, y=0)
AAAACCATGCCC
AAAACCATGCCC
AAAACCATGCCC
(x2=0, y=1)
AATTCCATGCGG
AATTCCATGCGG
AATTCCATGCGG
(x2=0, y=0)
AATTCCATGCCC
AATTCCATGCCC
AATTCCATGCCC
…
(x1=0, x2=0, y=0)
AAAACCAATTCCATGCCC
AAAACCAATTCCATGCCC
AAAACCAATTCCATGCCC
(x1=0, x2=0, y=1)
AAAACCAATTCCATGCGG
AAAACCAATTCCATGCGG
AAAACCAATTCCATGCGG
(x1=0, x2=1, y=0)
AAAACCAATTGGATGCCC
AAAACCAATTGGATGCCC
AAAACCAATTGGATGCCC
(x1=0, x2=1, y=1)
AAAACCAATTGGATGCGG
AAAACCAATTGGATGCGG
AAAACCAATTGGATGCGG
… (x1=0, x2=0, x3=0, y=0)
AAAACCAATTCCAAGGCCATGCCC
AAAACCAATTCCAAGGCCATGCCC
AAAACCAATTCCAAGGCCATGCCC
(x1=0, x2=0, x3=0, y=1)
AAAACCAATTCCAAGGCCATGCGG
AAAACCAATTCCAAGGCCATGCGG
AAAACCAATTCCAAGGCCATGCGG
(x1=0, x2=0, x3=1, y=0)
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
(x1=0, x2=0, x3=1, y=1)
AAAACCAATTCCAAGGGGATGCGG
AAAACCAATTCCAAGGGGATGCGG
AAAACCAATTCCAAGGGGATGCGG
(x1=0, x2=1, x3=0, y=0)
AAAACCAATTGGAAGGCCATGCCC
AAAACCAATTGGAAGGCCATGCCC
AAAACCAATTGGAAGGCCATGCCC
(x1=0, x2=1, x3=0, y=1)
AAAACCAATTGGAAGGCCATGCGG
AAAACCAATTGGAAGGCCATGCGG
AAAACCAATTGGAAGGCCATGCGG
…
x1
x2
x3
y
0
1
where
AAGG
AATT
AAAA ATGC
CC
GG
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
+
Amplify
Library
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
(x1=0, x2=0, x3=1, y=0)
AAAACCAATTCCAAGGGGATGCCC
(x1=0, x2=1, x3=1, y=1)
AAAACCAATTGGAATTGGATGCGG
Example 2
(x1=0, x2=1, x3=1, y=1)
TTCCCCTTAACCTTTTGG TACGCC
(x2=1, y=0)
AATTGGATGCCC
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTGGAATTGGATGCGG
AATTGGATGCCC
TTTTGG
TTTTGG
TTAACC
TTAACC
TTAACC
TTAACC
Hybridization
(x1=0, x2=1, x3=1, y=1)
(x1=0, x2=0, x3=1, y=0)
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
(x2=1, y=0)
TACGCCTTCCCCTTAACCTTTTGG
TACGCCTTCCCCTTAACCTTTTGG
(x2=1, x3=0, y=0)
AATTGGAAGGCCATGCCC
(x2=1, y=0)
AATTGGATGCCC
TTCCCCTACGCC
TTCCCC
TTCCCCTACGCC
AATTGGAAGGCCATGCCC
AATTGGATGCCC
TTAACC
TTAACC
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Updated Library Updated Library LL22
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
(x1=0, x2=0, x3=1, y=0)
(x1=0, x2=1, x3=1, y=1)
AAAACCAATTGGAATTGGATGCGG
(x2=1, y=0)
AATTGGATGCCC
AATTGGAAGGCCATGCCC
AATTGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
(x1=0, y=1)
AAAACCATGCGG
AAAACCATGCGG
AAAACCATGCGG
(x1=0, y=0)
AAAACCATGCCC
AAAACCATGCCC
AAAACCATGCCC
(x2=0, y=1)
AATTCCATGCGG
AATTCCATGCGG
AATTCCATGCGG
(x2=0, y=0)
AATTCCATGCCC
AATTCCATGCCC
AATTCCATGCCC
…
(x1=0, x2=0, y=0)
AAAACCAATTCCATGCCC
AAAACCAATTCCATGCCC
AAAACCAATTCCATGCCC
(x1=0, x2=0, y=1)
AAAACCAATTCCATGCGG
AAAACCAATTCCATGCGG
AAAACCAATTCCATGCGG
(x1=0, x2=1, y=0)
AAAACCAATTGGATGCCC
AAAACCAATTGGATGCCC
AAAACCAATTGGATGCCC
(x1=0, x2=1, y=1)
AAAACCAATTGGATGCGG
AAAACCAATTGGATGCGG
AAAACCAATTGGATGCGG
… (x1=0, x2=0, x3=0, y=0)
AAAACCAATTCCAAGGCCATGCCC
AAAACCAATTCCAAGGCCATGCCC
AAAACCAATTCCAAGGCCATGCCC
(x1=0, x2=0, x3=0, y=1)
AAAACCAATTCCAAGGCCATGCGG
AAAACCAATTCCAAGGCCATGCGG
AAAACCAATTCCAAGGCCATGCGG
(x1=0, x2=0, x3=1, y=0)
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTCCAAGGGGATGCCC
(x1=0, x2=0, x3=1, y=1)
AAAACCAATTCCAAGGGGATGCGG
AAAACCAATTCCAAGGGGATGCGG
AAAACCAATTCCAAGGGGATGCGG
(x1=0, x2=1, x3=0, y=0)
AAAACCAATTGGAAGGCCATGCCC
AAAACCAATTGGAAGGCCATGCCC
AAAACCAATTGGAAGGCCATGCCC
(x1=0, x2=1, x3=0, y=1)
AAAACCAATTGGAAGGCCATGCGG
AAAACCAATTGGAAGGCCATGCGG
AAAACCAATTGGAAGGCCATGCGG
…
AAAACCAATTGGAATTGGATGCGG
AATTGGCCTTGGATGCGG
x1
x2
x3
y
0
1
where
AAGG
AATT
AAAA ATGC
CC
GG
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
+
Library
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
(x1=0, x2=0, x3=1, y=0)
AAAACCAATTCCAAGGGGATGCCC
(x1=0, x2=1, x3=1, y=1)
AAAACCAATTGGAATTGGATGCGG
Query
(x1=1, x2=1, x3=0)
TTCCGGTTAACCTTTTCC
(x2=1, y=0)
AATTGGATGCCC
Hybridization
TTCCGGTTAACCTTTTCC
TTAACCTTTTCC
AAAACCAATTGGAATTGGATGCGG
AATTGGCCTTGGATGCGG
AATTGGAAGGCCATGCCC
AATTGGATGCCC
TTCCGG
AATTGGAAGGCCATGCCC
AATTGGCCTTGGATGCGG
AAAACCAATTCCAAGGGGATGCCC
AAAACCAATTGGAATTGGATGCGG
AATTGGATGCCC
(x1=0, x2=1, x3=1, y=1)
(x1=0, x2=0, x3=1, y=0)
(x2=1, x3=1, y=1)
(x2=1, x3=0, y=0)
(x2=1, y=0)
TTCCGG
TTAACC
TTAACC
TTAACC
TTAACC
AAAACCAATTGGAATTGGATGCGG
TTAACC
AATTGGCCTTGGATGCGG
TTAACC
AATTGGAAGGCCATGCCC
TTCCGGTTAACC
AATTGGATGCCC
TTAACC
Majority voting
Predict the class
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
PLM ChipPLM Chip
SNU Biointelligence LabSNU Biointelligence Lab
Future of Molecular Future of Molecular NanobiointelligenceNanobiointelligenceComputersComputers
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Future Technology EnablersFuture Technology Enablers
Source: Motorola, Inc, 2000
Now +2 +4 +6 +8 +10 +12
Full motion
mobile
video/office
Metal gates,
Hi-k/metal
oxides, Lo-k
with Cu, SOI
Pervasive voice
recognition, “smart”
transportation
Vertical/3D
CMOS, Micro-
wireless nets,
Integrated optics
Smart lab-on-chip,
plastic/printed ICs,
self-assembly
Quantum computer,
molecular electronics
Bio-electric
computers
Wearable communications,
wireless remote medicine,
‘hardware over internet’ !
1e6-1e7 x lower power
for lifetime batteries
True neural computing
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Molecular Molecular BiocomputersBiocomputers
DNA 나노구조를이용한 Patterning
In VitroWet 데이터뱅크를이용한정보검색
DNA 구조설계
지원소프트웨어
분자기반의대규모
데이터베이스
소프트웨어
분자기반의대규모연상메모리
초소형
초대용량
정보검색시스템
자기조립에기반한
나노구조생성Molecular
electronic
components &
circuits
하드웨어
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
H/W & S/W Technology for Wet H/W & S/W Technology for Wet Information RetrievalInformation Retrieval
Silicon based
approach
Wet Blast
Silicon
Processor
DNA Computer
DNA
Processor
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Design Support and Programming Software Design Support and Programming Software for DNA Computers and for DNA Computers and NanoNano--MachinesMachines
Data
PluginData
Plugin
Fitness
PluginFitness
Plugin
GA Engine
PluginGA Engine
Plugin
Plugin Manager
NACST/
Seq
NACST/Sim
NACST/
Report
NACST/
Plotter
GUI
[Shin et al., IEEE TEC 2005]
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
BIT BIT 시장시장전망전망
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Biosensors (Bio Data) + Biosensors (Bio Data) + BiocomputersBiocomputers(Bio H/W) + Bioinformatics (Bio S/W)(Bio H/W) + Bioinformatics (Bio S/W)
Biocomputer
Bead
Capture probe
(Vn = 1)
Vn = 1 Vn+1Vn+2
Vn+3Vn-1Vn-2
Vn = 0 Vn+1Vn+2
Vn+3Vn-1Vn-2
3'-ATCGTCGAAGGAATGC-5'
5'-TAGCAGCTTCCTTACG-3'
5'-ACACTGTGCTGATCTC-3'
DNA Algorithm
Bioinformatics S/W
Biosensors
Bio-MEMS Technology
Design Support Software
© 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Collaborating labs
서울대바이오지능 & 인공지능연구실
서울대의대생화학교실서울대세포및미생물공학연구실한양대프로테오믹스연구실㈜바이오니아 & ㈜바이오인포메틱스
Supported by
과기부국가지정연구실사업
산자부차세대신기술연구개발사업
More information at
http://bi.snu.ac.kr/
http://cbit.snu.ac.kr/
AcknowledgementsAcknowledgements