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

btzhang@cse.snu.ac.kr

http://bi.snu.ac.kr/

Humans and ComputersHumans and Computers

Silicon Computers

What Kind of

Computers?

Human Computers

The Entire Problem Space

Mind, Brain, Cells, MoleculesMind, Brain, Cells, Molecules

Molecule

1011 cells

1010 mol.

∞ memory

Molecular Mechanisms of Synaptic LearningMolecular Mechanisms of Synaptic Learning

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)

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”

Levels of ComputationLevels of Computation

� Mind = y(Symbols) “Symbolic”

� Mind = f(Brain)

= f(g(Cells)) “Connectionist”

= f(g(h(Molecules)))

= y(Molecules) “Interactionist”

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

DNA Computation of Hamiltonian PathsDNA Computation of Hamiltonian Paths

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

♦ 반도체기술: 109 operations per Joule

Characteristics of DNA MoleculesCharacteristics of DNA Molecules

Self-assembly

Polymer

Repeat

Self-replication

Molecular recognition

DNA Based ComputersDNA Based Computers

[Braich et al., Science 2002]

DNA Computer by Olympus

DNA Computer by Adleman’s Group

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

DNA as Smart DrugsDNA as Smart Drugs

[Benenson et al., Nature 2001 & Nature, 2004]

Solving a 20Solving a 20--var 3var 3--CNF ProblemCNF Problem

DNA NanostructuresDNA Nanostructures

� Molecular Tweezers

� DNA nanostructure � Information

processing

methods

[Chen and Seeman, Nature 1991]

[Yurke et al., Nature 2000]

Molecular Memory and SelfMolecular Memory and Self--AssemblyAssembly

� DNA self-assembly as information processing utilizing

♦ Parallel-interaction

♦ Molecular recognition

♦ Self-organization

[Caltech and Duke]

NanoparticleNanoparticle--Based Theorem Based Theorem Proving for Medical DiagnosisProving for Medical Diagnosis

A B DNA linker

∆∆∆∆

5’ ¬

QTS ¬P R

¬RPQ¬S ¬T

5’ 3’¬Q

¬RPQ¬S

R¬S 5’II 3’ 3’5’

Au-SS-

[Park et al., in preparation]

DNA Computing Chip for Medical DiagnosisDNA Computing Chip for Medical Diagnosis

[Lee et al., in preparation]

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

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]

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

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?

CHAHLCA →→∧

LCHAHLCA ¬∧∧∧∨¬∧∨¬∨¬ )( )(

Transform into CNF

Diagnosis by DNA Computing:Diagnosis by DNA Computing:Logical InferenceLogical Inference

LCHAHLCA ¬∧∧∧∨¬∧∨¬∨¬ )( )(

Resolution

5’-GACTTGCAACGT-3’

5’-GTTA-3’

¬LTGCA

¬A ¬C LGACT TGCA ACGT

¬H ACAAT CTGA

¬H ACAAT CTGA ¬C L

GACT TGCA ACGT

¬LTGCA

GACT TGCA ACGT

¬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

¬¬¬¬A ¬¬¬¬C L

Diagnosis by DNA Computing:Diagnosis by DNA Computing:DetectionDetection

� DNA-Based nanoparticle assembly strategy

(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)

Probabilistic Library Model (PLM)Probabilistic Library Model (PLM)

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

Hybridize

[Zhang, DNAC-2004]

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

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

(x1=0, y=0)

AAAACCATGCCC

(x2=0, y=1)

AATTCCATGCGG

(x2=0, y=0)

AATTCCATGCCC

(x1=0, x2=0, y=0)

AAAACCAATTCCATGCCC

(x1=0, x2=0, y=1)

AAAACCAATTCCATGCGG

(x1=0, x2=1, y=0)

AAAACCAATTGGATGCCC

(x1=0, x2=1, y=1)

AAAACCAATTGGATGCGG

… (x1=0, x2=0, x3=0, y=0)

AAAACCAATTCCAAGGCCATGCCC

(x1=0, x2=0, x3=0, y=1)

AAAACCAATTCCAAGGCCATGCGG

(x1=0, x2=0, x3=1, y=0)

(x1=0, x2=0, x3=1, y=1)

AAAACCAATTCCAAGGGGATGCGG

(x1=0, x2=1, x3=0, y=0)

AAAACCAATTGGAAGGCCATGCCC

(x1=0, x2=1, x3=0, y=1)

AAAACCAATTGGAAGGCCATGCGG

AAAA ATGC

Amplify

Library Example 1

(x1=0, x2=1, x3=0, y=0)

TACGGGTTCCGGTTAACCTTTTGG

AATTGGAAGGCCATGCCC

AATTGGCCTTGGATGCGG

AATTGGATGCCC

TTTTGG

TTAACC

TTCCGG

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)

(x1=0, x2=1, x3=1, y=1)

(x2=1, y=0)

AATTGGATGCCC

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)

(x2=1, y=0)

AATTGGATGCCC

AATTGGAAGGCCATGCCC

AATTGGATGCCC

(x1=0, y=1)

AAAACCATGCGG

(x1=0, y=0)

AAAACCATGCCC

(x2=0, y=1)

AATTCCATGCGG

(x2=0, y=0)

AATTCCATGCCC

(x1=0, x2=0, y=0)

AAAACCAATTCCATGCCC

(x1=0, x2=0, y=1)

AAAACCAATTCCATGCGG

(x1=0, x2=1, y=0)

AAAACCAATTGGATGCCC

(x1=0, x2=1, y=1)

AAAACCAATTGGATGCGG

… (x1=0, x2=0, x3=0, y=0)

(x1=0, x2=0, x3=0, y=1)

(x1=0, x2=0, x3=1, y=0)

(x1=0, x2=0, x3=1, y=1)

(x1=0, x2=1, x3=0, y=0)

(x1=0, x2=1, x3=0, y=1)

AAAA ATGC

Amplify

Library

(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)

Example 2

(x1=0, x2=1, x3=1, y=1)

TTCCCCTTAACCTTTTGG TACGCC

(x2=1, y=0)

AATTGGATGCCC

AATTGGAAGGCCATGCCC

AATTGGCCTTGGATGCGG

AATTGGATGCCC

TTTTGG

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

(x2=1, x3=0, y=0)

AATTGGAAGGCCATGCCC

(x2=1, y=0)

AATTGGATGCCC

TTCCCCTACGCC

TTCCCC

TTCCCCTACGCC

AATTGGAAGGCCATGCCC

AATTGGATGCCC

TTAACC

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)

(x2=1, y=0)

AATTGGATGCCC

AATTGGAAGGCCATGCCC

AATTGGATGCCC

(x1=0, y=1)

AAAACCATGCGG

(x1=0, y=0)

AAAACCATGCCC

(x2=0, y=1)

AATTCCATGCGG

(x2=0, y=0)

AATTCCATGCCC

(x1=0, x2=0, y=0)

AAAACCAATTCCATGCCC

(x1=0, x2=0, y=1)

AAAACCAATTCCATGCGG

(x1=0, x2=1, y=0)

AAAACCAATTGGATGCCC

(x1=0, x2=1, y=1)

AAAACCAATTGGATGCGG

… (x1=0, x2=0, x3=0, y=0)

(x1=0, x2=0, x3=0, y=1)

(x1=0, x2=0, x3=1, y=0)

(x1=0, x2=0, x3=1, y=1)

(x1=0, x2=1, x3=0, y=0)

(x1=0, x2=1, x3=0, y=1)

AATTGGCCTTGGATGCGG

AAAA ATGC

Library

(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)

(x1=1, x2=1, x3=0)

TTCCGGTTAACCTTTTCC

(x2=1, y=0)

AATTGGATGCCC

Hybridization

TTCCGGTTAACCTTTTCC

TTAACCTTTTCC

AATTGGCCTTGGATGCGG

AATTGGAAGGCCATGCCC

AATTGGATGCCC

TTCCGG

AATTGGAAGGCCATGCCC

AATTGGCCTTGGATGCGG

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

AATTGGCCTTGGATGCGG

TTAACC

AATTGGAAGGCCATGCCC

TTCCGGTTAACC

AATTGGATGCCC

TTAACC

Majority voting

Predict the class

PLM ChipPLM Chip

SNU Biointelligence LabSNU Biointelligence Lab

Future of Molecular Future of Molecular NanobiointelligenceNanobiointelligenceComputersComputers

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

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

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

Processor

Design Support and Programming Software Design Support and Programming Software for DNA Computers and for DNA Computers and NanoNano--MachinesMachines

PluginData

Plugin

Fitness

PluginFitness

Plugin

GA Engine

PluginGA Engine

Plugin

Plugin Manager

NACST/

NACST/Sim

NACST/

Report

NACST/

Plotter

[Shin et al., IEEE TEC 2005]

BIT BIT 시장시장전망전망

Biosensors (Bio Data) + Biosensors (Bio Data) + BiocomputersBiocomputers(Bio H/W) + Bioinformatics (Bio S/W)(Bio H/W) + Bioinformatics (Bio S/W)

Biocomputer

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

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

molecular nanobiointelligence computers · 2015-11-24 · molecular nanobiointelligence computers...

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