who am i?

Who am I?

name danny van noort

education MSc. experimental physicsuniversity of LeidenThe Netherlands

PhD. applied physicsLinköpings universitySweden

Post docs BioMIP(BioMolecular Information Processing)Institute of computer science and mathematics (GMD)Sankt AugustinGermany

Dept. of Ecology and Evolutionary BiologyPrinceton UniversityUSA

Who am I?

name danny van noort

Office Room 115

building #138, ICT

tel: 880 9131 0r 881 9882

email [email protected]

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

Where to find me

Where to find me

Course outline

1 Introduction

2 Theoretical backgroundBiochemistry/molecular biologyComputation

3 Extension of theoretical background (biochemistry or computer science)

4 History of the field

5 Splicing systems

6 P systems

7 Hairpins

8 Micro technology introductions Microreactors / Chips

9 Microchips and fluidics

10 Self assembly

11 Regulatory networks

12 Molecular motors

13 DNA nanowires

14 DNA computing - summery

Course outline

Course outline

Essay + presentation on new molecular computing idea

Presentation on literature

date 3th and 10th of June

Announcement

NO Lecture

What is DNA computing?

What is DNA Computing?

The field of DNA computing is concerned with the possibility of performing computations using biological molecules.

It is also concerned with understanding how complex biological molecules process information in an attempt to gain insight into new models of computation.

Cells and nature compute by reading and rewriting DNA by processes that modify sequence at the DNA or RNA level. DNA computing is interested in applying computer science methods and models to understand such biological phenomena and gain insight into early molecular evolution and the original of biological information processing.

Molecular electronics, Theoretical biology, Evolutionary biology, Emergent computation, Brain sciences, Organic chemistry, Biomimetic engineering, Parallel processing, Distributed computing, Behavioural ecology, Cytology, Discrete mathematics, Optimisation theory, Artificial Intelligence, Cognitive science, Botany, Psychology, Algorithmics, Clinical engineering, Biophysics, Connectionism, Integrative physiology, Technology transfer, Selectionism, Immunology, Automata theory, Evolutionary computation, Simulation of computational systems, Histology, Ethology, Medical computing, Signal transduction and processing, Cellular automata, Electronic engineering, Vision, Object oriented design, Philosophy of science, VLSI, Non-linear dynamical systems, Game theory, Communication, Bioengineering, Self-organisation, Biochemistry, Pattern recognition, Information theory, Machine learning, Biosystem simulation, Genetics, Mathematical biology, Microbiology, Zoology, Science education, Physiology, Systems theory, Biosensors, Analogue devices and sensors, Microtechnology, Robotics ...


15011001101010001 ATGCTCGAAGCT



a completely new method among a few others (e.g., quantum computing) of general computation alternative to electronic/semi-conductor technology

uses biochemical processes based on DNA

What is DNA Computing not?

not to confuse with bio-computing which applies biological laws (evolution, selection) to computer algorithm design.

Biocomputing vs. Bioinformatics

Biomolecular computingDNA computing

1.6-2.2Inter-metal Dielectric K 45 nm

16

Known CMOS limitations

1999 2002 2005 2008 2011

<1.5

Source: Texas Instruments and ITRS IC Design Technology Working Group

140 nm

80 nm

60 nm

parameters approach molecule size

Gate length

4.0

2.71.6-2.2

0.251

4

Relative Fab Cost

20

Future technology

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

Historical timeline

Research

1950’s …

R.Feynman’spaper on sub microscopiccomputers

1994

L.Adleman solves Hamiltonian path problem using DNA.Field started

1995

D.Boneh paper on breaking DES with DNA

Commercial

1970’s …

DNA usedin bio application

1996

Human GenomeSequence

2000

2000

DNA computerarchitecture ?

Affymetrix sells GeneChipDNA analyzer

2015

Commercial computer ?

2005

Lucentbuilds DNA“motor”

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

Computer speed number of parallel processors number of steps each processor can perform per unit of time

DNA computer 3 grams of water contains 1022 molecules massively parallel

Electronic computer advantage in number of steps performed per unit of time

Speed of DNA computing

information per space unit perform per unit of time

DNA computer 106 Gbits per cm2 (1 bit per nm3)

Electronic computer 1 Gbits per cm2

Density of DNA computing

DNA computer 1019 operations per Joule

Electronic computer 109 operations per Joule

Efficiency of DNA computing

DNA as Computational Tool

DNA as computing tool

Nucleotide: purine or pyrimidine base deoxyribose sugar phosphate group

Purine bases A(denine), G(uanine)

DNA sequences consist of A, C, G, T

Pyrimidine bases C(ytosine), T(hymine)


{000} {001} {010} {011} {100} {101}{110} {111}

All possible solutions

Negative selection

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'

Selection principle

V0-1: 5'-AACCACCAACCAAACC V0-0: 5'-AAAACGCGGCAACAAG V1-1: 5'-TCAGTCAGGAGAAGTC V1-0: 5'-TCTTGGGTTTCCTGCA V2-1: 5'-TTTTCCCCCACACACA V2-0: 5'-TTGGACCATACGAGGA V3-1: 5'-CGTTCATCTCGATAGC V3-0: 5'-AGAGTCTCACACGACA V4-1: 5'-AAGGACGTACCATTGG V4-0: 5'-CTCTAGTCCCATCTAC V5-1: 5'-CAACGGTTTTATGGCG V5-0: 5'-GCGCAATTTGGTAACC V6-1: 5'-TAGCAGCTTCCTTACG V6-0: 5'-ACACTGTGCTGATCTC V7-1: 5'-CACATGTGTCAGCACT V7-0: 5'-TGTGTGTGCCTACTTG V8-1: 5'-GATGGGATAGAGAGAG V8-0: 5'-AATCCCACCAGTTGAC V9-1: 5'-ATGCAGGAGCGAATCA V9-0: 5'-GCTTGTTCAACCTGGTV10-1: 5'-CCCAGTATGAGATCAG V10-0: 5'-CTGTCCAAGTACGCTAV11-1: 5'-ATCGAGCTTCTCAGAG V11-0: 5'-TGTAGAGGCTAGCGAT

Word design with 16 bases

Logic operations

Logic NOT operations

a b

Logic AND operations

a b

Logic OR operations

((h f) a) ((g i) b)

((d h) c)

((c i) d)

((a g) f)

3x3 knight problem

3x3 knight problem

Selection module

magnet

Positive selection module

Some pictures

The highlights

Leonard Adleman Molecular computation of solutions to combinatorial problems Science, 266, 1021-1024, 1994

Q. Liu et al. DNA computing on a chip Nature, vol. 403, pp. 175-179, 2000

Q. Ouyang et al. DNA solution to the maximal clique problem Science, 278, 446-449, 1997

Richard Lipton DNA solution to hard combinatorial problems problem Science, 268, 542-545, 1995

DNA computing: the highlights

Hamilton path problem Millions of DNA strands,

diffusing in a liquid, can self-assemble into all possible path configurations.

A judicious series of molecular maneuvers can fish out the correct solutions.

Adleman, combining elegance with brute force, could isolate the one true solution out of many probability.

Lenard Adleman: hamiltonian path

universal computation can be performed by the sequence-directed self-assembly of DNA into a 2D sheet

experimental investigations have demonstrated that 2D sheets of DNA will self-assemble

Wang tiles, branched DNA with sticky ends, reduces this theoretical construct to a practical one

this type of assembly can be shown to emulate the operation of a Universal Turing Machine.

Eric Winfree: DNA self-assembly

Eric Winfree: DNA self-assembly

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Ned Seeman: DNA self-assembly

A P system is a computing model which abstracts from the way the alive cells process chemical compounds in their compartmental structure. In short, in the regions defined by a membrane structure we have objects which evolve according to given rules.

The objects can be described by symbols or by strings of symbols (in the former case their multiplicity matters, that is, we work with multisets of objects placed in the regions of the membrane structure; in the second case we can work with languages of strings or, again, with multisets of strings).

By using the rules in a nondeterministic, maximally parallel manner, one gets transitions between the system configurations. A sequence of transitions is a computation. With a halting computation we can associate a result, in the form of the objects present in a given membrane in the halting configuration, or expelled from the system during the computation.

Various ways of controlling the transfer of objects from a region to another one and of applying the rules, as well as possibilities to dissolve, divide or create membranes were considered.

Gheorghe Păun: P-systems

a b

c


a b

bc

aabc


There is a solid theoretical foundation for splicing as an operation on formal languages.

In biochemical terms, procedures based on splicing may have some advantages, since the DNA is used mostly in its double stranded form, and thus many problems of unintentional annealing may be avoided.

The basic model is a single tube, containing an initial population of dsDNA, several restriction enzymes, and a ligase. Mathematically this is represented as a set of strings (the initial language), a set of cutting operations, and a set of pasting operations.

It has been proved to a Universal Turing Machine.

Tom Head: splicing systems

These are the techniques that are common in the microbiologist's lab and can be used to program a molecular computer. DNA can be: synthezise desired strands can be created separate strands can be sorted and separated by

length merge by pouring two test tubes of DNA into one

to perform union extract extract those strands containing a given

pattern melt/anneal breaking/bonding two ssDNA molecules with

complementary sequences amplify use of PCR to make copies of DNA strands cut cut DNA with restriction enzymes rejoin rejoin DNA strands with 'sticky ends' detect confirm presence or absence of DNA

Tom Head: splicing systems

Q. Liu: experiments on a surface

(wxy) (wyz) (xy) (wy)=1

{0000} {0001} {0010} {0011} {0100} {0101}{0110} {0111}{1000} {1001} {1010} {1011} {1100} {1101}{1110} {1111}


Computing in biology

Cells and nature compute by reading and rewriting DNA by processes that modify sequence at the DNA or RNA level. DNA computing is interested in applying computer science methods and models to understand biological phenomena and gain insight into early molecular evolution and the origin of biological information processing.

Computing in biology

The biology of computing

Pyrimidine pathway

Electronic pathway

Tokyo subway system

lacl cl cl tetRtetR gfpgfp

P T PP

L2P T -

tet lac ct gfp

From Guet et al., Science 24 May 2002

lac- strain CMW101 three promoter genes: lacl, cl, tetR the binding state of lacl and tetR can be changed with IPTG (isopropyl -D-thiogalactopyranoside) and aTc (anhydro-tetracycline).

only signal when aTc but no IPTG

Transcriptional regulators

RNA can be used to programme a cell to produce a specific output, in form of proteins or nanostructures.

(self)-replication is contained in propagation and can be compared with the goal to produce to build self replicating machines in silico.

cell are the factories, RNA is the input

Instructional design

Instructional design: proteins

Instructional design: phage

Bacteria swim by rotating flagella Motor located at junction of

flagellum and cell envelope Motor can rotate clockwise (CW) or

counterclockwise (CCW)

CW CCW CW

Molecular motors

Massively parallel problem solving Combinatorial optimization Molecular nano-memory with fast associative search AI problem solving Medical diagnosis, drug discovery Cryptography Further impact in biology and medicine:

Wet biological data bases Processing of DNA labeled with digital data Sequence comparison Fingerprinting

Applications of biomolecular computing

Future applications

85

a) Self-replication: Two for one

Based on DNA self-replication

b) Self-repair:

Based on regeneration

c) DNA computer mutation/evolution

or

biohazard

Learning.

May be malignant

d) New meaning of a computer virus ?

Interesting possibilities

Evolvable biomolecular hardware

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

http://www.mrc.uidaho.edu/fpga/circuit.html

Trillions of DNA

Name Tel. Address

James 419-1332 Washington DC

David 352-4730 La Jolla, CA.

Paul 648-7921 Honolulu, HI

Julia 418-9362 Palo Alto CA

…

Phone book

Molecular storage

http://www.mkland.co.kr/img/grpc07s.gif

88DetectionDetection

MicroreactorMicroreactor PCRPCR Gel ElectrophoresisGel Electrophoresis

BeadBead

DNA computing algorithm MEMS (Microfluidics)

+

Molecular computer on a chip

BioMEMS

Lab-on-a-chip technology

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

Conclusions

DNA Computing uses DNA molecules to computing or storage materials.

DNA computing technology has many interesting properties, including Massively parallel, solution-based, biochemical Nano-scale, biocompatible high energy efficiency high memory storage density

DNA computing is in very early stage of development.

Conclusions

MIT, Caltech, Princeton University, Berkeley, Yale, Duke, Irvine, Delaware, Lucent

Molecular Computer Project (MCP) in Japan EMCC (European Molecular Computing Consortium) is

composed of national groups from 11 European countries BioMIP (BioMolecular Information Processing) at the

German National Research Center for Information Technology (former GMD, now Fraunhofer)

Leiden Center for Natural Computation (LCNC)

Research groups

94

Biomolecular Computation (BMC) www.cs.duke.edu/~reif/ Leiden Center for Natural Computation (LCNC)

www.wi.leidenuniv.nl/~lcnc/ BioMolecular Information Processing (BioMip)

www.gmd.de/BIOMIP European Molecular Computing Consortium (EMCC)

http://openit.disco.unimib.it/emcc/ DNA Computing and Informatics at Surfaces

www.corninfo.chem.wisc.edu/writings/DNAcomputing.html DNA nanostructres http://seemanlab4.chem.nyu.edu/

Web resources

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.

Books

who am i?

Documents

distributed computing

field of dna computing

medical computing

information theory

systems theory

theoretical biology

automata theory

optimisation theory