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Who am I?. Who am I?. Where to find me. Where to find me. Where to find me. Course outline. Course outline. Course outline. Presentation on literature. Essay + presentation on new molecular computing idea. Announcement. NO Lecture. What is DNA computing?. - PowerPoint PPT PresentationTRANSCRIPT
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
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
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 ...
What is DNA Computing?
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 ...
What is DNA Computing?
What is DNA Computing?
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.
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
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)
DNA as computing tool
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
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
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
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
(wxy) (wyz) (xy) (wy)=1
{0000} {0001} {0010} {0011} {0100} {0101}{0110} {0111}{1000} {1001} {1010} {1011} {1100} {1101}{1110} {1111}
Q. Liu: experiments on a surface
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
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
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
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.
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
88DetectionDetection
MicroreactorMicroreactor PCRPCR Gel ElectrophoresisGel Electrophoresis
BeadBead
DNA computing algorithm MEMS (Microfluidics)
+
Molecular computer on a chip
Lab-on-a-chip technology
Integrates sample handling, separation and detection and data analysis for: DNA, RNA and protein solutions using LabChip technology
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