Ashish Goel Stanford University Joint work with Len Adleman, Holin Chen, Qi Cheng, Ming-Deh Huang, Pablo Moisset, Paul.

Slides:



Advertisements
Similar presentations
Ashish Goel, 1 A simple analysis Suppose complementary DNA strands of length U always stick, and those of length L never stick (eg:
Advertisements

Strict Self-Assembly of Discrete Sierpinski Triangles James I. Lathrop, Jack H. Lutz, and Scott M. Summers Iowa State University © James I. Lathrop, Jack.
Active Tile Self Assembly: Daria Karpenko Department of Mathematics and Statistics, University of South Florida Simulating Cellular Automata at Temperature.
An information-bearing seed for nucleating algorithmic self-assembly Presented by : Venkata Chaitanya Goli Robert D. Barish1, Rebecca Schulman1,
Self-Assembly with Geometric Tiles ICALP 2012 Bin FuUniversity of Texas – Pan American Matt PatitzUniversity of Arkansas Robert Schweller (Speaker)University.
1 SODA January 23, 2011 Temperature 1 Self-Assembly: Deterministic Assembly in 3D and Probabilistic Assembly in 2D Matthew CookUniversity of Zurich and.
The Power of Seeds in Tile Self-Assembly Andrew Winslow Department of Computer Science, Tufts University.
Design of a Minimal System for Self-replication of Rectangular Patterns of DNA Tiles Vinay K Gautam 1, Eugen Czeizler 2, Pauline C Haddow 1 and Martin.
Mansi Mavani Graduate Student Department of Physics, OSU Stillwater
Self-Assembly Ho-Lin Chen Nov Self-Assembly is the process by which simple objects autonomously assemble into complexes. Geometry, dynamics,
Reducing Tile Complexity for Self-Assembly Through Temperature Programming Midwest Theory Day, December 10, 2006 Based on paper to appear in SODA 2006.
Complexities for Generalized Models of Self-Assembly Gagan Aggarwal Stanford University Michael H. Goldwasser St. Louis University Ming-Yang Kao Northwestern.
Topics in Biological Physics Design and self-assembly of two-dimensional DNA crystals Benny Gil 16/12/08 Fig3.a.
DNA Computing by Self Assembly  Erik Winfree, Caltech.
Robust Self-Assembly of DNA Eduardo Abeliuk Dept. of Electrical Engineering Stanford University November 30, 2006.
DNA Lithography Alex Buzduga & Matt Mosteller. Context: DNA Replication DNA is formed by two complementary strands, twisted in a helix To replicate, DNA.
Reducing Tile Complexity for Self-Assembly Through Temperature Programming Symposium on Discrete Algorithms SODA 2006 January 23, 2006 Robert Schweller.
Matthew J. Patitz Explorations of Theory and Programming in Self-Assembly Matthew J. Patitz Department of Computer Science University of Texas-Pan American.
1 Proceedings of the 24 th Annual ACM-SIAM Symposium on Discrete Algorithms January, 2013 Fuel Efficient Computation in Passive Self-Assembly Robert SchwellerUniversity.
DNA Based Self-Assembly and Nano-Device: Theory and Practice Peng Yin Committee Prof. Reif (Advisor), Prof. Agarwal, Prof. Hartemink Prof. LaBean, Prof.
Chemical Basis of Life Chapter 2. Introduction Matter - anything that has mass Made of elements Substance that cannot be broken down to other substances.
Bonding A covalent bond is stronger and holds the atoms in a molecule together. A Hydrogen bond is weaker and it attracts molecules to one another.
Algorithmic self-assembly for nano-scale fabrication Erik Winfree Computer Science Computation & Neural Systems and The DNA Caltech DARPA NSF NASA.
Molecular Self-Assembly: Models and Algorithms Ashish Goel Stanford University MS&E 319/CS 369X; Research topics in optimization; Stanford University,
Theoretical Tile Assembly Models Tianqi Song. Outline Wang tiling Abstract tile assembly model Reversible tile assembly model Kinetic tile assembly model.
Notes for self-assembly of thin rectangles Days 19, 20 and 21 of Comp Sci 480.
Notes on assembly verification in the aTAM Days 22, 24 and 25 of Comp Sci 480.
Introduction to Chemistry Chapter 2. Introduction Matter - anything that has mass Made of elements (92 naturally occurring Element - substance that cannot.
Notes on the optimal encoding scheme for self-assembly Days 10, 11 and 12 Of Comp Sci 480.
What is DNA Computing? Shin, Soo-Yong Artificial Intelligence Lab.
Powering the nanoworld: DNA-based molecular motors Bernard Yurke A. J. Turberfield University of Oxford J. C. Mitchell University of Oxford A. P. Mills.
4/4/20131 EECS 395/495 Algorithmic DNA Self-Assembly General Introduction Thursday, 4/4/2013 Ming-Yang Kao General Introduction.
Horn Clause Computation by Self-Assembly of DNA Molecules Hiroki Uejima Masami Hagiya Satoshi Kobayashi.
Notes on local determinism Days 12, 13 and 14 of Comp Sci 480.
Algorithmic Self-Assembly of DNA Sierpinski Triangles Ahn, Yong-Yeol Journal Club.
DNA nanotechnology: Geometric sorting boards
Biochemistry The study of chemical reactions of living things.
An Introduction to Algorithmic Tile Self-Assembly.
Notes for temperature 1 self- assembly Days 15, 16, 17 and 18 of Comp Sci 480.
Ashish Goel, 1 The Source of Errors: Thermodynamics Rate of correct growth ¼ exp(-G A ) Probability of incorrect growth ¼ exp(-G A.
1 David DotyCalifornia Institute of Technology Matthew J. PatitzUniversity of Texas Pan-American Dustin ReishusUniversity of Southern California Robert.
NANO TECHNOLOGY. Something to think about Imagine being able to cure cancer by drinking a medicine stirred into your favorite fruit juice. Imagine a supercomputer.
Towards Autonomous Molecular Computers Towards Autonomous Molecular Computers Masami Hagiya, Proceedings of GP, Nakjung Choi
1 35 th International Colloquium on Automata, Languages and Programming July 8, 2008 Randomized Self-Assembly for Approximate Shapes Robert Schweller University.
Bonding A covalent bond is stronger and holds the atoms in a molecule together. A Hydrogen bond is weaker and it attracts molecules to one another.
1 January 18, 2010 Shape Replication through Self-Assembly and Rnase Enzymes Zachary AbelHarvard University Nadia BenbernouMassachusetts Institute of Technology.
Complexity of Graph Self-Assembly in Accretive Systems and Self-Destructible Systems Peng Yin Joint with John H. Reif and Sudheer Sahu 1 Department of.
Developing DNA nanotechnology for use in nanoelectronics
Molecular Self-Assembly: Models and Algorithms Ashish Goel Stanford University MS&E 319/CS 369X; Research topics in optimization; Stanford University,
Molecular Algorithms -- Overview
Computational and Experimental Design of DNA Devices
Molecular Self-Assembly: Models and Algorithms Ashish Goel Stanford University MS&E 319/CS 369X; Research topics in optimization; Stanford University,
Introduction to Tiling Assembly
Molecular Computation
Programmable DNA Lattices: Design Synthesis & Applications
Thirteenth International Meeting on DNA Computers
Paul Rothemund’s Scaffolded DNA Origami Method
Tiing Error Correction & ExperimentsChen
DNA Self-Assembly Robert Schweller Northwestern University
Strict Self-Assembly of Discrete Sierpinski Triangles
Horn Clause Computation by Self-Assembly of DNA Molecules
JSPS Project on Molecular Computing (presentation by Masami Hagiya)
Self-Assembly Ho-Lin Chen Nov
DNA computing on surfaces
John H. Reif and Sudheer Sahu
Self-Assembly of Any Shape with
DNA Hybridization Catalysts and Catalyst Circuits
Combinatorial Optimization Problems in Self-Assembly (Given a shape, output an “efficient” tile-system for assembling it)
The Power of Nondeterminism in Self-Assembly
Algorithms for Robust Self-Assembly
Presentation transcript:

Ashish Goel Stanford University Joint work with Len Adleman, Holin Chen, Qi Cheng, Ming-Deh Huang, Pablo Moisset, Paul Rothemund, Rebecca Schulman, Erik Winfree Algorithmic Self-Assembly at the Nano-Scale Counter made by self-assembly [Rothemund, Winfree ’00] [Adleman, Cheng, Goel, Huang ’01] [Cheng, Goel, Moisset ‘04]

Ashish Goel, 2 Molecular Self-assembly  Self-assembly is the spontaneous formation of a complex by small (molecular) components under simple combination rules  Geometry, dynamics, combinatorics are all important  Inorganic: Crystals, supramolecules  Organic: Proteins, DNA  Goals: Understand self-assembly, design self-assembling systems  A key problem in nano-technology, molecular robotics, molecular computation

Ashish Goel, 3 A Matter of Scale Question: Why an algorithmic study of “molecular” self- assembly specifically? Answer: The scale changes everything  Consider assembling micro-level (or larger) components, eg. robot swarms. Can attach rudimentary computers, motors, radios to these structures. robot swarms Can now implement an intelligent distributed algorithm.  In molecular self-assembly, we have nano-scale components. No computers. No radios. No antennas. Need local rules such as “attach to another component if it has a complementary DNA strand”  Self-assembly at larger scales is interesting, but is more a sub- discipline of distributed algorithms, artificial intelligence etc.

Ashish Goel, 4 The Tile Model of Self-Assembly [Wang ’61]

Ashish Goel, 5 Synthesized Tile Systems – I  Styrene molecules attaching to a Silicon substrate  Coat Silicon substrate with Hydrogen  Remove one Hydrogen atom and bombard with Styrene molecules  One Styrene molecule attaches, removes another Hydrogen atom, resulting in a chain  Suggested use: Self-assembled molecular wiring on electronic circuits [Wolkow et al. ’00]

Ashish Goel, 6 Synthesized Tile Systems - II A DNA “rug” assembled using DNA “tiles” The rug is roughly 500 nm wide, and is assembled using DNA tiles roughly 12nm by 4nm (false colored) (Due to Erik Winfree, Caltech)

Ashish Goel, 7 Rothemund’s DNA Origami A self-folded virus!!

Ashish Goel, 8 Abstract Tile Systems  Tile: the four glues and their strengths  Tile System:  K tiles Infinitely many copies available of each tile  Temperature   Accretion Model:  Assembly starts with a single seed tile, and proceeds by repeated addition of single tiles e.g. Crystal growth  Are interested primarily in tile systems that assemble into a unique terminal structure [Rothemund and Winfree ‘00] [Wang ‘61]

Ashish Goel, 9 Is Self-Assembly Just Crystallization?  Crystals do not grow into unique terminal structures  A sugar crystal does not grow to precisely 20nm  Crystals are typically made up of a small number of different types of components  Two types of proteins; a single Carbon molecule  Crystals have regular patterns  Computer circuits, which we would like to self-assemble, don’t  Molecular Self-assembly = combinatorics + crystallization  Can count, make interesting patterns  Nature doesn’t count too well, so molecular self-assembly is a genuinely new engineering paradigm. Think engines. Think semiconductors.

Ashish Goel, 10 DNA and Algorithmic Self-Assembly We will tacitly assume that the tiles are made of DNA strands woven together, and that the glues are really free DNA strands  DNA is combinatorial, i.e., the functionality of DNA is determined largely by the sequence of ACTG bases. Can ignore geometry to a first order. Trying to “count” using proteins would be hell  Proof-of-concept from nature: DNA strands can attach to “combinatorially” matching sequences  DNA tiles have been constructed in the lab, and DNA computation has been demonstrated  Can simulate arbitrary tile systems, so we do not lose any theoretical generality, but we get a concrete grounding in the real world  The correct size (in the nano-range)

Ashish Goel, 11 A Roadmap for Algorithmic Self-Assembly  Self-assembly as a combinatorial process  The computational power of self-assembly  Self-assembling interesting shapes and patterns, efficiently Automating the design process?  Analysis of program size and assembly time  Self-assembly as a chemical reaction  Entropy, Equilibria, and Error Rates  Reversibility  Connections to experiments  Self-assembly as a machine  Not just assemble something, but perform work  Much less understood than the first three

Ashish Goel, 12 Can we create efficient counters?

Ashish Goel, 13 Can we create efficient counters? Yes! Eg. Using “Chinese remaindering” T=2

Ashish Goel, 14 Can we create efficient counters? Yes! Eg. Using “Chinese remaindering” T=2

Ashish Goel, 15 Can we create efficient counters? Yes! Eg. Using “Chinese remaindering” T=2

Ashish Goel, 16 Yes! Eg. Using “Chinese remaindering” T=2 Can we create efficient counters?

Ashish Goel, 17 Can we create efficient counters? Yes! Using “Chinese remaindering” T=2

Ashish Goel, 18 Can we create efficient counters? Yes! Eg. Using “Chinese remaindering” T=2 Generalizing: Say p 1, p 2, …, p k are distinct primes. We can use  i p i tiles to assemble a k £ (  i p i ) rectangle.

Ashish Goel, 19 Molecular machines

Ashish Goel, 20 Strand Invasion

Ashish Goel, 21 Strand Invasion

Ashish Goel, 22 Strand Invasion

Ashish Goel, 23 Strand Invasion

Ashish Goel, 24 Strand Invasion

Ashish Goel, 25 Strand Invasion

Ashish Goel, 26 Strand Invasion

Ashish Goel, 27 Strand Invasion

Ashish Goel, 28 Strand Invasion

Ashish Goel, 29 Strand Invasion

Ashish Goel, 30 Strand Invasion

Ashish Goel, 31 Strand Invasion

Ashish Goel, 32 Strand Invasion

Ashish Goel, 33 Strand Invasion

Ashish Goel, 34 Strand Invasion

Ashish Goel, 35 Strand Invasion

Ashish Goel, 36 Strand Invasion

Ashish Goel, 37 Strand Invasion

Ashish Goel, 38 Strand Invasion

Ashish Goel, 39 Strand Invasion

Ashish Goel, 40 Strand Invasion (cont) Strand Invasion

Ashish Goel, 41 Strand Invasion