Mpp Rsv 2008 Public

The Molecular Programming Project

The California Institute of Technology (Caltech)The University of Washington, Seattle (UW)

Paul RothemundShuki Bruck Niles PierceEric Klavins Richard Murray

LEADERSHIP

Erik Winfree (PI)

“Creating the theory and practice of programming molecular systems”

The MPP

The Art ofMolecularProgramming

1962-2008 2008-20??

MPP 1

BiologyChemistry

NanotechnologyComputer Science MPPMPP

A tale of two technologies

Size: 25 m, 30 tons 20 m, 50 tons.Smarts: multiple CPUs 7 kg brainResolution: 45 nm in chips 0.3 nm everywhereComplexity: 106 parts, 1010 transistors 1017 cells, 1027 proteinsConstruction: built in factory growth algorithmSpecification: CAD files genetic program

??Molecular ProgrammingMolecular Programming

MPP 2

Programmable molecular subroutines

DNA circuits

DNA tiles

50 nm

DNA walkers

circuits

self-assembly

MPP 3

folding

biology

DNA origami100 nm

MPP teamachievements

dynamics

Growth of design complexity in DNA nanotechnology and DNA computing

Com

plex

ity (

nt)

Time1980 1990 2000 2005 201019951985

10

100

1000

10000

DNA 4-arm junctions(Seeman, 1982)

doubles every 3 years

conceptualadvances

MPP 4

Goals of the MPP• Goal 1: Create a functional abstraction hierarchy and use this

hierarchy to create programming languages and compilers.

• Goal 2: Create a theoretical framework for the analysis and design of molecular programs, one that serves as the underpinnings for an actual practice of molecular programming.

• Goal 3: Validate our compilers and theoretical framework with experimental systems utilizing molecular programs with 10 to 100 times the number of devices of components currently used.

• Goal 4: Test our molecular programming technologies on real-world applications.

• Goal 5: Recruit and train a generation of molecular programmers with the insights and skills necessary to conceive, design, and implement complex molecular systems.

MPP 5

Models of Computation

folding self-assembly circuits dynamics

Molecular program: a sequence of beads on a string, bond energies

System state: a path on a square lattice

System energy: sum of matching bond energies

G = -3

Execution:flip moves

Output: a finite shape

MPP 6



Molecular program: a set of tiles with attachment types and strengths

System state: an assembly of tiles


G = -3

Input:an initialassembly

Output:an extended structure

MPP 7

11

10 1

1

01 0

0

11 0

0

00 1

B

BB

1B B

B

BB

1B

B

1B

B

B1

BB1

B

00

11

Execution: attachment ofmatching tiles



Molecular program: a set of tiles with attachment types and strengths

System state: an assembly of tiles


G = -3

Execution: attachment ofmatching tiles

Output:an extended structure

MPP 8

c0

1c n

0

0n n

1

0c n

1

1n c

B

BB

0B B

B

BB

cB

B

cB

B

B0

BB0

B

n1

0c

47




Theory of Computation by Algorithmic Self-Assembly

Turing universal for computation (Winfree, 1996)Program-size complexity (Rothemund & Winfree, 2000)Time complexity (Adleman, Cheng, Goel, Huang, 2001)Error-correction & fault-tolerance (Chen & Goel, 2004)Self-healing (Winfree, 2006)Graph grammars and rule synthesis (Klavins & Ghrist, 2006)Turing universal for construction (Soloveichik & Winfree, 2007)

MPP 9



Molecular program: a set of formal chemical reaction steps

System state: concentrations or counts of species

System energy: chemical free energy

Input:amount of input species

Output:amount of output species

MPP 10

Execution: chemical kinetics



Theory of Computation by Chemical Reaction Networks

Digital logic circuits (Magnasco, 1997)Space-bounded Turing machines (Angluin, Aspnes, Eisenstat, 2007)Turing universal (Soloveichik, Cook, Winfree, Bruck, 2008)Formal machines and semantics (Cardelli, 2008)

Time complexity?Linear systems & signal processing?Error-correction & fault-tolerance?Programming stochastic behavior?Reaction-diffusion and spatial organization?

MPP 11



Molecular program: a set of units with attachment and detachment rules

System state: an assembly of units and port states

Execution: attachment anddetachment of

applicable unitsOutput:

a reconfigured structure

MPP 12


Design process: DNA origami

100 nm

P. W. K. Rothemund, Nature, 440: 297-302 (2006)MPP 13

Design process: algorithmic self-assembly

100 nm

K. Fujibayashi, R. Hariadi, S. H. Park,

E. Winfree, S. Murata (Nano Letters, 2008)MPP 14

Design process: DNA gate circuits

G. Seelig, D. Soloveichik, D. Y. Zhang, E. Winfree,Science, 314: 1585-1587 (2006)MPP 15

Design process: self-assembly & disassembly

P. Yin, H. M. T. Choi, C. R. Calvert, N. A. Pierce, Nature, 451: 318-422 (2008)MPP 16

A Compiler for Molecular Programming

(4) Theory for Design and Analysis• modeling and abstraction

• languages, semantics• geometry, space, stochastics

• modularity and composition• robustness and fault-tolerance• algorithms, data structures, efficiency

MPP 17


(3) Components and Mechanisms• modeling and abstraction

• formal representations, semantics• geometry, space, stochastics

• library of implementable functions• standard interfaces

a bx y

MPP 18


(2) Secondary structure mechanisms• modeling and abstraction

• molecular structure & dynamics• geometry, space, stochastics

• thermodynamics• energy landscapes• kinetic pathways

MPP 19


(1) Nucleic Acid Sequence Design• design constraints• combinatorial optimization• thermodynamic & kinetic validation• design rules

MPP 20


(0) Synthesis and operation• commercial DNA synthesis• purification, sample preparation• monitoring, characterization• debugging

MPP 21

autonomous molecular systems

Experimental Validation & Scale-Up

automated fluid handling parallel fluorescence readout

DNA microarray reader wafer scale atomic force microscope fluorescence microscopeMPP 22

Enabling Diverse Applications

Genome-based manufacturing of inanimate objects

Embedding systematic programmable molecular subsystems within biological, chemical, and nanotechnology systems.

Programmable therapies

Molecular instrumentationfor probing cellular processes

MPP 23

DNApatternedscaffold

Circuits for detection & analysis of features within in situ samples.

Circuits for diagnosis & response to diseases in living cells.

Algorithms for growth of complex materials and structures.

NUPACK softwareOn-line: thermodynamic analysissequence designIn development:kinetics simulationsenergy landscapescompiler tools

CoursesTextbooksWorkshopsBoot campsUG research (~60 in 5 years)

Outreach, Knowledge Transfer, and Education

Science-inspired Art Paintings by Ann Erpino. MOMA exhibit.

K-12 visiting daysGiving Pasadena & Seattle public school kids a personal view of science & higher education.

Pasadena

Seattle

MPP 24

Expertise and Project Management

Paul RothemundBE, CNS,CS, Caltech

MacArthur Fellow

DNA computing

DNA origami

materials science

chemistry

Shuki BruckCNS, EE, Caltech

IST founding director

distributed systems

circuit complexity

fault tolerance

stochastic chemistry

Niles PierceACM, BE, Caltech

Bioengineering chair

numerical methods

sequence design

DNA engineering

biomedicine

Eric KlavinsEE, UW

NSF CAREER

control theory

robotics

formal languages

synthetic biology

Richard MurrayCDS, ME, Caltech

IST director

control theory

robotics

distributed systems

synthetic biology

LEADERSHIP

Erik Winfree (PI)CS, CNS, BE, Caltech

MacArthur Fellow

DNA computing

DNA self-assembly

biochemical circuits

theory of computation

STUDENTS AND POSTDOCSSimple flat structure, encouraging independence and exploration:

MPP pool of ~7 undergraduates, ~10 graduate students, ~2 postdocs, ~1 visiting scholar (UW + Caltech)

other funding: ~5 undergraduates, ~14 graduate students, ~5 postdocs involved with MPP areas

Monthly joint (cyber) group meetings, annual joint meeting at Caltech

MPP 25

A new generation of MPP researchers

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MPP 26

The Molecular Programming Project

• Goal 1: abstraction, programming languages, compilers– Domain-specific compilers for DNA tiles, origami, circuits, assembly– Prototype for a general-purpose molecular programming language and compiler– Algorithms for designing and mapping kinetic pathways on free energy landscapes

• Goal 2: theoretical analysis and design– Fault-tolerant architectures and molecular compensators– Frameworks for programming stochastic behavior, geometry, dynamics– Complexity theory for molecular algorithms

• Goal 3: experimental validation– Automated synthesis, characterization and debugging– 1000 gate circuit implementing a programmed function– 10,000 pixel programmable spatial patterns

• Goal 4: enabling real-world applications– Nano-assembled crossbar memory w/ decoding circuitry– Multiple input/output programmable triggered cell death– Multi-channel spatio-temporal “logic analyzer” for genetic expression in cells

• Goal 5: train a generation of molecular programmers– Use of molecular programming tools by non-experts– Molecular programming in undergraduate CS classes– A new generation of molecular programming researchers

2008-2013

MPP 27

MPPMPP

ComputerScience

BiologyChemistry

Nanotechnology

Mpp Rsv 2008 Public

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