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Dr. Erik Winfree:The DNA and Natural Algorithms Group PDF Print E-mail
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Tuesday, 22 December 2009 08:16

Dr. Erik Winfree is an expert in DNA-based computers. He has expertise in the areas of computer science, computation and neural systems, molecular computation, computing by self-assembly, genetic regulatory networks, signal-transduction cascades, ribosomal translation, and DNA and RNA folding. Our group is interested in biomolecular computation: how systems of biomolecules, such as DNA and enzymes, can process information and carry out algorithms.  While our theoretical studies are wide-ranging, our experimental efforts focus on coaxing DNA to perform algorithmic tricks. 

Our task is to investigate how synthetic biochemical systems can be designed to carry out algorithms and compute; what models of computation arise from biochemical processes and how they can be programmed; and how to "compile" abstract descriptions of biomolecular algorithms down to specific synthetic DNA sequences that implement the desired computation in the laboratory.

Like the carefully orchestrated molecular processes that occur within living cells, biomolecular computation can in principle occur autonomously, without the need for any external intervention during the computation.   Being able to design and understand such systems is our ultimate goal.  We are exploring several interconnected paradigms of biomolecular computation, based loosely on processes that are ubiquitous throughout living organisms:

Algorithmic self-assembly of DNA tiles (inspired by crystals, microtubules, and virus capsids on the biological side, and by Wang tiles on the mathematical) encodes information in the geometric arrangement of tiles, and performs logical steps by the selective addition of tiles as geometrically compatible sites.  Algorithmic self-assembly may be ideally suited for bottom-up self-fabrication of complex nanostructures. A major question concerns how to reduce error rates during assembly; we are investigating "proofreading" logic for error-resilient algorithmic growth, as well as methods to programmably control the nucleation of self-assembled structures. Both theoretical and experimental projects are ongoing.

In vitro RNA transcriptional circuits are a stripped-down, bare-bones version of genetic regulatory networks in the cell; signals are carried by the concentration of specific RNA transcripts; RNA polymerase and RNase regulate the production and degradation of RNA.  In vitro RNA transcriptional circuits should allow dynamic control of biomolecular processes -- at the time scale of minutes. On the theory side, we have shown how these networks can function as biochemical neural networks; on the experimental side, we have demonstrated and characterized a two-node bistable circuit. Future research aims at spatial patterning in reaction-diffusion conditions, and at measuring stochastic behavior due to small copy numbers in small volumes.

Biochemical circuits, such as cellular signal transduction cascades, are logically related to boolean circuits. For example, a given enzyme molecules may be either phosphorylated ("on") or not ("off").   Phosphorylation cascades are ideal for the study of reliable computation in the presence of thermal "noise". More generally, one may ask how to design formal chemical reaction networks to perform computation, and how stochastic noise is shaped by network activity. Experimentally, we are constructing DNA-based logic gates that can be "wired" into arbitrary circuits.

Chemical self-replication and evolution must have gotten started somehow, way back when. We are using algorithmic self-assembly to investigate a radical hypothesis of Graham Cairns-Smith, that life got started as clay crystals that reproduced patterns as they grew. On paper, at least, it appears that simple crystal growth mechanisms are sufficient for very complex Darwinian evolution.

RNA and DNA hybridization and folding are essential processes for all DNA computing, and can perform complex logical operations in their own right.  Realistic yet tractable models of nucleic acid interactions form the foundation for higher-level descriptions of DNA nanodevices, and allow for automated design of DNA sequences for DNA structures and devices. We are developing fast simulation algorithms for simulating folding at the secondary structure level.

Research Tools

Tools and information are being migrated to the lab's (private access) DNA Lab Wiki. This page will soon be updated to contain links to
  • Software packages that we actively use for research, e.g. mfold, Vienna, Namot2, Tcl, RasMol..., as well as other software installed on our cluster, e.g. VMware, MATLAB, Mathematica, ...
  • Computer system administration and information.
  • Caltech stockrooms and what you might find in them.
  • Information on instruments we have in the lab (e.g. SPEX, AVIV, DI....) and their maintenance guidelines, including who is responsible for the instrument. (LOCAL ACCESS ONLY)
  • Vendors that we actively use, e.g. IDT DNA, Ambion,...
  • Laboratory protocols and policies (e.g. safety).
  • Some papers that might be of interest. (LOCAL ACCESS ONLY)

Recent Publication

  • Programmable Control of Nucleation for Algorithmic Self-Assembly. (Journal version, December 4, 2009) see below
  • Self-assembly of carbon nanotubes into two-dimensional geometries using DNA origami templates. *
    Hareem T. Maune, Si-Ping Han, Robert D. Barish, Marc Bockrath, William A. Goddard III, Paul W. K. Rothemund, Erik Winfree.
    Carbon nanotubes are amazing molecules -- rolled up sheets of hexagonal carbon mesh with astounding thermal and electrical properties, they're the nanocircuit engineer's dream. But they're oh so hard to handle! Too small and too slippery to pick up and put them where you want, most researchers either study individual nanotubes, make do with regular arrays of nanotubes, look for chance circuits in randomly scattered piles of nanotubes, or rely on bulk properties of tangled tubes. Here we suggest a way to self-assemble complex nanotube circuits in parallel -- by sticking them in precise locations on DNA origami nanobreadboards. In the future, we envision this technique being expanded from the two-nanotube devices demonstrated in this paper, to multiple nanotube circuits on individual origami, to large-scale nanotube circuits on origami placed on lithographically-patterned surfaces. Dream on!
    [Nature Nanotechnology, 8 November, 2009 (5 pages): paper, 1.3 MB, supplementary information, 2.1 MB.]
    (Caltech press release, Eric Drexler's blog )
  • Control of DNA Strand Displacement Kinetics Using Toehold Exchange. *
    David Yu Zhang and Erik Winfree.
    Computer programs work by transferring control from one line of the program to another. Many DNA programs work by transferring control from one part of a molecule to another. That's what toehold exchange does. It is an essential mechanism in many of our DNA strand displacement circuits. Here we study the device physics of toehold exchange and show that kinetics can be predicted remarkably accurately from the thermodynamics of toehold binding. With this understanding, we can make our DNA programs transition smoothly from step to step.
    [JACS, to appear, 2009 (12 pages): paper, 2.2 MB, supplementary information, 200 KB. ]
  • Placement and orientation of individual DNA shapes on lithographically patterned surfaces. *
    RJ Kershner, LD Bozano, CM Micheel, AH Hung, AR Fornof, JN Cha, CT Rettner, M Bersani, J Frommer, PWK Rothemund, GM Wallraff.
    Electrical engineers excel at making things using top-down patterning, such as using lithograph to etch circuits on large wafers of silicon, but it is quite difficult to achieve feature sizes below 20nm. Molecular engineers excel at making things using bottom-up self-assembly, such as using DNA hybridization to fold a virus genome into DNA origami, but it is quite difficult to put all the precisely-constructed molecules in the right places on a large scale. Patterning from 10cm down to 20nm. Patterning from 100nm down to 3Å. A match made in heaven. (Joint Caltech/IBM work.)
    [Nature Nanotechnology, 3: 557-561, 16 August, 2009 (5 pages): paper, 784 KB, supplementary information, 9.9 MB, and supplementary movie, 3.3 MB.]
    (Comments in the press... but please take them with a grain of salt. Working nanoscale circuits are much much further from reality than some of these would suggest. Let's be clear: the work here doesn't attempt to make functional circuits, it just provides a step toward the solution for how to position DNA origami on a lithographically-patterned substrate. We have no idea when or if this approach will pay off for a commercial application. Anyway: Caltech Press Release, IBM Press Release,, BBC, CNET, Wired, EE Times, Discover Magazine )
  • An Information-Bearing Seed for Nucleating Algorithmic Self-Assembly. *
    Robert D. Barish, Rebecca Schulman, Paul W. K. Rothemund, and Erik Winfree.
    What is a seed? The tiny seed of a giant sequoia tree, sprouting after the fire. The invisible seed of an idea, from which a thousand possibilities grow. A crystal seed, determining the order of all that grows from it. The seed of man and woman, carrying with it the future of humanity. Clearly, it's important stuff. Why? The seed carries the information, the creative part, the inspiration -- and what follows is mere mechanism, the consequences, the algorithm. In this work, we use DNA origami as a highly effective seed for growing DNA tile crystals. Arbitrary information can be put on the seed; it directs the growth of DNA crystals and determines their morphology... much as a genome determines phenotype of an organism... or even, as an idea creates the future.
    [PNAS, 106: 6054-6059, 2009 (6 pages): .pdf, 1.7 MB, supplementary information, 2.6 MB, and appendix, 124 KB. ]
    (Comments in the press: Caltech Press Release, New Scientist, Foresight )
  • Statistical Learning of Arbitrary Computable Classifiers. *
    David Soloveichik.
    It's possible to learn. But there's a conundrum. If the thing you're trying to learn is really complex, you'll need a really complex model. That means you'll need a lot of data to distinguish between the good models and the bad ones. Can you know when you've got enough data? Remarkably, yes... most of the time. But not until you've seen the data -- a distinction that explains why other researchers (with different learning assumptions) have claimed that this is impossible.
    [arXiv preprint: cs.LG/0806.3537v2 (5 pages): .pdf, 84 KB. ]
  • Dynamic Allosteric Control of Noncovalent DNA Catalysis Reactions. *
    David Yu Zhang and Erik Winfree.
    "When you're hot, you're hot; when you're not, you're not." Some find in that phrase solace for fickle fortune. Others see an engineering principle. It's the ideal for a switchable catalyst: when it's ON, it works full speed ahead, but when it's OFF, nothing doing. In this paper, we design and demonstrate a modification of the catalyst for a previously studied DNA hybrization reaction (Zhang et al 2007) that can be turned ON and OFF by an exogenous DNA strand. This is accomplished by designing the catalyst so that it has two conformations -- switchable by the exogenous strand -- one of which hides the toehold critical for initiating the catalytic reaction. I can almost hear those poor DNA molecules' lament.
    [JACS, 130:13921–13926, 2008 (6 pages): .pdf, 850 KB. ]
  • Programmability of Chemical Reaction Networks. *
    Matthew Cook, David Soloveichik, Erik Winfree, and Jehoshua Bruck.
    So you'd like to program chemistry, would you? Well, it's tough. Suppose you already had a handle on the molecular design problem: "No problem," you say, "given a specification for a set of chemical reaction equations, I can construct molecules that react according to plan." Even that's not good enough, because there are limits to what a system can do when the parts are bouncing around like a bag of marbles. So, it's tough. But we can give you some hints. In this paper, we compare stochastic chemical reaction networks to a variety of known models of computation (including one of our eccentric favorites, John Conway's FRACTRAN) and examine the limits of computability. You'll meet molecular counts, probabilities, vector addition systems, primitive recursive functions... and discover just when you can and can't perform uniform computation with chemistry. And all this is for well-mixed solutions with a fixed number of chemical species -- if you want to include polymers and geometrical structures, that's a whole other game. Maybe easier, actually.
    [In Algorithmic Bioprocesses, Springer Berlin Heidelberg, Eds. A. Condon, D. Harel, J. N. Kok, A. Salomaa, E. Winfree, pp. 543-584 (937 KB) (2009).
    Draft preprint (45 pages): .pdf, 824 KB. ]
  • Programming DNA Tube Circumferences. *
    Peng Yin, Rizal F. Hariadi, Sudheer Sahu, Harry M. T. Choi, Sung Ha Park, Thomas H. LaBean, John H. Reif.
    To form a good crystal, must the monomer unit be a compact, well-folded structure? Or can it be a floppy mess? Peng shows that what's really important is the structure in the context of the resulting crystal -- so yes, a monomer can be a floppy mess, and it's still alright! This philosophy leads to the "single-stranded tile" (SST) motif. Because each tile consists of just one strand with four domains, each specifying connectivity in one of the four lattice directions. This allows a straightforward programming of crystals ribbons and tubes with specified width. Lovely.
    [Science, vol 321: 824-826, 2008 (3 pages): online .pdf, 240 KB. (26 pages): online supplementary, 3.6 MB. See the blurb at and Caltech's Press Release. ]

    David's PhD Thesis: Molecules computing : self-assembled nanostructures, molecular automata, and chemical reaction networks. *
    133 pages. California Institute of Technology, Defended May 2008.
    David Soloveichik. Thesis advisor: Erik Winfree.
    We call it the song of the little nightingale. According to the text of the Clauser Prize announcement, this thesis provides seminal contributions to the theory of molecular computing, addressing fundamental questions like: Can we program the self-assembly of complex molecular structures? How can the behavior of molecular systems be made more robust to imperfections and noise? How complex can chemical circuitry become? And, what are the fundamental limits? That's a lovely song to sing.
    [.pdf, 2.5 MB; Caltech ETD.]
  • Error suppression mechanisms for DNA tile self-assembly and their simulation. *
    Kenichi Fujibayashi, David Yu Zhang, Erik Winfree, Satoshi Murata.
    With the pieces floating all over the place, how can self-assembly be restricted just to the active attachment sites where you want it? How can you prevent the pieces from sticking to each other to soon? Well, one possibility is to put a lock on their binding sites, and design the active attachment sites (and no others!) to serve as a key. Here, we present two possible implementations of this concept for the algorithmic self-assembly of DNA tiles. They are analyzed by theory and simulation; surprisingly, for our designs, locking both the input and output sides of tiles provides substantially greater error suppression than just locking one side.
    [Natural Computing, (on line, July 2008) (24 pages): online .pdf, 3.4 MB. ]
  • DNA as a Universal Substrate for Chemical Kinetics. *
    David Soloveichik, Georg Seelig, and Erik Winfree
    If chemistry is programmable, what's the programming language? Well, there are many possibilities... but let's not go with a fad. For well over one hundred years, chemists have been using the elegant and refined mathematical language of mass action chemical kinetics. Traditionally, it has been used descriptively, as a means of describing chemical systems encountered in the real world. As chemical engineers interested in programming chemistry, we wish to use the language of chemical kinetics prescriptively, as a means of describing the behaviors we aim to achieve. It's a rich language, capable of expressing behaviors as varied and sophisticated as stable attractors, oscillators, chaotic dynamics, signal processing and computation -- but much of the evidence for this has come from theoretical studies of chemical reaction networks that are possible in principle, rather than from the study of real systems. That is, for the past hundred years, many intriguing chemical reaction networks existed only in a kind of theoretical dream world, with no physical implementation known. Finally, here we show how any chemical reaction network you dream up can be implemented using systems of DNA logic gates, transforming chemical kinetics from a modeling language into a programming language. Go ahead, make your dreams come true!
    [DNA 14 conference preprint, revised: (10 pages): .pdf, 508 KB.
    In LNCS 5347 pages 57-69 (2009): .pdf, 582 KB. ]
  • A simple DNA gate motif for synthesizing large-scale circuits. *
    Lulu Qian and Erik Winfree
    What is the core of life? Is it, as the poets sometimes say, the ebb and flow of the tides, the in and out of breathing, the ups and downs of fortune? Or is it, as the electrical engineers might say, all in the circuitry, the processing of information, the dynamics of behavior? In this work, we present a simple DNA device, called the "seesaw gate", that should make both poets and engineers happy. It operates by a simple back-and-forth process -- a strand comes in on one side, pushing that side "down" and kicking off the one on the other side as it goes "up". And then visa versa, like a seesaw. Released strands are then free to interact with other seesaw gates, allowing networks to be designed systematically. Remarkably, the ebb and flow of activity in these networks can perform computations of arbitrary complexity in principle -- in fact, we describe a compiler that translates digital logic circuits into functionally equivalent seesaw gate networks, and we argue that the simplicity of the motif should make networks containing thousands of gates possible. If this theoretical proposal pans out experimentally, could it become a core technology for embedding circuitry in synthetic biochemical systems?
    [DNA 14 conference preprint: (13 pages): .pdf, 417 KB.
    In LNCS 5347 pages 70-89 (2009): .pdf, 414 KB. ]
  • Robust Stochastic Chemical Reaction Networks and Bounded Tau-Leaping. *
    David Soloveichik
    Biology is robust. Step on it, squash it, zap it, shake it -- odds are, it will just keep going. What makes biological systems so robust, and what are the implications of this robustness? To study this question, forget the arms and legs and blood and guts -- biological organisms are just chemistry, intricate networks of chemical reactions. What's special about robust chemical reaction networks? That should be a simpler question. In this paper, David makes a surprising connection: robustness allows stochastic chemical reaction networks to be simulated efficiently. In fact, this insight leads to an elegant and fast algorithm for simulating stochastic chemical reaction networks and even results in formal statements limiting how fast a simulator could possibly be. The result provides a new and fundamental framework for analyzing the efficiency of stochastic simulation algorithms. The bottom line is, robustness doesn't only make life easier for the organism, it makes life easier for those of us studying the organism.
    [ Journal of Computational Biology 16(3): 501-522 (2009), (22 pages): .pdf, 364 KB.
    arXiv version, with corrections & additions: cs.CC/0803.1030 (29 pages): .pdf, 427 KB. ]
  • Toward Reliable Algorithmic Self-Assembly of DNA Tiles: A Fixed-Width Cellular Automaton Pattern. *
    Kenichi Fujibayashi, Rizal Hariadi, Sung Ha Park, Erik Winfree, Satoshi Murata.
    In biological morphogenesis, genetic information is expressed as biochemical processes that create an organism. In algorithmic self-assembly, information in DNA is expressed as complex folding and crystallization processes that construct intricately patterned supramolecular objects. Here, we combine several techniques developed in this lab -- Erik's DNA tiles, Paul's DNA origami, Rebecca's ribbon crystals, and Rob's (as yet unpublished) origami seeds -- to self-assemble a fixed-width cellular automaton pattern related to Sierpinski triangles. (One commentator calls them "snakeskin nanobelts", an odd but evocative phrase.) Along the way, we gained some insights about assembly errors and how to prevent them -- about how algorithmic crystals aggregate and grow together and about lattice defect and computation error rates.
    [Nano Letters, 8(17) 1791-1797, 2008 (5 pages): .pdf, 884 KB and supplementary information, 1.9 MB. Highlighted in Nature. Made the cover of Nano Letters!]
  • Engineering Entropy-Driven Reactions and Networks Catalyzed by DNA. *
    David Yu Zhang, Andrew J. Turberfield, Bernard Yurke, and Erik Winfree.
    The entropy of the universe is always increasing. That sounds like a force -- something that keeps increasing can push something else, can't it? The problem, of course, is that using entropy to do work sounds like trying to plow a field by herding stray cats -- it just ends in chaos. But does it have to? Nope: chemists, in fact, are quite familiar with entropy-driven reactions. In this paper, we show how to design systems of DNA molecules with catalytic reactions that are driven by entropy. And it doesn't end in chaos, far from it: we argue that our reactions can be wired together into arbitrary analog or digital circuits, which means they can process information and thus create order. So entropy can drive the production of order? Yup. No wonder the universe is such a beautiful place!
    [Science, 318:1121-1125, 2007 (5 pages) .pdf, 604k and supplementary info, 640k. Chosen as an editor's choice; also see Roy Bar-Ziv's excellent Perspectives essay, an interesting commentary in The Scientist and a nice article in The New Scientist.
  • Computation with Finite Stochastic Chemical Reaction Networks. *
    David Soloveichik, Matthew Cook, Erik Winfree, and Jehoshua Bruck.
    Some people think of chemistry as a bag of colored marbles. Shake really hard. When the marbles hit each other, they change colors, according to rules. So there's a bit of structure, but it's a chaotic mess -- at any given moment, it's anyone's guess what will happen next. Can chemistry do computation, then? At least since Jacob & Monod, and perhaps before, it has been generally recognized that biochemical systems, such as genetic regulatory networks, can operate much like electrical circuits -- the concentration of some chemical species can carry an ON/OFF signal. Arbitrary digital circuit logic can be performed. If enough marbles turn green, we'll say the output was "1". Bennett even realized that if we string the marbles together like a necklace of beads, then Turing-universal computation can be performed. That's strictly more powerful, theoretically, than digital circuits. What we show here is that finite stochastic chemical reaction networks -- bags of marbles without strings -- can also perform Turing-universal computation. Reasonably quickly, too! This result holds if we accept some probability, no matter how small, that the chemistry will produce the wrong output... but remarkably, the result fails if we insist that the chemistry always and without exception produces the correct output. It pays to be tolerant, if even ever so slightly.
    [Natural Computing, (Volume 7, pages 615-633, 2008), (19 pages): online .pdf, 690 KB or Technical Report CaltechPARADISE:2007.ETR085 (19 pages): .pdf, 530 KB. ]

Our Sponsors

First and foremost, Caltech:
the Division of Engineering and Applied Sciences, Computer Science and Computation & Neural Systems; and
Information Science and Technology (IST) Centers for Biological Circuit Design (CBCD) and Physics of Information (CPI).

Active federal and international grants to E. Winfree:

  • NSF CCF: "The Molecular Programming Project", Grant No. 0832824 (co-PIs P.W.K. Rothemund, N. Pierce, R. Murray, J. Bruck, E. Klavins, 8/15/2008-7/31/2013)
  • NSF CCF/EMT/NANO: "Integration of DNA Nanotechnology with Nanoelectronics", Grant No. 0829951 (co-PIs P.W.K. Rothemund, Marc Bockrath, 9/1/2008-8/31/2011)
  • NSF CCF/EMT/MISC: "Behavior Based Molecular Robotics", Grant No. 0829805 (collaborative with Milan Stojanovic, 9/1/2008-8/31/2011)
  • NSF CCF/BIC/EMT: "Toward Large Scale Integrated Nucleic Acid Circuits", Grant No. 0728703 (co-PI Georg Seelig, Caltech. 9/15/2007-8/31/2010)
  • FENA: "Bottom-Up Nanofabrication with DNA Self-Assembly", (subordinate to UCLA FENA Theme 2; co-PI P.W.K Rothemund, Caltech. 9/2006-8/2009)
  • NSF CCF/NANO/EMT: "Toward Universal Bottom-Up Nanofabrication with DNA", Grant No. 0622254 (co-PIs N. Pierce, P.W.K Rothemund, M.W. Bockrath, Caltech; collaborative with B. Yurke, Bell Labs. 10/2006-9/2009)
  • NSF CBET/NIRT: "Active Nanostructures for Nucleic Directed synthesis of Organic Functional Polymers", Grant No. 0608889 (PI N. Seeman, NYU; co-PIs W. Goddard, Caltech; W.-Q. Deng, Caltech; J. Canary, NYU. 10/2006-9/2010)
  • HFSP: "Genetic coding and logical control for RNA molecular switches", Award No. RGY0074/2006-C (PI F. Simmel, U Munich. 5/2006-5/2009 w/ extension)
Expired federal grants to E. Winfree:
  • NASA Astrobiology: "Enzyme-free In-vitro Evolution of DNA Tile Crystals as Model Primitive Organisms", Grant No. NNG06GA50G (Caltech. 11/2005-11/2008)
  • NSF CHE/CBC: "Center for Molecular Cybernetics", Grant No.0533064 (collaborative center lead by M. Stojanovic, Columbia, which is 0533065. 9/2005-8/2008)
  • NSF DMS: "Coarse-Graining DNA Energy Landscapes for the Analysis of Hybridization Kinetics", Grant No. 0506468 (PI N. Pierce, Caltech; co-PI H. Mabuchi, Caltech. 8/2005-7/2008)
  • NSF CCF/BIC/EMT: "Cooperative and Adaptive Behaviors By Molecular Robots", Grant No.0523317 (PI M. Stojanovic, Columbia. 7/2005-6/2008)
  • NSF CCF/NANO/EMT: "Algorithmic error-correction in biologically inspired self-assembly and computation", Grant No. 0523761 (collaborative with A. Goel, Stanford. 7/2005-6/2008)
  • Microsoft: "Using Programmable Stacking Bonds to Combine DNA Origami into Larger, More Complex, Reconfigurable Structures", (co-PI: Paul W.K. Rothemund, Caltech. 6/2007-5/2008)
  • NSF CCF/NANO/EMT: "Controlling Errors in Algorithmic Self-Assembly", Grant No. 0432193 (Caltech. 8/2004-7/2007)
  • NSF CNS/CAREER/PECASE: "Foundations of Autonomous Biomolecular Computation", Grant No. 0093486, with supplement 0536822 (Caltech. 4/2001-3/2006)
  • NSF CBET/NIRT/GOALI: "DNA-Based Nanomechanical devices", Grant No. 0103002 (PI N. Seeman, NYU; co-PI W. Goddard, Caltech. 8/2001-7/2005)
  • ONR YIP: "Biomolecular Computing by In Vitro Transcriptional Networks", Grant No. N000140110813 (Caltech. 5/2001-4/2004)
  • NSF EIA/ITR/SY(CISE): "Biomolecular Computing by DNA/Enzyme Systems", Grant No. 0113443 (co-PI H. Mabuchi, Caltech. 9/2001-8/2004)
  • DARPA BioComputation Contract F30602-01-2-0561 (PI J. Reif, Duke; co-PI N. Seeman, NYU): "Programmable DNA Lattices: Design, Synthesis, and Applications"
  • NASA NRA (PI L. Adleman, USC; co-PI M. Huang, A. Goel, USC): "Biological Aspects of Computation", NASA NRA2-37143

We once received funding from GenTel; we had relationships with Molecubotics, a visionary but struggling Bay Area start-up; and we now interact with Nanorex, the developers of Nanoengineer.

You might notice that our lab no longer takes funding from military agencies. This was a conscious decision, made as my DARPA and ONR grants from 2001 were expiring. My thinking about this is somewhat similar to, but not identical to, Ben Kuiper's reasons. One thing to note is that NSF, FENA, and NASA all interact with military funding agencies, and often funding programs involve joint efforts -- so what difference does it make? Read Ben's essay. For me, there is no question that it feels different, and recognizing and acting on that feeling is important to me.

Any opinions, findings, and conclusions or recommendations expressed in this web site ( are the opinions of some, all, or none of the members of the DNA Group, and do not necessarily reflect the views of the National Science Foundation, Office of Naval Research, DARPA, FENA, GenTel, Molecubotics, Nanorex, or other members of the DNA Group.


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