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KEY POINTS * The notion of computation is none other than a systematic way of processing information, and thus computation is central to the function of biological systems, as it is crucial

for complex man-made machinery. * Whereas biological computing is ubiquitous in living systems, the capacity to engineer new biological computing systems will open the way to an

unprecedented level of rational control over living matter that can be used in all areas of biological engineering and medicine * Current engineering effort is split between biochemical

systems that function in carefully constituted settings and biological systems that operate in living cells or cell ensembles. The two approaches are complementary because biochemical

systems show what is possible in principle, whereas biological systems must deal with the complexity of the host and thus are at this point simpler and smaller in scale. * The construction

of molecular computing systems has been inspired by known theoretical models of computation, such as state machines and logic and analogue circuits. Each model is best suited for a different

class of tasks. * The logic circuits model has spawned a large number of implementations both in the test tube and in living cells, with the basic building blocks comprising DNA oligomers

in the test tube and re-engineered regulatory switches in living cells. Recent achievements include neural-like network with associative memory made of DNA switches, a trainable

ribozyme-based molecular network, a number of distributed logic gates in bacteria and yeast and a cell-type classifier for cancer cell detection and destruction. * Molecular systems inspired

by state machines were implemented with both biochemical and biological approaches, resulting in molecular finite automaton and recombinase-based counter. ABSTRACT The task of information

processing, or computation, can be performed by natural and man-made 'devices'. Man-made computers are made from silicon chips, whereas natural 'computers', such as the

brain, use cells and molecules. Computation also occurs on a much smaller scale in regulatory and signalling pathways in individual cells and even within single biomolecules. Indeed, much of

what we recognize as life results from the remarkable capacity of biological building blocks to compute in highly sophisticated ways. Rational design and engineering of biological computing

systems can greatly enhance our ability to study and to control biological systems. Potential applications include tissue engineering and regeneration and medical treatments. This Review

introduces key concepts and discusses recent progress that has been made in biomolecular computing. Access through your institution Buy or subscribe This is a preview of subscription

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ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS BIOMOLECULAR MODELING THRIVES IN THE AGE

OF TECHNOLOGY Article 20 May 2021 MECHANICAL COMPUTING Article 06 October 2021 SYNTHETIC NEUROMORPHIC COMPUTING IN LIVING CELLS Article Open access 24 September 2022 REFERENCES * Wiener, N.

_Cybernetics_ (MIT Press, 1948). Google Scholar  * Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. _Nature_ 402, C47–C52 (1999). CAS

  PubMed  Google Scholar  * Nurse, P. Life, logic and information. _Nature_ 454, 424–426 (2008). CAS  PubMed  Google Scholar  * Keasling, J. D. Manufacturing molecules through metabolic

engineering. _Science_ 330, 1355–1358 (2010). CAS  PubMed  Google Scholar  * Morelli, A. E. & Thomson, A. W. Tolerogenic dendritic cells and the quest for transplant tolerance. _Nature

Rev. Immunol._ 7, 610–621 (2007). CAS  Google Scholar  * Khademhosseini, A., Langer, R., Borenstein, J. & Vacanti, J. P. Microscale technologies for tissue engineering and biology.

_Proc. Natl Acad. Sci. USA_ 103, 2480–2487 (2006). CAS  PubMed  PubMed Central  Google Scholar  * Bockamp, E. et al. Conditional transgenic mouse models: from the basics to genome-wide sets

of knockouts and current studies of tissue regeneration. _Regen. Med._ 3, 217–235 (2008). CAS  PubMed  Google Scholar  * Kartsson, M., Weber, W. & Fussenegger, M. in _Methods in

Enzymology: Synthetic Biology, Part A. Methods for Part/Device Characterization and Chassis Engineering_ Vol. 497 (ed. Voigt, C.) 239–253 (2011). Google Scholar  * Sugita, M. Functional

analysis of chemical systems _in vivo_ using a logical circuit equivalent. _J. Theor. Biol._ 1, 415–430 (1961). CAS  PubMed  Google Scholar  * Adleman, L. M. Molecular computation of

solutions to combinatorial problems. _Science_ 266, 1021–1024 (1994). THIS PAPER PROVIDES THE FIRST EXPERIMENTALLY SHOWN COMPUTATION WITH DNA MOLECULES. CAS  PubMed  Google Scholar  *

Lipton, R. J. DNA solution of hard computational problems. _Science_ 268, 542–545 (1995). CAS  PubMed  Google Scholar  * Faulhammer, D., Cukras, A. R., Lipton, R. J. & Landweber, L. F.

Molecular computation: RNA solutions to chess problems. _Proc. Natl Acad. Sci. USA_ 97, 1385–1389 (2000). CAS  PubMed  PubMed Central  Google Scholar  * Knight, T. F. & Sussman, G. J. in

_Unconventional Models of Computation_ (eds Calude, C.S., Casti, J. & Dinneen, M.J.) 257–272 (Springer, 1998). Google Scholar  * Benner, S. A. & Sismour, A. M. Synthetic biology.

_Nature Rev. Genet._ 6, 533–543 (2005). CAS  PubMed  Google Scholar  * Baker, D. et al. Engineering life: building a fab for biology. _Sci. Am._ 294, 44–51 (2006). CAS  PubMed  Google

Scholar  * Tan, C. M., Song, H., Niemi, J. & You, L. C. A synthetic biology challenge: making cells compute. _Mol. Biosyst._ 3, 343–353 (2007). CAS  PubMed  Google Scholar  * Xie, Z.,

Wroblewska, L., Prochazka, L., Weiss, R. & Benenson, Y. Multi-input RNAi-based logic circuit for identification of specific cancer cells. _Science_ 333, 1307–1311 (2011). THIS PAPER

DESCRIBES A LARGE-SCALE CONTROL SYSTEM IN HUMAN CELLS THAT IDENTIFIES A CANCEROUS CELL STATE ON THE BASIS OF MULTIPLE ENDOGENOUS MIRNA MARKERS AND PRODUCES A FLUORESCENT OR

APOPTOSIS-INDUCING PROTEIN FOLLOWING POSITIVE DETECTION. CAS  PubMed  Google Scholar  * Shapiro, E. & Benenson, Y. Bringing DNA computers to life. _Sci. Am._ 294, 44–51 (2006). CAS 

PubMed  Google Scholar  * Voigt, C. A. & Keasling, J. D. Programming cellular function. _Nature Chem. Biol._ 1, 304–307 (2005). CAS  Google Scholar  * Nelson, V. P., Nagle, H. T., Irwin,

J. D. & Carroll, B. D. _Digital Logic Circuit Analysis and Design_ (Prentice Hall, 1995). Google Scholar  * Monod, J. & Jacob, F. General conclusions — teleonomic mechanisms in

cellular metabolism, growth, and differentiation. _Cold Spring Harb. Symp. Quant. Biol._ 26, 389–401 (1961). CAS  PubMed  Google Scholar  * Sugita, M. Functional analysis of chemical systems

_in vivo_ using a logical circuit equivalent. V. Molecular biological interpretation of self-reproducing automata theory and chemico-physical interpretation of information in biological

systems. _J. Theor. Biol._ 53, 223–237 (1975). CAS  PubMed  Google Scholar  * Glass, L. & Kauffman, S. A. Logical analysis of continuous, nonlinear biochemical control networks. _J.

Theor. Biol._ 39, 103–129 (1973). CAS  PubMed  Google Scholar  * Kauffman, S., Peterson, C., Samuelsson, B. & Troein, C. Random Boolean network models and the yeast transcriptional

network. _Proc. Natl Acad. Sci. USA_ 100, 14796–14799 (2003). CAS  PubMed  PubMed Central  Google Scholar  * Ptashne, M. Principles of a switch. _Nature Chem. Biol._ 7, 484–487 (2011). CAS 

Google Scholar  * Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in _Escherichia coli_. _Nature_ 403, 339–342 (2000). CAS  PubMed  Google Scholar

  * Ajo-Franklin, C. M. et al. Rational design of memory in eukaryotic cells. _Genes Dev._ 21, 2271–2276 (2007). CAS  PubMed  PubMed Central  Google Scholar  * Sugita, M. Functional analysis

of chemical systems _in vivo_ using a logical circuit equivalent. II. The idea of a molecular automaton. _J. Theor. Biol._ 4, 179–184 (1963). CAS  PubMed  Google Scholar  * Arkin, A. &

Ross, J. Computational functions in biochemical reaction networks. _Biophys. J._ 67, 560–578 (1994). CAS  PubMed  PubMed Central  Google Scholar  * Weiss, R., Homsy, G. E. & Knight, T.

F. in _Evolution as Computation: DIMACS Workshop_ (eds Landweber, L.F. & Winfree, E.) 275–295 (Springer, 1999). Google Scholar  * Tamsir, A., Tabor, J. J. & Voigt, C. A. Robust

multicellular computing using genetically encoded NOR gates and chemical 'wires'. _Nature_ 469, 212–215 (2011). AN EXAMPLE OF COMPLEX, DISTRIBUTED LOGIC CIRCUIT THAT USES UNIVERSAL

TRANSCRIPTION-BASED NOR GATES IS PROVIDED IN THIS PAPER. CAS  PubMed  Google Scholar  * Buchler, N. E., Gerland, U. & Hwa, T. On schemes of combinatorial transcription logic. _Proc.

Natl Acad. Sci. USA_ 100, 5136–5141 (2003). CAS  PubMed  PubMed Central  Google Scholar  * Benenson, Y. RNA-based computation in live cells. _Curr. Opin. Biotechnol._ 20, 471–478 (2009). CAS

  PubMed  PubMed Central  Google Scholar  * Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. _Nature_

434, 1130–1134 (2005). Article  CAS  PubMed  Google Scholar  * Regot, S. et al. Distributed biological computation with multicellular engineered networks. _Nature_ 469, 207–211 (2011). THIS

STUDY DESCRIBE A FLEXIBLE PLATFORM FOR DISTRIBUTED COMPUTATION WITH YEAST STRAINS BASED ON MATING PATHWAY. CAS  PubMed  Google Scholar  * Korn, G. A. & Korn, T. M. _Electronic Analog and

Hybrid Computers_ (McGraw–Hill, 1964). Google Scholar  * Oishi, K. & Klavins, E. Biomolecular implementation of linear I/O systems. _IET Syst. Biol._ 5, 252–260 (2011). CAS  PubMed 

Google Scholar  * McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. _Bull. Math. Biol._ 5, 115–133 (1943). Google Scholar  * Sugita, M. &

Fukuda, N. Functional analysis of chemical systems _in vivo_ using a logical circuit equivalent. III. Analysis using a digital circuit combined with an analogue computer. _J. Theor. Biol._

5, 412–418 (1963). CAS  PubMed  Google Scholar  * Hjelmfelt, A., Weinberger, E. D. & Ross, J. Chemical implementation of neural networks and Turing machines. _Proc. Natl Acad. Sci. USA_

88, 10983–10987 (1991). CAS  PubMed  PubMed Central  Google Scholar  * Qian, L. L., Winfree, E. & Bruck, J. Neural network computation with DNA strand displacement cascades. _Nature_

475, 368–372 (2011). A DNA CIRCUIT IMPLEMENTING A SMALL NEURAL NETWORK WITH ASSOCIATIVE MEMORY PROPERTIES IS DESCRIBED IN THIS PAPER. CAS  PubMed  Google Scholar  * Stahl, W. R. &

Goheen, H. E. Molecular algorithms. _J. Theor. Biol._ 5, 266–287 (1963). CAS  PubMed  Google Scholar  * Baer, R. M. & Martinez, H. M. Automata and biology. _Annu. Rev. Biophys. Bioeng._

3, 255–291 (1974). CAS  PubMed  Google Scholar  * Benenson, Y. et al. Programmable and autonomous computing machine made of biomolecules. _Nature_ 414, 430–434 (2001). THIS WAS THE FIRST

EXPERIMENTAL IMPLEMENTATION OF A FINITE AUTOMATON MODEL OF COMPUTATION WITH MOLECULAR BUILDING BLOCKS. CAS  PubMed  Google Scholar  * Bennett, C.H. The thermodynamics of computation — a

review. _Int. J. Theor. Phys._ 21, 905–940 (1982). CAS  Google Scholar  * Shapiro, E. & Karunaratne, K. S. G. Method and system of computing similar to a Turing machine. US Patent

6266569 (2001). * Rothemund, P. W. K. in _DNA-Based Computers. Proceedings of a DIMACS Workshop_ (eds Lipton, R.J. & Baum, E.B.) 75–120 (1995). Google Scholar  * Ham, T. S., Lee, S. K.,

Keasling, J. D. & Arkin, A. P. Design and construction of a double inversion recombination switch for heritable sequential genetic memory. _PLoS ONE_ 3 (2008). * Head, T. Formal language

theory and DNA — an analysis of the generative capacity of specific recombinant behaviors. _Bull. Math. Biol._ 49, 737–759 (1987). CAS  PubMed  Google Scholar  * Paun, G. & Rozenberg,

G. A guide to membrane computing. _Theor. Comput. Sci._ 287, 73–100 (2002). Google Scholar  * Adamatzky, A. Universal dynamical computation in multidimensional excitable lattices. _Int. J.

Theor. Phys._ 37, 3069–3108 (1998). CAS  Google Scholar  * Harju, T., Petre, I., Rogojin, V. & Rozenberg, G. Patterns of simple gene assembly in ciliates. _Discrete Appl. Math._ 156,

2581–2597 (2008). Google Scholar  * Nowacki, M. et al. RNA-mediated epigenetic programming of a genome-rearrangement pathway. _Nature_ 451, 153–158 (2008). CAS  PubMed  Google Scholar  *

Cardelli, L. in _Algorithmic Bioprocesses_ (eds Condon, A., Harel, D., Kok, J.N., Salomaa, A. & Winfree, E.) 429–462 (Springer, 2009). Google Scholar  * Abelson, H. et al. Amorphous

computing. _Commun. ACM_ 43, 74–82 (2000). Google Scholar  * Rackham, O. & Chin, J. W. Cellular logic with orthogonal ribosomes. _J. Am. Chem. Soc._ 127, 17584–17585 (2005). CAS  PubMed

  Google Scholar  * Desilva, A. P., Gunaratne, H. Q. N. & McCoy, C. P. A molecular photoionic AND gate based on fluorescent signalling. _Nature_ 364, 42–44 (1993). Google Scholar  *

Macdonald, J. et al. Medium scale integration of molecular logic gates in an automaton. _Nano Lett._ 6, 2598–2603 (2006). CAS  PubMed  Google Scholar  * Pei, R. J., Matamoros, E., Liu, M.

H., Stefanovic, D. & Stojanovic, M. N. Training a molecular automaton to play a game. _Nature Nanotechnol._ 5, 773–777 (2010). THIS PAPER DESCRIBES AN ADVANCED DNAZYME-BASED CIRCUITRY

THAT CAN BE PROGRAMMED TO PERFORM DIFFERENT TASKS ACCORDING TO MOLECULAR 'TRAINING INSTRUCTIONS' IT HAS RECEIVED. CAS  Google Scholar  * Elbaz, J. et al. DNA computing circuits

using libraries of DNAzyme subunits. _Nature Nanotechnol._ 5, 417–422 (2010). CAS  Google Scholar  * Yin, P., Choi, H. M. T., Calvert, C. R. & Pierce, N. A. Programming biomolecular

self-assembly pathways. _Nature_ 451, 318–322 (2008). CAS  PubMed  Google Scholar  * Zhang, D. Y., Turberfield, A. J., Yurke, B. & Winfree, E. Engineering entropy-driven reactions and

networks catalyzed by DNA. _Science_ 318, 1121–1125 (2007). CAS  PubMed  Google Scholar  * Seelig, G., Soloveichik, D., Zhang, D. Y. & Winfree, E. Enzyme-free nucleic acid logic

circuits. _Science_ 314, 1585–1588 (2006). CAS  PubMed  Google Scholar  * Qian, L. L. & Winfree, E. Scaling up digital circuit computation with DNA strand displacement cascades.

_Science_ 332, 1196–1201 (2011). IN THIS PAPER, A LARGE-SCALE DNA CIRCUIT THAT IS BASED ON STRAND DISPLACEMENT REACTIONS IS DESCRIBED. CAS  PubMed  Google Scholar  * Lewandoski, M.

Conditional control of gene expression in the mouse. _Nature Rev. Genet._ 2, 743–755 (2001). CAS  PubMed  Google Scholar  * Lambowitz, A. M. & Zimmerly, S. Group II introns: mobile

ribozymes that invade DNA. _Cold Spring Harb. Perspect. Biol._ 3, a003616 (2011). PubMed  PubMed Central  Google Scholar  * Jaenisch, R. & Bird, A. Epigenetic regulation of gene

expression: how the genome integrates intrinsic and environmental signals. _Nature Genet._ 33, 245–254 (2003). CAS  PubMed  Google Scholar  * Haynes, K. A. & Silver, P. A. Synthetic

reversal of epigenetic silencing. _J. Biol. Chem._ 286, 27176–27182 (2011). CAS  PubMed  PubMed Central  Google Scholar  * Davidson, E. A. & Ellington, A. D. Synthetic RNA circuits.

_Nature Chem. Biol._ 3, 23–28 (2007). CAS  Google Scholar  * Isaacs, F. J., Dwyer, D. J. & Collins, J. J. RNA synthetic biology. _Nature Biotech._ 24, 545–554 (2006). CAS  Google Scholar

  * Win, M. N. & Smolke, C. D. Higher-order cellular information processing with synthetic RNA devices. _Science_ 322, 456–460 (2008). CAS  PubMed  PubMed Central  Google Scholar  *

Lucks, J. B., Qi, L., Mutalik, V. K., Wang, D. & Arkin, A. P. Versatile RNA-sensing transcriptional regulators for engineering genetic networks. _Proc. Natl Acad. Sci. USA_ 108,

8617–8622 (2011). THIS STUDY DESCRIBED AND TESTED, IN THE ENGINEERING CONTEXT, AN ANTISENSE RNA-BASED REGULATORY MECHANISM IN BACTERIA. CAS  PubMed  PubMed Central  Google Scholar  * Culler,

S. J., Hoff, K. G. & Smolke, C. D. Reprogramming cellular behavior with RNA controllers responsive to endogenous proteins. _Science_ 330, 1251–1255 (2010). CAS  PubMed  PubMed Central 

Google Scholar  * Rinaudo, K. et al. A universal RNAi-based logic evaluator that operates in mammalian cells. _Nature Biotech._ 25, 795–801 (2007). CAS  Google Scholar  * Leisner, M.,

Bleris, L., Lohmueller, J., Xie, Z. & Benenson, Y. Rationally designed logic integration of regulatory signals in mammalian cells. _Nature Nanotechnol._ 5, 666–670 (2010). CAS  Google

Scholar  * Tu, K. C. & Bassler, B. L. Multiple small RNAs act additively to integrate sensory information and control quorum sensing in _Vibrio harveyi_. _Genes Dev._ 21, 221–233 (2007).

CAS  PubMed  PubMed Central  Google Scholar  * Levine, E., Zhang, Z., Kuhlman, T. & Hwa, T. Quantitative characteristics of gene regulation by small RNA. _PLoS Biol._ 5, 1998–2010

(2007). CAS  Google Scholar  * Niazov, T., Baron, R., Katz, E., Lioubashevski, O. & Willner, I. Concatenated logic gates using four coupled biocatalysts operating in series. _Proc. Natl.

Acad. Sci. USA_ 103, 17160–17163 (2006). CAS  PubMed  PubMed Central  Google Scholar  * Zhou, J., Arugula, M. A., Halamek, J., Pita, M. & Katz, E. Enzyme-based NAND and NOR logic gates

with modular design. _J. Phys. Chem. B_ 113, 16065–16070 (2009). CAS  PubMed  Google Scholar  * Privman, V., Strack, G., Solenov, D., Pita, M. & Katz, E. Optimization of enzymatic

biochemical logic for noise reduction and scalability: how many biocomputing gates can be interconnected in a circuit? _J. Phys. Chem. B_ 112, 11777–11784 (2008). CAS  PubMed  Google Scholar

  * Wagner, N., Alesebi, S. & Ashkenasy, G. How symmetry and order affect logic operations and computation in catalytic chemical networks. _J. Comput. Theor. Nanosci._ 8, 471–480 (2011).

CAS  Google Scholar  * Brent, R. & Ptashne, M. A eukaryotic transcriptional activator bearing the DNA specificity of a prokaryotic repressor. _Cell_ 43, 729–736 (1985). CAS  PubMed 

Google Scholar  * Kramer, B. & Fussenegger, M. in _Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics_ (Wiley, 2005). Google Scholar  * Kramer, B. P., Fischer, C. &

Fussenegger, M. BioLogic gates enable logical transcription control in mammalian cells. _Biotechnol. Bioeng._ 87, 478–484 (2004). THIS WORK DEMONSTRATED A THREE-INPUT LOGIC GATE ON A

MAMMALIAN PROMOTER. CAS  PubMed  Google Scholar  * Cox, R. S., Surette, M. G. & Elowitz, M. B. Programming gene expression with combinatorial promoters. _Mol. Syst. Biol._ 3, 145 (2007).

PubMed  PubMed Central  Google Scholar  * Guet, C. C., Elowitz, M. B., Hsing, W. H. & Leibler, S. Combinatorial synthesis of genetic networks. _Science_ 296, 1466–1470 (2002). CAS 

PubMed  Google Scholar  * Hooshangi, S., Thiberge, S. & Weiss, R. Ultrasensitivity and noise propagation in a synthetic transcriptional cascade. _Proc. Natl Acad. Sci. USA_ 102,

3581–3586 (2005). CAS  PubMed  PubMed Central  Google Scholar  * Ellis, T., Wang, X. & Collins, J. J. Diversity-based, model-guided construction of synthetic gene networks with predicted

functions. _Nature Biotech._ 27, 465–471 (2009). CAS  Google Scholar  * Tabor, J. J. et al. A synthetic genetic edge detection program. _Cell_ 137, 1272–1281 (2009). PubMed  PubMed Central

  Google Scholar  * Wang, B. J., Kitney, R. I., Joly, N. & Buck, M. Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology. _Nature Commun._ 2,

18 Oct 2011 (doi: 10.1038/ncomms1516). * Ye, H. F., Daoud- El Baba, M., Peng, R. W. & Fussenegger, M. A synthetic optogenetic transcription device enhances blood-glucose homeostasis in

mice. _Science_ 332, 1565–1568 (2011). CAS  PubMed  Google Scholar  * Nissim, L. & Bar-Ziv, R. H. A tunable dual-promoter integrator for targeting of cancer cells. _Mol. Syst. Biol._ 6,

444 (2010). PubMed  PubMed Central  Google Scholar  * Grunberg, R. & Serrano, L. Strategies for protein synthetic biology. _Nucleic Acids Res._ 38, 2663–2675 (2010). PubMed  PubMed

Central  Google Scholar  * Park, S. H., Zarrinpar, A. & Lim, W. A. Rewiring MAP kinase pathways using alternative scaffold assembly mechanisms. _Science_ 299, 1061–1064 (2003). CAS 

PubMed  Google Scholar  * Skerker, J. M. et al. Rewiring the specificity of two-component signal transduction systems. _Cell_ 133, 1043–1054 (2008). CAS  PubMed  PubMed Central  Google

Scholar  * Turner, B. M. Cellular memory and the histone code. _Cell_ 111, 285–291 (2002). CAS  PubMed  Google Scholar  * Appella, E. & Anderson, C. W. Signaling to p53: breaking the

posttranslational modification code. _Pathol. Biol._ 48, 227–245 (2000). CAS  PubMed  Google Scholar  * Dueber, J. E., Yeh, B. J., Chak, K. & Lim, W. A. Reprogramming control of an

allosteric signaling switch through modular recombination. _Science_ 301, 1904–1908 (2003). CAS  PubMed  Google Scholar  * Grilly, C., Stricker, J., Pang, W. L., Bennett, M. R. & Hasty,

J. A synthetic gene network for tuning protein degradation in _Saccharomyces cerevisiae_. _Mol. Syst. Biol._ 3, 127 (2007). PubMed  PubMed Central  Google Scholar  * Wang, H. Proving

theorems by pattern recognition I. _Commun. ACM_ 40, 1–42 (1961). Google Scholar  * Robinson, R. M. Undecidability and non-periodicity for tilings of plane. _Invent. Math._ 12, 177–209

(1971). Google Scholar  * Winfree, E. _Algorithmic Self-assembly of DNA_. Thesis, California Institute of Technology (1998). Google Scholar  * Winfree, E., Liu, F. R., Wenzler, L. A. &

Seeman, N. C. Design and self-assembly of two-dimensional DNA crystals. _Nature_ 394, 539–544 (1998). CAS  PubMed  Google Scholar  * Mao, C. D., LaBean, T. H., Reif, J. H. & Seeman, N.

C. Logical computation using algorithmic self-assembly of DNA triple-crossover molecules. _Nature_ 407, 493–496 (2000). CAS  PubMed  Google Scholar  * Schulman, R. & Winfree, E.

Synthesis of crystals with a programmable kinetic barrier to nucleation. _Proc. Natl Acad. Sci. USA_ 104, 15236–15241 (2007). CAS  PubMed  PubMed Central  Google Scholar  * Barish, R. D.,

Schulman, R., Rothemund, P. W. K. & Winfree, E. An information-bearing seed for nucleating algorithmic self-assembly. _Proc. Natl Acad. Sci. USA_ 106, 6054–6059 (2009). THIS IS THE MOST

COMPLEX DNA-TILING COMPUTATION TO DATE, IMPLEMENTING A COUNTER TO 17. CAS  PubMed  PubMed Central  Google Scholar  * Chen, J. H. & Seeman, N. C. Synthesis from DNA of a molecule with the

connectivity of a cube. _Nature_ 350, 631–633 (1991). CAS  PubMed  Google Scholar  * Rothemund, P. W. K. Folding DNA to create nanoscale shapes and patterns. _Nature_ 440, 297–302 (2006).

CAS  PubMed  Google Scholar  * Douglas, S. M. et al. Self-assembly of DNA into nanoscale three-dimensional shapes. _Nature_ 459, 414–418 (2009). CAS  PubMed  PubMed Central  Google Scholar 

* Lund, K. et al. Molecular robots guided by prescriptive landscapes. _Nature_ 465, 206–210 (2010). CAS  PubMed  PubMed Central  Google Scholar  * Delebecque, C. J., Lindner, A. B., Silver,

P. A. & Aldaye, F. A. Organization of intracellular reactions with rationally designed RNA assemblies. _Science_ 333, 470–474 (2011). THIS PAPER IS AN EXAMPLE OF NUCLEIC ACIDS

NANOTECHNOLOGY USE IN LIVING CELLS FOR IMPROVED EFFICIENCY OF A BIOPRODUCTION PATHWAY. CAS  PubMed  Google Scholar  * Douglas, S. M., Bachelet, I. & Church, G. M. A logic-gated nanorobot

for targeted transport of molecular payloads. _Science_ 335, 831–834 (2012). THIS STUDY COMBINES DNA NANOTECHNOLOGY WITH THE LOGIC GATE CONCEPT FOR SELECTIVE CELL TARGETING. CAS  PubMed 

Google Scholar  * Smith, W. D. in _DNA-Based Computers. Proceedings of a DIMACS Workshop_ (eds Lipton, R.J. & Baum, E.B.) 121–186 (American Mathematical Society, 1995). Google Scholar  *

Sakamoto, K. et al. State transitions by molecules. _Biosystems_ 52, 81–91 (1999). CAS  PubMed  Google Scholar  * Benenson, Y., Adar, R., Paz-Elizur, T., Livneh, Z. & Shapiro, E. DNA

molecule provides a computing machine with both data and fuel. _Proc. Natl Acad. Sci. USA_ 100, 2191–2196 (2003). CAS  PubMed  PubMed Central  Google Scholar  * Soreni, M., Yogev, S.,

Kossoy, E., Shoham, Y. & Keinan, E. Parallel biomolecular computation on surfaces with advanced finite automata. _J. Am. Chem. Soc._ 127, 3935–3943 (2005). CAS  PubMed  Google Scholar  *

Adar, R. et al. Stochastic computing with biomolecular automata. _Proc. Natl Acad. Sci. USA_ 101, 9960–9965 (2004). CAS  PubMed  PubMed Central  Google Scholar  * Benenson, Y., Gil, B.,

Ben-Dor, U., Adar, R. & Shapiro, E. An autonomous molecular computer for logical control of gene expression. _Nature_ 429, 423–429 (2004). THIS PAPER SHOWS THE FIRST BIOCHEMICAL

PROTOTYPE OF A LARGE-SCALE CONTROL CIRCUIT COMBINING SOPHISTICATED SENSING, COMPUTATION AND ACTUATION MODULES TO MAKE DECISIONS BASED ON MULTIPLE ENVIRONMENTAL CUES. CAS  PubMed  Google

Scholar  * Gil, B., Kahan-Hanum, M., Skirtenko, N., Adar, R. & Shapiro, E. Detection of multiple disease indicators by an autonomous biomolecular computer. _Nano Lett._ 11, 2989–2996

(2011). CAS  PubMed  Google Scholar  * Ran, T., Kaplan, S. & Shapiro, E. Molecular implementation of simple logic programs. _Nature Nanotechnol._ 4, 642–648 (2009). CAS  Google Scholar 

* Friedland, A. E. et al. Synthetic gene networks that count. _Science_ 324, 1199–1202 (2009). AN IMPRESSIVE EXAMPLE OF RECOMBINASE-BASED CIRCUITRY IS DISCUSSED IN THIS PAPER THAT CAN

IRREVERSIBLY RECORD UP TO THREE CONSECUTIVE EVENTS. CAS  PubMed  PubMed Central  Google Scholar  * Wang, Z. G., Elbaz, J., Remacle, F., Levine, R. D. & Willner, I. All-DNA finite-state

automata with finite memory. _Proc. Natl Acad. Sci. USA_ 107, 21996–22001 (2010). CAS  PubMed  PubMed Central  Google Scholar  * Dayarian, A., Chaves, M., Sontag, E. D. & Sengupta, A. M.

Shape, size, and robustness: feasible regions in the parameter space of biochemical networks. _PLoS Comput. Biol._ 5, e1000256 (2009). PubMed  PubMed Central  Google Scholar  * Kim, H. D.

& O'Shea, E. K. A quantitative model of transcription factor-activated gene expression. _Nature Struct. Mol. Biol._ 15, 1192–1198 (2008). CAS  Google Scholar  * Rosenfeld, N.,

Young, J. W., Alon, U., Swain, P. S. & Elowitz, M. B. Gene regulation at the single-cell level. _Science_ 307, 1962–1965 (2005). CAS  PubMed  Google Scholar  * Gregor, T., Tank, D. W.,

Wieschaus, E. F. & Bialek, W. Probing the limits to positional information. _Cell_ 130, 153–164 (2007). CAS  PubMed  PubMed Central  Google Scholar  * Scott, M., Hwa, T. & Ingalls,

B. Deterministic characterization of stochastic genetic circuits. _Proc. Natl Acad. Sci. USA_ 104, 7402–7407 (2007). CAS  PubMed  PubMed Central  Google Scholar  * Ando, H., Sinha, S.,

Storni, R. & Aihara, K. Synthetic gene networks as potential flexible parallel logic gates. _Europhys. Lett._ 93, 50001 (2011). Google Scholar  * Klavins, E. in _Proc. 49th IEEE Conf.

Decision Control_ 2547–2553 (2010). Google Scholar  * Bleris, L. et al. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. _Mol. Syst. Biol._

7, 519 (2011). PubMed  PubMed Central  Google Scholar  * Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. _Nature_ 403, 335–338 (2000). CAS 

PubMed  Google Scholar  * Tigges, M., Marquez-Lago, T. T., Stelling, J. & Fussenegger, M. A tunable synthetic mammalian oscillator. _Nature_ 457, 309–312 (2009). CAS  PubMed  Google

Scholar  * Stricker, J. et al. A fast, robust and tunable synthetic gene oscillator. _Nature_ 456, 516–519 (2008). CAS  PubMed  PubMed Central  Google Scholar  * Becskei, A. & Serrano,

L. Engineering stability in gene networks by autoregulation. _Nature_ 405, 590–593 (2000). CAS  PubMed  Google Scholar  * Marchisio, M. A. & Stelling, J. Computational design of

synthetic gene circuits with composable parts. _Bioinformatics_ 24, 1903–1910 (2008). CAS  PubMed  Google Scholar  * Beal, J., Lu, T. & Weiss, R. Automatic compilation from high-level

biologically-oriented programming language to genetic regulatory networks. _PLoS ONE_ 6, e22490 (2011). CAS  PubMed  PubMed Central  Google Scholar  * Canton, B., Labno, A. & Endy, D.

Refinement and standardization of synthetic biological parts and devices. _Nature Biotech._ 26, 787–793 (2008). CAS  Google Scholar  * Turing, A. M. On computable numbers, with an

application to the Entscheidungsproblem. _Proc. Lond. Math. Soc._ 42, 230–265 (1937). Google Scholar  Download references ACKNOWLEDGEMENTS The author's research is funded by ETH Zurich,

a US National Institutes of Health and National Cancer Institute grant (5R01CA155320) and a European Research Council starting grant. He wishes to thank F. Rudolf for discussions. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH Zurich), Mattenstrasse 26, Basel, 4058, Switzerland Yaakov

Benenson Authors * Yaakov Benenson View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Yaakov Benenson. ETHICS

DECLARATIONS COMPETING INTERESTS The author holds a pending patent application covering some of his work discussed in this Review. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION S1

(FIGURE) Tiling implementation of Turing machine computation. (PDF 164 kb) RELATED LINKS RELATED LINKS FURTHER INFORMATION Author's homepage GLOSSARY * Input A unit of information that

is processed by a computing system, or a collection of all such units. * Output A unit of information that is produced as a result of computation, or a collection of all such units. *

Mapping A specific relationship between the inputs and the outputs of computation expressed as a mathematical function or as a computation procedure (program); it can be formally described

as a collection of all pairs ([inputs], [outputs]), where [inputs] is a specific combination of legitimate inputs, and [outputs] is a result of computation for this combination. * Models of

computation Specific approaches towards implementing information-processing tasks; they are usually required to be universal. * Molecular computer A design framework that enables

construction of molecular systems that are capable of implementing desired input–output mappings between molecular inputs and outputs or a specific implementation of such a system. *

Autonomous systems A molecular computer that does not require external interference apart from initializing the computer components and (optionally) the inputs. * Gene circuit A set of

engineered genes that can be implanted into a living cell and, following their expression, can form functional biological networks comprising these genes and their products (RNA and

protein). * Logic functions Mappings between multiple inputs and a single output, where both the inputs and the output can only take values of zero and one (or false and true). * Logic

circuits Specific arrangements of logic gates that can compute specific logic functions. * Universal set A collection of gate types that can be used to compute any logic function. *

Universal gates Gates of a single type that can be used to implement any conceivable logic function. * Normal form A standard way of expressing logic functions that can be used to represent

any logic function. * Analogue circuits Arrangements of gates that compute continuous-value functions, such as multiplication. * State machines A class of models of computation that comprise

a tape of symbols as data storage and a controller that scans the tape, reads and writes symbols and modifies its own state based on specific transition rules. * Finite automata A class of

state machines that process strings from left to right. The controller scans symbols one by one, changing the state at each step, depending on the current state, according to the rule

<current state>, <current symbol> <next state> (and move to the next symbol). * Belousov–Zhabotinsky reactions A set of coupled chemical processes that do not reach

equilibrium for extended periods of time and instead exhibit oscillations or other dynamic features * Distributed computing A computer architecture that uses multiple stand-alone computing

units that interact with each other to accomplish a common computational task. * Amorphous computing An extreme case of distributed computing with very large number of simple computing units

that can move in space and only interact locally. * McCulloch–Pitts neuron An abstract gate embodying some features of neuron cells, which calculates a weighted sum of the inputs and

generates an output of one when this sum is above a certain threshold. * Logic gate A small computational unit that implements a fixed logic function such as AND between one or two, but

sometimes more, inputs. * Strand displacement A chemical process whereby a single-stranded DNA oligonucleotide replaces the shorter of two strands in a partially double-stranded DNA duplex.

This starts with the oligonucleotide binding to the single-stranded section (the 'toehold') and goes to completion because the new duplex has a higher thermodynamic stability. *

Hopfield neural network A class of artificial neural networks in which the individual 'neurons' mutually excite and inhibit each other. The network can be trained with specific

input sets (patterns) such that each pattern corresponds to a steady state. After the network has been trained, any new input pattern will cause the network to convert to the state that is

closest to this pattern, implementing memory by association. * Open-loop processes Physical processes or computations whose outputs do not affect the inputs. RIGHTS AND PERMISSIONS Reprints

and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Benenson, Y. Biomolecular computing systems: principles, progress and potential. _Nat Rev Genet_ 13, 455–468 (2012).

https://doi.org/10.1038/nrg3197 Download citation * Published: 12 June 2012 * Issue Date: July 2012 * DOI: https://doi.org/10.1038/nrg3197 SHARE THIS ARTICLE Anyone you share the following

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