Review Article | Published:

Biomolecular computing systems: principles, progress and potential

Nature Reviews Genetics volume 13, pages 455468 (2012) | Download Citation

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.

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.

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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.

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  1. Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology (ETH Zurich), Mattenstrasse 26, 4058 Basel, Switzerland.

    • Yaakov Benenson

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The author holds a pending patent application covering some of his work discussed in this Review.

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Correspondence to Yaakov Benenson.

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    Tiling implementation of Turing machine computation.

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.

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https://doi.org/10.1038/nrg3197