Scaling down DNA circuits with competitive neural networks

Genot, Anthony J.; Fujii, Teruo; Rondelez, Yannick

doi:10.1098/rsif.2013.0212

Cited by 51 publications

(52 citation statements)

References 72 publications

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“…As a further example, it is known that competitive learning in winner-takes-all neural networks can be implemented very concisely using molecular computing architectures, because direct competition for catalytic resources can replace mutually inhibitory connections between all pairs of neurons [51,72]. Thus, the study of biochemical learning devices may be of philosophical, as well as practical, interest.…”

Section: Discussionmentioning

confidence: 99%

Design of a biochemical circuit motif for learning linear functions

Lakin

Minnich

Lane

et al. 2014

J. R. Soc. Interface.

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Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.

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Section: Discussionmentioning

confidence: 99%

Design of a biochemical circuit motif for learning linear functions

Lakin

Minnich

Lane

et al. 2014

J. R. Soc. Interface.

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“…For instance, replacing a direct activation (A promotes B) by an indirect one (A promotes C which promotes B) will result in more than just some latency in the activation: there will also be a latency in all responses of the strand B, meaning that its concentration will also decrease slower (figure 3). Additionally, enzymes may get saturated, which would change the reaction rates of other parts of the system in ways difficult to apprehend for the human mind [21,33]. Those problems can be avoided by modifying parameters in the system (strand stability, template concentrations, enzyme activities, etc.…”

Section: The Dynamic Network Assembly Toolboxmentioning

confidence: 99%

“…This phenomenon was leveraged by Genot et al to perform efficient computation [32]. In the DNA toolbox, enzymes are perfect examples of such shared resources [33]. Because the standard experimental parameters are set to avoid saturation (and thus competition) as much as possible, the activity of one enzyme has to be reduced by an order of magnitude.…”

Section: Saturation-based Effectsmentioning

confidence: 99%

“…VISUALDSD is also useful to highlight counterintuitive phenomena such as the winner-take-all effect [19,32,33]. DSD is also the foundation of Qian and Winfree's seesaw gate [1,2], which they used to build complex logical circuits.…”

Section: Related Workmentioning

confidence: 99%

“…Saturation is toggled on by default, both to be closer to the actual in vitro behaviour and to help design systems specifically using this phenomenon, such as in a winner-take-all system [19,32,33].…”

Section: Mathematical Modelmentioning

confidence: 99%

See 2 more Smart Citations

Computer-assisted design for scaling up systems based on DNA reaction networks

Aubert

Mosca

Fujii

et al. 2014

J. R. Soc. Interface.

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In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the possibility to use CMA-ES, a state-of-the-art optimization algorithm that will automatically evolve parameters chosen by the user to try to match a specified behaviour. Finally, because all possible functionality cannot be captured by a single software, DACCAD includes the possibility to export a system in the synthetic biology markup language, a widely used language for describing biological reaction systems. DACCAD can be downloaded online at http://www.yannick-rondelez.com/downloads/.

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Molecular Computation for Molecular Classification

et al. 2023

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DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal‐processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low‐cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high‐tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

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Scaling down DNA circuits with competitive neural networks

Cited by 51 publications

References 72 publications

Design of a biochemical circuit motif for learning linear functions

Design of a biochemical circuit motif for learning linear functions

Computer-assisted design for scaling up systems based on DNA reaction networks

Molecular Computation for Molecular Classification

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