A Loser-Take-All DNA Circuit

Rodriguez, Kellen R; Sarraf, Namita; Qian, Lulu

doi:10.1021/acssynbio.1c00318

Cited by 12 publications

(11 citation statements)

References 23 publications

(58 reference statements)

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“…A follow-up paper from the same group described a loser-take-all network also. 63 One of the advantages of a WTA-style network is that it doesn't require explicit trainingwhich is often a cumbersome process, especially in chemistryto perform classification as long as the explicit pattern to be recognized is known. The classification process in this motif is akin to comparing two images and tallying if they match, pixel by pixel.…”

Section: Pattern Recognition In the Chemical Medium Through Winner-ta...mentioning

confidence: 99%

A survey on molecular-scale learning systems with relevance to DNA computing

Nagipogu

Reif

2023

Nanoscale

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Section: Pattern Recognition In the Chemical Medium Through Winner-ta...mentioning

confidence: 99%

A survey on molecular-scale learning systems with relevance to DNA computing

Nagipogu

Reif

2023

Nanoscale

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“…For example, the concentration of a target can be sensed and tuned. Furthermore, strategies such as “winner-take-all” or “loser-take-all” can be chosen to obtain only one output from the side of interest ( Figure 5 ) ( Cherry and Qian, 2018 ; Rodriguez et al, 2021 ).…”

Section: Strategies For Functional Modulesmentioning

confidence: 99%

“… Strategies for concentration sensing, a selector that only one signal can be output. ( Cherry and Qian, 2018 ; Rodriguez et al, 2021 ). …”

Section: Strategies For Functional Modulesmentioning

confidence: 99%

DNA-based customized functional modules for signal transformation

Zhang

Sun²

2023

Front. Chem.

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Information on the temporal and spatial scale of cellular molecules in biological systems is crucial for estimating life processes and may be conducive to an improved understanding of disease progression. This intracellular and extracellular information is often difficult to obtain at the same time due to the limitations of accessibility and sensing throughput. DNA is an excellent material for in vivo and in vitro applications, and can be used to build functional modules that can transform bio-information (input) into ATCG sequence information (output). Due to their small volume and highly amenable programming, DNA-based functional modules provide an opportunity to monitor a range of information, from transient molecular events to dynamic biological processes. Over the past two decades, with the advent of customized strategies, a series of functional modules based on DNA networks have been designed to gather different information about molecules, including the identity, concentration, order, duration, location, and potential interactions; the action of these modules are based on the principle of kinetics or thermodynamics. This paper summarizes the available DNA-based functional modules that can be used for biomolecular signal sensing and transformation, reviews the available designs and applications of these modules, and assesses current challenges and prospects.

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“…In contrast, our system proposed here produces probabilistic switching of the entire population into one output state or another. Related work by Rodriguez et al [25] provides an example of an experimental chemical system where the initial minority wins out by design, which is another important aspect of our work, although that system is not probabilistic in nature.…”

Section: Related Work In Molecular Programmingmentioning

confidence: 99%

Operant conditioning of stochastic chemical reaction networks

Arredondo

Lakin²

2022

PLoS Comput Biol

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Adapting one’s behavior to environmental conditions and past experience is a key trait of living systems. In the biological world, there is evidence for adaptive behaviors such as learning even in naturally occurring, non-neural, single-celled organisms. In the bioengineered world, advances in synthetic cell engineering and biorobotics have created the possibility of implementing lifelike systems engineered from the bottom up. This will require the development of programmable control circuitry for such biomimetic systems that is capable of realizing such non-trivial and adaptive behavior, including modification of subsequent behavior in response to environmental feedback. To this end, we report the design of novel stochastic chemical reaction networks capable of probabilistic decision-making in response to stimuli. We show that a simple chemical reaction network motif can be tuned to produce arbitrary decision probabilities when choosing between two or more responses to a stimulus signal. We further show that simple feedback mechanisms from the environment can modify these probabilities over time, enabling the system to adapt its behavior dynamically in response to positive or negative reinforcement based on its decisions. This system thus acts as a form of operant conditioning of the chemical circuit, in the sense that feedback provided based on decisions taken by the circuit form the basis of the learning process. Our work thus demonstrates that simple chemical systems can be used to implement lifelike behavior in engineered biomimetic systems.

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A Loser-Take-All DNA Circuit

Cited by 12 publications

References 23 publications

A survey on molecular-scale learning systems with relevance to DNA computing

A survey on molecular-scale learning systems with relevance to DNA computing

DNA-based customized functional modules for signal transformation

Operant conditioning of stochastic chemical reaction networks

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