Training an asymmetric signal perceptron through reinforcement in an artificial chemistry

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis–Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible.

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Cross-Inhibitor: a time-sensitive molecular circuit based on DNA strand displacement

Nucleic Acids Research ◽

10.1093/nar/gkaa835 ◽

2020 ◽

Vol 48 (19) ◽

pp. 10691-10701

Author(s):

Chanjuan Liu ◽

Yuan Liu ◽

Enqiang Zhu ◽

Qiang Zhang ◽

Xiaopeng Wei ◽

...

Keyword(s):

Input Signal ◽

Dna Nanotechnology ◽

Strand Displacement ◽

Important Goal ◽

Molecular Systems ◽

Molecular Programming ◽

Dna Strand Displacement ◽

Input Signals ◽

Biochemical Systems ◽

Dna Strand

Abstract Designing biochemical systems that can be effectively used in diverse fields, including diagnostics, molecular computing and nanomachines, has long been recognized as an important goal of molecular programming and DNA nanotechnology. A key issue in the development of such practical devices on the nanoscale lies in the development of biochemical components with information-processing capacity. In this article, we propose a molecular device that utilizes DNA strand displacement networks and allows interactive inhibition between two input signals; thus, it is termed a cross-inhibitor. More specifically, the device supplies each input signal with a processor such that the processing of one input signal will interdict the signal of the other. Biochemical experiments are conducted to analyze the interdiction performance with regard to effectiveness, stability and controllability. To illustrate its feasibility, a biochemical framework grounded in this mechanism is presented to determine the winner of a tic-tac-toe game. Our results highlight the potential for DNA strand displacement cascades to act as signal controllers and event triggers to endow molecular systems with the capability of controlling and detecting events and signals.

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Noise control for molecular computing

Journal of The Royal Society Interface ◽

10.1098/rsif.2018.0199 ◽

2018 ◽

Vol 15 (144) ◽

pp. 20180199 ◽

Cited By ~ 3

Author(s):

Tomislav Plesa ◽

Konstantinos C. Zygalakis ◽

David F. Anderson ◽

Radek Erban

Keyword(s):

Stochastic Dynamics ◽

Noise Control ◽

Reaction System ◽

Reaction Network ◽

Intrinsic Noise ◽

Biochemical Networks ◽

Mass Action ◽

Mass Action Kinetics ◽

Interdisciplinary Field ◽

Biochemical Systems

Synthetic biology is a growing interdisciplinary field, with far-reaching applications, which aims to design biochemical systems that behave in a desired manner. With the advancement in nucleic-acid-based technology in general, and strand-displacement DNA computing in particular, a large class of abstract biochemical networks may be physically realized using nucleic acids. Methods for systematic design of the abstract systems with prescribed behaviours have been predominantly developed at the (less-detailed) deterministic level. However, stochastic effects, neglected at the deterministic level, are increasingly found to play an important role in biochemistry. In such circumstances, methods for controlling the intrinsic noise in the system are necessary for a successful network design at the (more-detailed) stochastic level. To bridge the gap, the noise-control algorithm for designing biochemical networks is developed in this paper. The algorithm structurally modifies any given reaction network under mass-action kinetics, in such a way that (i) controllable state-dependent noise is introduced into the stochastic dynamics, while (ii) the deterministic dynamics are preserved. The capabilities of the algorithm are demonstrated on a production–decay reaction system, and on an exotic system displaying bistability. For the production–decay system, it is shown that the algorithm may be used to redesign the network to achieve noise-induced multistability. For the exotic system, the algorithm is used to redesign the network to control the stochastic switching, and achieve noise-induced oscillations.

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Enzyme-sharing as a cause of multi-stationarity in signalling systems

Journal of The Royal Society Interface ◽

10.1098/rsif.2011.0664 ◽

2011 ◽

Vol 9 (71) ◽

pp. 1224-1232 ◽

Cited By ~ 47

Author(s):

Elisenda Feliu ◽

Carsten Wiuf

Keyword(s):

Steady State ◽

Chemical Species ◽

Steady States ◽

Mass Action ◽

Signalling Pathways ◽

Necessary Condition ◽

Full Generality ◽

Mass Action Kinetics ◽

Cellular Decision Making ◽

The Stability

Multi-stationarity in biological systems is a mechanism of cellular decision-making. In particular, signalling pathways regulated by protein phosphorylation display features that facilitate a variety of responses to different biological inputs. The features that lead to multi-stationarity are of particular interest to determine, as well as the stability, properties of the steady states. In this paper, we determine conditions for the emergence of multi-stationarity in small motifs without feedback that repeatedly occur in signalling pathways. We derive an explicit mathematical relationship φ between the concentration of a chemical species at steady state and a conserved quantity of the system such as the total amount of substrate available. We show that φ determines the number of steady states and provides a necessary condition for a steady state to be stable—that is, to be biologically attainable. Further, we identify characteristics of the motifs that lead to multi-stationarity, and extend the view that multi-stationarity in signalling pathways arises from multi-site phosphorylation. Our approach relies on mass-action kinetics, and the conclusions are drawn in full generality without resorting to simulations or random generation of parameters. The approach is extensible to other systems.

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Online Learning in a Chemical Perceptron

Artificial Life ◽

10.1162/artl_a_00105 ◽

2013 ◽

Vol 19 (2) ◽

pp. 195-219 ◽

Cited By ~ 12

Author(s):

Peter Banda ◽

Christof Teuscher ◽

Matthew R. Lakin

Keyword(s):

System Design ◽

Discrete Time ◽

Chemical System ◽

Strand Displacement ◽

Input Output ◽

Dna Strand Displacement ◽

Linear Integration ◽

Biological Neuron ◽

Dna Strand ◽

Logic Functions

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and threshold. Both chemical perceptrons can successfully identify all 14 linearly separable two-input logic functions and maintain high robustness against rate-constant perturbations. We suggest that DNA strand displacement could, in principle, provide an implementation substrate for our model, allowing the chemical perceptron to perform reusable, programmable, and adaptable wet biochemical computing.

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