Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System

The current biochemical information processing systems behave in a predetermined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification baaed on external stimuli would be highly desirable. However, so far, it haa been too challenging to implement these in real or simulated chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports MichaelisMenten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt

Technology as 'Applied Science': a Serious Misconception that Reinforces Distorted and Impoverished Views of Science

The current consideration of technology as 'applied science', this is to say, as something that comes 'after' science, justifies the lack of attention paid to technology in science education. In our paper we question this simplistic view of the science-technology relationship, historically rooted in the unequal appreciation of intellectual and manual work, and we try to show how the absence of the technological dimension in science education contributes to a na¿ ve and distorted view of science which deeply affects the necessary scientific and technological literacy of all citizens

Modular Verification of DNA Strand Displacement Networks via Serializability Analysis

Abstract. DNA strand displacement gates can be used to emulate arbi-trary chemical reactions, and a number of different schemes have been proposed to achieve this. Here we develop modular correctness proofs for strand displacement encodings of chemical reaction networks and show how they may be applied to two-domain strand displacement systems. Our notion of correctness is serializability of interleaved reaction encod-ings, and we infer this global property from the properties of the gates that encode the individual chemical reactions. This allows correctness to be in-ferred for arbitrary systems constructed using these components, and we illustrate this by applying our results to a two-domain implementation of a well-known approximate majority voting system.

Functional Analysis of Large-scale DNA Strand Displacement Circuits

Abstract. We present a method for the analysis of functional properties of large-scale DNA strand displacement (DSD) circuits based on Satisfiability Modulo Theories that enables us to prove the functional correctness of DNA circuit designs for arbitrary inputs, and provides significantly improved scalability and expressivity over existing methods. We implement this method as an extension to the Visual DSD tool, and use it to formalize the behavior of a 4-bit square root circuit, together with the components used for its construction. We show that our method successfully verifies that certain designs function as required and identifies erroneous computations in others, even when millions of copies of a circuit are interacting with each other in parallel. Our method is also applicable in the verification of properties for more general chemical reaction networks.