Towards a Biomolecular Learning Machine

Artificial Life, 2013

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 ...

2000

Abstract—Today’s computers are built up from a minimal set of standard pattern recognition opera- tions. Logic gates, such as NAND, are common ex- amples. Biomolecular materials offer an alternative approach, both in terms of variety and context sensi- tivity. Enzymes, the basic switching elements in bio- logical cells, are notable for their ability to discrimi- nate specific molecules in a

Artificial Life

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system.

Essays In Biochemistry, 2016

The design of synthetic gene networks (SGNs) has advanced to the extent that novel genetic circuits are now being tested for their ability to recapitulate archetypal learning behaviours first defined in the fields of machine and animal learning. Here, we discuss the biological implementation of a perceptron algorithm for linear classification of input data. An expansion of this biological design that encompasses cellular ‘teachers’ and ‘students’ is also examined. We also discuss implementation of Pavlovian associative learning using SGNs and present an example of such a scheme and in silico simulation of its performance. In addition to designed SGNs, we also consider the option to establish conditions in which a population of SGNs can evolve diversity in order to better contend with complex input data. Finally, we compare recent ethical concerns in the field of artificial intelligence (AI) and the future challenges raised by bio-artificial intelligence (BI).

The development of synthetic biology has enabled us to make massive progress on biotechnology and to approach research questions from a brand new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo and in silico, have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Noticeably, mathematical models have been increasingly used to predict the behaviors of molecular circuits. The Fernando’s model, which is thought to be one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in the neural network architecture. In this article, we carry out in-depth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also const...

Proceedings of International Conference on Artificial Life and Robotics, 2018

Molecule Robotics have been realized by using bio molecules such as DNA, proteins, for example in Molecular Robotics Research Project [2012-17, JSPS]; bio molecules, such as DNA or proteins are highly structured and they already have a kind of "intelligence" and adapt to environment change. Hence, we have tried to extract their "ability" and induce Intelligence artificially. We have used well-known DNA reactions, Seesaw gate reaction and we found that DNA molecule can sense concentration of single strand input sequence or quasi-input (including mismatched base pairs and point mutations in the sequence) and chooses higher concentration one; this result shows DNA molecule (short DNA sequence) can adapt to its environment.

SIGACT News, 1997

1 Introduction Biomolecular computing is the computing methodology in which biologically important molecules are used as memory. The'aperiodic'nature of these polymeric molecules makes them suitable as memory units [GRY56, WHR+ 87]. Instead of monotonous repeating units of most synthetic polymers (such as polyethylene), certain biological polymers (such as DN A, RN A and protein) ha ve sets of repeating (or information encoding) units, and these units can appear in any order. The use of atomic or molecular- ...

The Journal of Physical Chemistry Letters, 2012

We report a realization of an associative memory signal/information processing system based on simple enzyme-catalyzed biochemical reactions. Optically detected chemical output is always obtained in response to the triggering input, but the system can also "learn" by association, to later respond to the second input if it is initially applied in combination with the triggering input as the "training" step. This second chemical input is not self-reinforcing in the present system, which therefore can later "unlearn" to react to the second input if it is applied several times on its own. Such processing steps realized with (bio)chemical kinetics promise applications of bio-inspired/memory-involving components in "networked" (concatenated) biomolecular processes for multi-signal sensing and complex information processing.

Nature Nanotechnology, 2013

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.

2000

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 gratitude to my beloved wife for being my soul mate, my anchor, and my home. vii