Artificial reaction networks.

In this paper we present a novel method of simulating cellular intelligence, the Artificial Reaction Network (ARN). The ARN can be described as a modular S-System, with some properties in common with other Systems Biology and AI techniques, including Random Boolean Networks, Petri Nets, Artificial Biochemical Networks and Artificial Neural Networks. We validate the ARN against standard biological data, and successfully apply it to simulate cellular intelligence associated with the well-characterized cell signaling network of Escherichia coli chemotaxis. Finally, we explore the adaptability of the ARN, as a means to develop novel AI techniques, by successfully applying the simulated E. coli chemotaxis to a general optimization problem

Exploring aspects of cell intelligence with artificial reaction networks.

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Applications and design of cooperative multi-agent ARN-based systems.

The Artificial Reaction Network (ARN) is an Artificial Chemistry inspired by Cell Signalling Networks (CSNs). Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In previous work, the ARN was applied to the simulation of the chemotaxis pathway of E. coli and to the control of quadrupedal robotic gaits. In this paper, the design and application of ARN-based cell-like agents termed Cytobots are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognizing environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics

Computing with feedforward networks of artificial biochemical neurons

Phosphorylation cycles are a common motif in biological in-tracellular signaling networks. A phosphorylaton cycle can be modeledas an arti cial biochemical neuron, which can be considered as a variantof the arti cial neurons used in neural networks. In this way the arti cialneural network metaphor can be used to model and study intracellularsignaling networks. The question what types of computations can occurin biological intracellular signaling networks leads to the study of thecomputational power of networks of arti cial biochemical neurons. Herewe consider the computational properties of arti cial biochemical neu-rons, based on mass-action kinetics. We also study the computationalpower of feedforward networks of such neurons. As a result, we give analgebraic characterization of the functions computable by these networks

Computing with feedforward networks of artificial biochemical neurons

Phosphorylation cycles are a common motif in biological in-tracellular signaling networks. A phosphorylaton cycle can be modeledas an arti cial biochemical neuron, which can be considered as a variantof the arti cial neurons used in neural networks. In this way the arti cialneural network metaphor can be used to model and study intracellularsignaling networks. The question what types of computations can occurin biological intracellular signaling networks leads to the study of thecomputational power of networks of arti cial biochemical neurons. Herewe consider the computational properties of arti cial biochemical neu-rons, based on mass-action kinetics. We also study the computationalpower of feedforward networks of such neurons. As a result, we give analgebraic characterization of the functions computable by these networks