Evolving Gene Regulatory Networks with Mobile DNA Mechanisms

This paper uses a recently presented abstract, tuneable Boolean regulatory network model extended to consider aspects of mobile DNA, such as transposons. The significant role of mobile DNA in the evolution of natural systems is becoming increasingly clear. This paper shows how dynamically controlling network node connectivity and function via transposon-inspired mechanisms can be selected for in computational intelligence tasks to give improved performance. The designs of dynamical networks intended for implementation within the slime mould Physarum polycephalum and for the distributed control of a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:1303.722

Investigating the properties of bio-chemical networks of artificial organisms with opposing behaviours

Organisms, be it singled-celled organisms or multi-cellular organisms, are constantly faced with opposing objectives requiring different sets of behaviours. These behaviours can be classified into two, predatory behaviours or anti-prey behaviours, with one set of behaviours causing an opposite effect to the other. A healthy organism aims to achieve its equilibrium state or to be in homeostasis. Homeostasis is achieved when a balance between the two opposing behaviours is created and maintained. This raises some questions: is there an innate mechanism that encodes for these categories of behaviours? Is there also an innate mechanism(s) that resolves conflicts and allows switching between these two opposing behaviours? If we consider artificial organisms as single-celled organisms, how do the organisms’ gene regulatory network, metabolic network and/or signalling network (their biochemical networks) maintain homeostasis of the organisms? This paper investigates the properties of the networks of best evolved artificial organisms, in order to help answer these questions, and guide the evolutionary development of controllers for artificial systems

Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites

Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR) based composites is a typical and crucial property in practical applications. Previous studies show that the abrasion resistance can be calculated by the multiple linear regression model. In our study, considering this relationship can also be described into the non-linear conditions, a Multilayer Feed-forward Neural Networks model with 3 nodes (MLFN-3) was successfully established to describe the relationship between the abrasion resistance and other properties, using 23 groups of data, with the RMS error 0.07. Our studies have proved that Artificial Neural Networks (ANN) model can be used to predict the SSBR-based composites, which is an accurate and robust process

A Theory of Cheap Control in Embodied Systems

We present a framework for designing cheap control architectures for embodied agents. Our derivation is guided by the classical problem of universal approximation, whereby we explore the possibility of exploiting the agent's embodiment for a new and more efficient universal approximation of behaviors generated by sensorimotor control. This embodied universal approximation is compared with the classical non-embodied universal approximation. To exemplify our approach, we present a detailed quantitative case study for policy models defined in terms of conditional restricted Boltzmann machines. In contrast to non-embodied universal approximation, which requires an exponential number of parameters, in the embodied setting we are able to generate all possible behaviors with a drastically smaller model, thus obtaining cheap universal approximation. We test and corroborate the theory experimentally with a six-legged walking machine. The experiments show that the sufficient controller complexity predicted by our theory is tight, which means that the theory has direct practical implications. Keywords: cheap design, embodiment, sensorimotor loop, universal approximation, conditional restricted Boltzmann machineComment: 27 pages, 10 figure

Development and Evolution of Neural Networks in an Artificial Chemistry

We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.Comment: 8 pages LaTeX, style file included, 8 embedded postscript figures. To be published in Proc. of 3rd German Workshop on Artificial Life (GWAL

Bio-inspired programmable matter for space applications

Nowadays, space structures are often designed to serve only a single objective during their mission life, examples are solar sails for propulsion, antennas for communication or shields for protection. By enabling a structure to change its shape and therefore adapt to different mission stages in a single structure, the flexibility of the spacecraft can be increased by greatly decreasing the mass of the entire system. The possibility to obtain such a structure lies in a cellular approach in which every cell is programmable to change its basic properties. The shape change of the global structure can be significantly by adding up these local changes, for example the cells length. An idea presented in this paper is to adapt these basic changeable elements from nature’s heliotropism. Heliotropism is the growth or movement of an organism towards the direction of the sunlight. By changing the turgor pressure between two adjacent cells in the plant’s stem, called motor cells, the stem of the plant flexes. Due to the simplicity of the principle, the movement through pressure change seems perfect for the application on deployable space structures. The design of the adaptive membrane consists of an array of cells which are inflated by employing residual air inflation. Residual air inflation uses the expansion of trapped air inside the structure when subjected to vacuum conditions to inflate the structure. A high packing efficiency and deployment reliability can be achieved by using this passive deployment technique coupled with a multiple unit membrane design. To imitate the turgor pressure change between the motor cells of the plants to space structures, piezoelectric micro pumps are added between two neighbouring cells. The smallest actuator unit in this assembly is therefore the two neighbouring cells and the connected micro pump. The cellular and multiple unit approach makes the structure highly scalable with countless application areas. This paper will outline the design idea and fabrication of the bio-inspired membrane and its application to space missions. Deployment simulations were undertaken in LS-DYNA™ and compared to bench test samples of vacuum inflating circular specimens. A model to control the local elements in order to obtain a desired global shape will be presented as well. The paper will conclude with an overview on the REXUS 13 sounding rocket experiment StrathSat-R which will deploy a prototype of the bio-inspired adaptive membrane in micro gravity in spring 2013

Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Engineering ligand-responsive RNA controllers in yeast through the assembly of RNase III tuning modules

The programming of cellular networks to achieve new biological functions depends on the development of genetic tools that link the presence of a molecular signal to gene-regulatory activity. Recently, a set of engineered RNA controllers was described that enabled predictable tuning of gene expression in the yeast Saccharomyces cerevisiae through directed cleavage of transcripts by an RNase III enzyme, Rnt1p. Here, we describe a strategy for building a new class of RNA sensing-actuation devices based on direct integration of RNA aptamers into a region of the Rnt1p hairpin that modulates Rnt1p cleavage rates. We demonstrate that ligand binding to the integrated aptamer domain is associated with a structural change sufficient to inhibit Rnt1p processing. Three tuning strategies based on the incorporation of different functional modules into the Rnt1p switch platform were demonstrated to optimize switch dynamics and ligand responsiveness. We further demonstrated that these tuning modules can be implemented combinatorially in a predictable manner to further improve the regulatory response properties of the switch. The modularity and tunability of the Rnt1p switch platform will allow for rapid optimization and tailoring of this gene control device, thus providing a useful tool for the design of complex genetic networks in yeast

The evolution of modular artificial neural networks.

This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex systems, particularly when such systems involve multi-domain sensory information which interacts in complex ways with system outputs. The aim in this work is to produce an evolutionary method that allows the structure of the network to evolve from simple to complex as it interacts with a dynamic environment. This new algorithm is therefore based on Incremental Evolution. A simulated model of a legged robot was used as a test-bed for the approach. The algorithm starts with a simple robotic body plan. This then grows incrementally in complexity along with its controlling neural network and the environment it reacts with. The network grows by adding modules to its structure - so the technique may also be termed a Growth Algorithm. Experiments are presented showing the successful evolution of multi-legged gaits and a simple vision system. These are then integrated together to form a complete robotic system. The possibility of the evolution of complex systems is one advantage of the algorithm and it is argued that it represents a possible path towards more advanced artificial intelligence. Applications in Electronics, Computer Science, Mechanical Engineering and Aerospace are also discussed