99 research outputs found
Molecular Computing: from conformational pattern recognition to complex processing networks
Natural biomolecular systems process information in a radically different manner than programmable machines. Conformational interactions, the basis of specificity and self-assembly, are of key importance. A gedanken device is presented that illustrates how the fusion of information through conformational self-organization can serve to enhance pattern processing at the cellular level. The device is used to highlight general features of biomolecular information processing. We briefly outline a simulation system designed to address the manner in which conformational processing interacts with kinetic and higher level structural dynamics in complex biochemical networks. Virtual models that capture features of biomolecular information processing can in some instances have artificial intelligence value in their own right and should serve as design tools for future computers built from real molecules
Information-Theoretic Aspects of Control in a Bio-Hybrid Robot Device
Information processing in natural systems radically differs from current information technology. This difference is particularly apparent in the area of robotics, where both organisms and artificial devices face a similar challenge: the need to act in real time in a complex environment and to do so with computing resources severely limited by their size and power consumption. The formidable gap between artificial and natural systems in terms of information processing capability motivates research into the biological modes of information processing. Such undertakings, however, are hampered by the fact that nature directly exploits the manifold physical characteristics of its computing substrates, while available theoretical tools in general ignore the underlying implementation. Here we sketch the concept of bounded computability in an attempt towards reconciling the information-theoretic perspective with the need to take the material basis of information processing into account. We do so in the context of Physarum polycephalum as a naturally evolved information processor and the use of this organism as an integral component of a robot controller
Self-adaptive Scouting---Autonomous Experimentation for Systems Biology
We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIV-immune system interaction. Finally, the difference between scouting and optimization is discussed
Robot control with biological cells
At present there exists a large gap in size, performance, adaptability and robustness between natural and artificial information processors for performing coherent perception-action tasks under real-time constraints. Even the simplest organisms have an enviable capability of coping with an unknown dynamic environment. Robots, in contrast, are still clumsy if confronted with such complexity. This paper presents a bio-hybrid architecture developed for exploring an alternate approach to the control of autonomous robots. Circuits prepared from amoeboid plasmodia of the slime mold Physarum polycephalum are interfaced with an omnidirectional hexapod robot. Sensory signals from the macro-physical environment of the robot are transduced to cellular scale and processed using the unique micro-physical features of intracellular information processing. Conversely, the response form the cellular computation is amplified to yield a macroscopic output action in the environment mediated through the robot’s actuators
Computing Substrates and Life
Alive matter distinguishes itself from inanimate matter by actively maintaining a high degree of inhomogenous organisation. Information processing is quintessential to this capability. The present paper inquires into the degree to which the information processing aspect of living systems can be abstracted from the physical medium of its implementation. Information processing serving to sustain the complex organisation of a living system faces both the harsh reality of real-time requirements and severe constraints on energy and material that can be expended on the task. This issue is of interest for the potential scope of Artificial Life and its interaction with Synthetic Biology. It is pertinent also for information technology. With regard to the latter aspect, the use of a living cell in a robot control architecture is considered
An Invariance Principle for Maintaining the Operating Point of a Neuron
Sensory neurons adapt to changes in the natural statistics of their environments through processes such as gain control and firing threshold adjustment. It has been argued that neurons early in sensory pathways adapt according to information-theoretic criteria, perhaps maximising their coding efficiency or information rate. Here, we draw a distinction between how a neuron’s preferred operating point is determined and how its preferred operating point is maintained through adaptation. We propose that a neuron’s preferred operating point can be characterised by the probability density function (PDF) of its output spike rate, and that adaptation maintains an invariant output PDF, regardless of how this output PDF is initially set. Considering a sigmoidal transfer function for simplicity, we derive simple adaptation rules for a neuron with one sensory input that permit adaptation to the lower-order statistics of the input, independent of how the preferred operating point of the neuron is set. Thus, if the preferred operating point is, in fact, set according to information-theoretic criteria, then these rules nonetheless maintain a neuron at that point. Our approach generalises from the unimodal case to the multimodal case, for a neuron with inputs from distinct sensory channels, and we briefly consider this case too
Procedural Generation and Rendering of Realistic, Navigable Forest Environments: An Open-Source Tool
Simulation of forest environments has applications from entertainment and art
creation to commercial and scientific modelling. Due to the unique features and
lighting in forests, a forest-specific simulator is desirable, however many
current forest simulators are proprietary or highly tailored to a particular
application. Here we review several areas of procedural generation and
rendering specific to forest generation, and utilise this to create a
generalised, open-source tool for generating and rendering interactive,
realistic forest scenes. The system uses specialised L-systems to generate
trees which are distributed using an ecosystem simulation algorithm. The
resulting scene is rendered using a deferred rendering pipeline, a Blinn-Phong
lighting model with real-time leaf transparency and post-processing lighting
effects. The result is a system that achieves a balance between high natural
realism and visual appeal, suitable for tasks including training computer
vision algorithms for autonomous robots and visual media generation.Comment: 14 pages, 11 figures. Submitted to Computer Graphics Forum (CGF). The
application and supporting configuration files can be found at
https://github.com/callumnewlands/ForestGenerato
Protein folding and the robustness of cells
The intricate intracellular infrastructure of all known life forms is based on proteins. The folded shape of a protein determines both the protein’s function and the set of molecules it will bind to. This tight coupling between a protein’s function and its interconnections in the molecular interaction network has consequences for the molecular course of evolution. It is also counter to human engineering approaches. Here we report on a simulation study investigating the impact of random errors in an abstract metabolic network of 500 enzymes. Tight coupling between function and interconnectivity of nodes is compared to the case where these two properties are independent. Our results show that the model system under consideration is more robust if function and interconnection are intertwined. These findings are discussed in the context of nanosystems engineering
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