Biomolecules assemble to form molecular machines such as molecular motors, cell signal processors, DNA transcription processors and protein synthesizers to fulfill their functions. Their collaboration allows the activity of biological systems. The reactions and behaviors of molecular machines vary flexibly while responding to their surroundings. This flexibility is essential for biological organisms. The underlying mechanism of molecular machines is not as simple as that expected from analogy with man-made machines. Since molecular machines are only nanometers in size and has a flexible structure, it is very prone to thermal agitation. Furthermore, the input energy level is not much difference from average thermal energy, kBT. Molecular machines can thus operate under the strong influence of this thermal noise, with a high efficiency of energy conversion. They would not overcome thermal noise but effectively use it for their functions. This is in sharp contrast to man-made machines that operate at energies much higher than the thermal noise. In recent years, the single molecule detection (SMD) and nano-technologies have rapidly been expanding to include a wide range of life science. The dynamic properties of biomolecules and the unique operations of molecular machines, which were previously hidden in averaged ensemble measurements, have now been unveiled. The aim of our research is to approach the engineering principle of adaptive biological system by uncovering the unique operation of biological molecular machines. I survey our SMD experiments designed to investigate molecular motors, enzyme reactions, protein dynamics, DNA transcription and cell signaling.
We propose a neural network based autoassociative memory system for unsupervised learning. This system is intended to be an example of how a general information processing architecture, similar to that of neocortex, could be organized. The neural network has its units arranged into two separate groups called populations, one input and one hidden population. The units in the input population form receptive fields that sparsely projects onto the units of the hidden population. Competitive learning is used to train these forward projections. The hidden population implements an attractor memory. A back projection from the hidden to the input population is trained with a Hebbian learning rule. This system is capable of processing correlated and densely coded patterns, which regular attractor neural networks are very poor at. The system shows good performance on a number of typical attractor neural network tasks such as pattern completion, noise reduction, and prototype extraction.
The design approach in engineering has created industrial revolution and modern civilization. The basis of design is the understanding of key principles underlying the system of interest. Such an approach has not been explored in biology until recently. While much remained unknown in the cell, key functional paradigms and many molecular components have been extensively characterized. The design approach can now be used in the cell to explore possible applications of biological components beyond their natural configurations, much like the design of analog computers using well characterized modules. In addition, the design approach provides an alternative method to explore design principles used by nature.
This paper presents a middleware system for multi-agents on a distributed system as a general test-bed for bio-inspired approaches. The middleware is unique to other approaches, including distributed object systems, because it can maintain and migrate a dynamic federation of multiple agents on different computers. It enables each agent to explicitly define its own deployment policy as a relocation between the agent and another agent. This paper describes a prototype implementation of the middleware built on a Java-based mobile agent system and its practical applications that illustrates the utility and effectiveness of the approach in real distributed systems.
In symbiotic processes, different organisms coexist stably and interact by sharing with same metabolites and environmental conditions. A symbiotic process of a lactic acid bacterium, Lactococcus lactis sub species lactis (ATCC11454) and diary yeast Kluyveromyces marxianus is studied in this paper. A mathematical model of the symbiotic process composed of two microorganisms is developed by integrating two pure cultivation models. A cascade pH controller coupled with the dissolved oxygen (DO) control is newly developed and lactate consumption activity of K. marxianus is controlled by changing the DO concentration. The pH and lactate are kept stably at constant levels and both microorganisms grow well. Stability of this symbiotic process with disturbance of inoculums sizes of both microorganisms is investigated. The dynamic behavior of fusion process of independent two bionetworks is also discussed.
Clustering tasks occur for various different application domains including very large data streams e.g. for robotics and life science, different data formats such as graphs and profiles, and a multitude of different objectives ranging from statistical motivations to data driven quantization errors. Thus, there is a need for efficient any-time self-adaptive models and implementations. The focus of this contribution is on clustering algorithms inspired by biological paradigms which allow to transfer ideas of organic computing to the important task of efficient clustering. We discuss existing methods of adaptivity and point out a taxonomy according to which adaptivity can take place. Afterwards, we develop general perspectives for an efficient self-adaptivity of self-organizing clustering.
We describe a new high-throughput method to quantify the structural properties of individual cell-sized liposomes. We labeled an internal aqueous solution of liposomes with a green fluorescent protein (GFP) and the membrane with a fatty acid conjugated with a red fluorescent probe. The internal aqueous volume and lipid membrane volume of each liposome was measured, and double-labeled liposomes were analyzed by flow cytometry, a useful tool that enables us to estimate the internal aqueous and lipid membrane volumes of individual cell-sized liposomes independently of shape and structure. This method shows promise in opening the way to understanding the characteristics of biochemical reactions occurring within a liposome, to optimizing the preparation method of liposomes, and to overcoming many of the difficulties in realizing an artificial cell.
Life-like adaptive behaviour is so far an illusive goal in robot control. A capability to act successfully in a complex, ambiguous, and harsh environment would vastly increase the application domain of robotic devices. Established methods for robot control run up against a complexity barrier, yet living organisms amply demonstrate that this barrier is not a fundamental limitation. To gain an understanding of how the nimble behaviour of organisms can be duplicated in made-for-purpose devices we are exploring the use of biological cells in robot control. This paper describes an experimental setup that interfaces an amoeboid plasmodium of Physarum polycephalum with an omnidirectional hexapod robot to realise an interaction loop between environment and plasticity in control. Through this bio-electronic hybrid architecture the continuous negotiation process between local intracellular reconfiguration on the micro-physical scale and global behaviour of the cell in a macroscale environment can be studied in a device setting.
The emergence of heterogeneous cellular state in uniform environment was studied. Using a continuous culture system which provides homogeneous culture environment, we investigated the fluctuation in expression level of glnA gene in a cell population. As results, we found that the expression level of glnA gene in the cells exhibit a large fluctuation (with two orders of magnitude of protein number), even though expression of the gene is essential for cellular growth and the environment is homogeneous. Furthermore, among several steady states, the transient processes of such heterogeneous cell population were investigated, by changing environmental conditions. The results showed that cells can respond to environmental changes even when their intra-cellular state is accompanied by fluctuations. These results may provide a clue to understand why biological systems can maintain and reproduce themselves robustly.
Bandwidth demands of communication networks are rising permanently. Thus, the requirements to modern routers regarding packet classification are rising accordingly. Conventional algorithms for packet classification use either a huge amount of memory or have high computational demands to perform the task. Using a hash function in order to classify packets is promising regarding both memory and computation time. However, such a hash function needs to be of high performance and cheap in hardware costs. These two design goals are contradictory. To limit the costs of a hardware implementation, known good hash functions, as used for software implementations of encryption algorithms, are applicable to only a limited extend. To achieve the goals mentioned above, an adaptive hash function is needed. In this paper, an approach for a hardware packet classifier using an evolvable hash function is presented. It consists of an evolutionary algorithm which is entirely implemented in hardware.
We propose a modeling system that enables users to create tree models with 3D gesture input and Interactive L-system. It generates tree models by using growth simulation based on the trunk or silhouette shapes of trees given by user gestures. The Interactive L-system is one of the growth simulation algorithm, having spatial information of tree models, and allows users to generate, manipulate, and edit the shape of tree models by user’s direct input interactively. The system carefully addresses the fragile balance and tradeoff between the freedom of user interaction and the autonomy of tree growth. Users intuitively and easily create tree models that have the exact features of branching structures or the silhouette shape of trees according to user intentions and imagination.
This paper describes how a distributed neural architecture for the general control of robots has been applied for the generation of a walking behaviour in the Aibo robotic dog. The architecture described has been already demonstrated useful for the generation of more simple behaviours like standing or standing up. This paper describes specifically how it has been applied to the generation of a walking pattern in a quadruped with twelve degrees of freedom, in both simulator and real robot. The main target of this paper is to show that our distributed architecture can be applied to complex dynamic tasks like walking. Nevertheless, by showing this, we also show how a completely neural and distributed controller can be obtained for a robot as complex as Aibo on a task as complex as walking. This second result is by itself a new and interesting one since, to our extent, there are no other completely neural controllers for quadruped with so many DOF that allow the robot to walk. Bio-inspiration is used in three ways: first we use the concept of central pattern generators in animals to obtain the desired walking robot. Second we apply evolutionary processes to obtain the neural controllers. Third, we seek limitations in how real dogs do walk in order to apply them to our controller and limit the search space.
As the name suggests, epidemic protocols mimic spread of virus to implement broadcasting with high reliability and low communication cost in peer-to-peer (P2P) overlay networks. In this paper, we study the reliability of epidemic protocols in scale-free networks, an important class of P2P overlay network topologies. In order to improve the robustness of epidemic protocols, we optimize the basic epidemic protocol in the following two ways. One optimization is to introduce an adaptive mechanism that allows each node to retransmit a broadcast message adaptively to the environment. The other optimization is to modify the protocol such that nodes will forward broadcast messages preferentially to neighbor nodes of small degree. The usefulness of these optimizations is demonstrated through simulation results.
Insects and arthropods have compound eyes consisting of multiple small eyes as their visual system. Various interesting features can be utilized in the applications of the compound eye to information systems. A compact image capturing system named Thin Observation Module by Bound Optics (TOMBO) is an effective instance of the photonic information systems based on compound-eye imaging. The TOMBO retrieves a high-resolution image from multiple low-resolution images captured by the compound eye. In this paper, wide distance-range imaging, 3-D information acquisition, and 3-D object interface are presented as effective applications of the TOMBO system.
The routing algorithm of SPF (Shortest Path First) [1] is widely distributed in large scale network such as internet. Since this routing algorithm is designed in order to improve throughput of each packet which is sequentially generated at the nodes, it is not suitable for averaging load balance in the network. The enzymic feedback in the cell is the typical and basic control mechanism which can realize homeostasis of the value of every reactant in the metabolic pathway. The purpose of this study is to design an adaptive routing in which the packets generated at the nodes can be sent to the final destinations with avoiding the partial and time-variant congestions in the network, and the load balance in the network can be averaged. We have proposed here a new biologically inspired adaptive routing algorithm by introducing an enzymic feedback control mechanism in the cell.
In this paper we propose a resilient scheme for multi-path routing using a biologically-inspired attractor selection method. The main advantage of this approach is that it is highly noise-tolerant and capable of operating in a very robust manner under changing environment conditions. We will apply an enhanced attractor selection model to multi-path routing in overlay networks and discuss some general properties of this approach based on numerical simulations. Furthermore, our proposal considers randomization in the path selection which reduces the selfishness and improves the overall network-wide performance.
In ad hoc networks, it is effective that each mobile host creates replicas of data items held by other mobile hosts for improving data accessibility. In our previous work, we assumed an environment where data items are updated and proposed two updated data dissemination methods which efficiently update old replicas. In these methods, the communication traffic is large since every mobile host necessarily requests updated data items when it knows that its own replicas are old. In this paper, we propose an updated data dissemination method exploiting an epidemic model, which is a popular bio-inspired approach, for reducing the communication traffic. In our proposed method, mobile hosts disseminate invalidation reports and discard old replicas when a mobile host updates a data item or when two mobile hosts are connected with each other. Each mobile host which discards an old replica requests the updated data item with a certain probability. We also present simulation results to evaluate the performance of our proposed method.
This article describes an implementation of a basic multi-processor system that exhibits replication and differentiation abilities on the POEtic tissue, a programmable hardware designed for bio-inspired applications [1,2]. As for a living organism, whose existence starts with only one cell that first divides, our system begins with only one totipotent processor, able to implement any of the cells required by the final organism, which can also fully replicate itself, using the functionalities of the POEtic substrate. Then, analogously to the cells in a developing organism, our just replicated totipotent processors differentiate in order to execute their specific part of the complete organism functionality. In particular, we will present a working realization using MOVE processors whose instructions define the flow of data rather than the operations to be executed [3]. It starts with one basic MOVE processor that first replicates itself three times; the four resulting processors then differentiate and connect together to implement a multi-processor modulus-60 counter.
A linear relationship between responses of biological systems and their fluctuations is presented. The fluctuation is given by the variance of a given quantity, whereas the response is given as the average change in the quantity for a given parameter change. By studying experimental evolution where fluorescence per E.coli cell increased, we confirmed our relationship with a positive correlation between the evolutionary rate of fluorescence and its fluctuation observed over genetically identical cells. The generality of the relationship and its possible application to other fluctuating systems are discussed.
In this research, “attractor selection,” which adopts the concept of “attractor” chiefly defined in biological and physical fields, is applied to a scheduling problem. An attractor is an attraction area that an orbit in space converges on asymptotically, and this area denotes a stable state. The attractor to which an orbit from a certain state of an initial condition is attracted is statistically determined. Attractor selection is an algorithm that searches for a stable state flexibly under changing environments. To apply attractor selection to a scheduling field, a scheduling framework based on scheduling strategy using a dispatching rule is introduced. A scheduling problem solution is scheduled by repeated applications of a prepared dispatching rule with plural strategies. The rule has a parameter that controls scheduling strategies based on the current “environment,” which means kinds, amounts, and remaining to due of jobs, machine conditions, etc. Attracter selection controls the parameters under changing environments. The proposed framework was applied to a real-time production scheduling problem, and the optimality of the parameters of the strategy and followup ability were considerd when environmental changes occur.
