Tetsuya Shimokawa

Brownian search-and-catch mechanism for myosin-VI steps

The cargo transporter myosin-VI processively walks along actin filaments using its two heads. Here we use single-molecule nanometry to show that the strong binding by myosin heads to actin is greatly accelerated (approximately 30-fold) when backward strain is applied to weakly bound heads during the actin search. We propose that the myosin head searches for the forward actin target by Brownian motion and catches the actin in a strain-dependent manner.

A first-passage-time analysis of the periodically forced noisy leaky integrate-and-fire model.

Abstract. We present a general method for the analysis of the discharge trains of periodically forced noisy leaky integrate-and-fire neuron models. This approach relies on the iterations of a stochastic phase transition operator that generalizes the phase transition function used for the study of periodically forced deterministic oscillators to noisy systems. The kernel of this operator is defined in terms of the the first passage time probability density function of the Ornstein Uhlenbeck process through a suitable threshold. Numerically, it is computed as the solution of a singular integral equation. It is shown that, for the noisy system, quantities such as the phase distribution (cycle histogram), the interspike interval distribution, the autocorrelation function of the intervals, the autocorrelogram and the power spectrum density of the spike train, as well as the input–output cross-correlation and cross-spectral density can all be computed using the stochastic phase transition operator. A detailed description of the numerical implementation of the method, together with examples, is provided.

Coherence resonance and discharge time reliability in neurons and neuronal models.

Neurons are subject to internal and external noise that have been known to modify the way they process incoming signals. Recent studies have suggested that such alterations have functional roles and can also be used in biomedical applications. The present work goes over experimental and theoretical descriptions of the response of neurons to white noise stimulation. It examines various forms of noise related behavior in a standard neuronal model, namely the leaky integrate and fire. This clarifies the conditions under which specific noise induced changes occur in neurons, and consequently can help in determining whether nervous systems operate under similar circumstances.

A chemically driven fluctuating ratchet model for actomyosin interaction.

With reference to the experimental observations by Yanagida and his co-workers on actomyosin interaction, a Brownian motor of fluctuating ratchet kind is designed with the aim to describe the interaction between a Myosin II head and a neighboring actin filament. Our motor combines the dynamics of the myosin head with a chemical external system related to the ATP cycle, whose role is to provide the energy supply necessary to bias the motion. Analytical expressions for the duration of the ATP cycle, for the Gibbs free energy and for the net displacement of the myosin head are obtained. Finally, by exploiting a method due to Sekimoto [J. Phys. Soc. Jpn. 66 (1997) 1234], a formula is worked out for the amount of energy consumed during the ATP cycle.

Stochastic simulations on a model of circadian rhythm generation.

Biological phenomena are often modeled by differential equations, where states of a model system are described by continuous real values. When we consider concentrations of molecules as dynamical variables for a set of biochemical reactions, we implicitly assume that numbers of the molecules are large enough so that their changes can be regarded as continuous and they are described deterministically. However, for a system with small numbers of molecules, changes in their numbers are apparently discrete and molecular noises become significant. In such cases, models with deterministic differential equations may be inappropriate, and the reactions must be described by stochastic equations. In this study, we focus a clock gene expression for a circadian rhythm generation, which is known as a system involving small numbers of molecules. Thus it is appropriate for the system to be modeled by stochastic equations and analyzed by methodologies of stochastic simulations. The interlocked feedback model proposed by Ueda et al. as a set of deterministic ordinary differential equations provides a basis of our analyses. We apply two stochastic simulation methods, namely Gillespies direct method and the stochastic differential equation method also by Gillespie, to the interlocked feedback model. To this end, we first reformulated the original differential equations back to elementary chemical reactions. With those reactions, we simulate and analyze the dynamics of the model using two methods in order to compare them with the dynamics obtained from the original deterministic model and to characterize dynamics how they depend on the simulation methodologies.

Application of evolutionary mechanism to dynamic Virtual Network Function Placement

Recently, communication network services have become increasingly diverse and dynamic. Network Function Virtualization (NFV) is an effective technique to deal with these dynamic situations. Most related work on the VNF placement problem does not consider the dynamics of requests, but only static scenarios. The important goals of the dynamic VNF placement problem include accommodating new requests following the traffic dynamics and reducing the time to calculate solutions. To tackle this problem, we utilize the concept of Modularly Varying Goals (MVG), which is based on a genetic algorithm (GA) and generates solutions that can easily adapt to time-varying goals in short time. In this paper, we propose Evolvable VNF Placement (EvoVNFP) that applies the concept of MVG to the dynamic VNF placement problem to reduce the time to obtain solutions. Results from numerical evaluations show that our method is able to better follow the dynamics of VNF requests and also reduce time until adapting to successive objectives.

Low-Complexity Nanosensor Networking Through Spike-Encoded Signaling

The Internet of Nano-Things will employ wireless sensor nodes that are not only extremely small in size and large in number but also severely restricted in their power supply. While a large part of the design of such networks will rely on the engineering of physical aspects, such as developing technology for efficient energy harvesting, sensing, and communication, a significant role will also be played by the algorithms underlying the operation of nodes to ensure that optimal use is made of the limited supply of energy. This paper discusses concepts that will facilitate the design of such algorithms, such as spike-based encoding of signals and data aggregation through gossiping. We propose a method to extract information from a sensor network using nonpacket spike-based communication with local interactions between nodes. Rather than gathering sensor values from each individual node and sending them to a central destination node, measurements from the nodes are processed through the interactions between nodes, while diffusing in the network, to obtain an integrated value characteristic for all nodes measurements.

Robustness and efficiency in interconnected networks with changes in network assortativity

In this study, the effect of assortativity on the robustness and efficiency of interconnected networks was investigated. This involved constructing a network that possessed the desired degree of assortativity. Additionally, an interconnected network was constructed wherein the assortativity between component networks possessed the desired value. With respect to single networks, the results indicated that a decrease in assortativity provided low hop length, high information diffusion efficiency, and distribution of communication load on edges. The study also revealed that excessive assortativity led to poor network performance. In the study, the assortativity between networks was defined and the following results were demonstrated: assortative connections between networks lowered the average hop length and enhanced information diffusion efficiency, whereas disassortative connections between networks distributed the communication loads of internetwork links and enhanced robustness. Furthermore, it is necessary to carefully adjust assortativity based on the node degree distribution of networks. Finally, the application of the results to the design of robust and efficient information networks was discussed.

The Fokker-Planck approach for the cooperative molecular motor model with finite number of motors

We provide the methodology for the analysis of the cooperative molecular motor model with finite number of motors, which are linearly and rigidly coupled, based on the Fokker-Planck approach. The probability density functions for the position of motors are solved numerically from the stationary Fokker-Planck equations. By using these probability density functions, we provide the analytical expressions, such as the velocity, the rate of the ATP consumption, the energetic efficiency, and the dissipation energy rates. Furthermore, we investigate three specific examples, such as single motor model, 2-motor model, and infinitely coupled motor model. Numerical algorithm to solve the Fokker-Planck equations is also provided.

Constructing virtual IoT network topologies with a brain-inspired connectivity model

Wireless sensor networks will be one of the fundamental technologies for realizing the future Internet of Things (IoT) environment. In IoT, the number of connected devices is expected to increase drastically and there will be a wide variety of requirements for application services, which will lead to frequent modifications or construction/destruction of topologies. In such situations, it is essential to know how power-saving, low-latency, and highly efficient IoT network topologies can be constructed. In this paper, we take inspiration from the brains network of interconnecting neurons is known for its efficient properties. We propose a virtual IoT network construction method based on the Exponential Distance Rule (EDR) model that describes the connection structure of the areas in the cerebral cortex. Since the original EDR model deals with large-scale networks with an enormous number of neurons and generates links between nodes considering physical distance constraints, the virtual IoT network constructed by the proposed method is able to achieve high scalability, low latency, and high communication efficiency at a relatively low cost.