State Of Anode Research Articles

An extended gene protein/products Boolean network model including post-transcriptional regulation.

BackgroundNetworks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs.ResultsIn this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit.ConclusionsThe extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanisms.

Survivability-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks

The paper proposes the algorithm of survival topology evolution model. The model applies the survival analysis to the research on the topology of networks. On one hand, it takes the survivability of every node into account. Since the state of a node is affected by the node itself, the environment and so on, it's necessary to study the survivability of nodes. In addition, the survival analysis on the nodes can indicate whether the nodes work well in real time. On the other hand, it also takes the deletion of links and nodes determined by the survivability of the nodes into account. Then it reaches the conclusion by mean field method. The result shows that the degree distributions of WSNs are approximately power law as B-A model and that the survivability of the nodes is proportional to the degree distribution of the network consisting of the previous nodes. These are further confirmed by simulation example.

Spreading processes on dynamically changing contact networks

We develop and analyze an agent-based model for the study of information propagation in dynamic contact networks. We represent information as a state of a node in a network that can be probabilistically transferred to an adjacent node within a single time step. The model is based on a closed (yet sufficiently large) population that can support processes of link generation and annihilation using different contact regimes. Our study is confined to the case of homogeneous contacts, where each agent establishes and breaks contacts in the same way. We consider information to be available for spreading in a fixed time window (i.e. finite memory). We find, surprisingly, that information transmission (measured as the proportion of informed nodes after a fixed number of time steps) is identical for dynamic preferential and random networks, but radically different for the associate mixing contact regime. We also find that the probability of transmission is, similarly counterintuitively, not a main driver of the process as opposed the the main network par maters determining contact lifetime and the turnover rate on connections. We discuss the explanation and the significance of these results in the light of the fundamental difference between dynamic and static (cumulative) networks.

Towards understanding the behavior of physical systems using information theory

One of the goals of complex network analysis is to identify the most influential nodes, i.e., the nodes that dictate the dynamics of other nodes. In the case of autonomous systems or transportation networks, highly connected hubs play a preeminent role in diffusing the flow of information and viruses; in contrast, in language evolution most linguistic norms come from the peripheral nodes who have only few contacts. Clearly a topological analysis of the interactions alone is not sufficient to identify the nodes that drive the state of the network. Here we show how information theory can be used to quantify how the dynamics of individual nodes propagate through a system. We interpret the state of a node as a storage of information about the state of other nodes, which is quantified in terms of Shannon information. This information is transferred through interactions and lost due to noise, and we calculate how far it can travel through a network. We apply this concept to a model of opinion formation in a complex social network to calculate the impact of each node by measuring how long its opinion is remembered by the network. Counter-intuitively we find that the dynamics of opinions are not determined by the hubs or peripheral nodes, but rather by nodes with an intermediate connectivity.

On the Cost of Knowledge of Mobility in Dynamic Networks: An Information-Theoretic Approach

In this paper, we extend an information-theoretic approach for characterizing the minimum cost of tracking the motion state information, such as locations and velocities, of nodes in dynamic networks. A rate-distortion formulation is proposed to solve this minimum-cost motion-tracking problem, where the minimum cost is the minimum information rate required to identify the network state at a sequence of tracking time instants within a certain distortion bound. The formulation is applicable to various mobility models, distortion criteria, and stochastic sequences of tracking time instants and hence is general. Under Brownian motion and Gauss-Markov mobility models, we evaluate lower bounds on the information rate of tracking the motion state information of nodes, where the motion state of a node is 1) the node's locations only, or 2) both its locations and velocities. We apply the obtained results to analyze the geographic routing overhead in mobile ad hoc networks. We present the minimum overhead incurred by maintaining the geographic information of nodes in terms of node mobility, packet arrival process, and distortion bounds. This leads to precise characterizations of the observation that given certain state-distortion allowance, protocols aimed at tracking motion state information may not scale beyond a certain level of node mobility.

Cargo Container Monitoring Data Reliability Evaluation in WSN Nodes

The application of state-of-art mobile technologies in cargo container transportation monitoring systems (ex. Sensor networks) is possible only after assessing the reliability of data transfer. Data reliability depends on how accurately container state is evaluated by sensors and sensor reliability itself. Four probability values are introduced that define the states of damaged and undamaged containers and evaluate the generation of true and false data. The formats of messages that are being sent to the integrated network database are described. The definition of data defect level is introduced and used to evaluate the aggregate state of a node, consisting of a container and a sensor. The dependences of defect levels on the probabilities to detect false data at nearby nodes in the problem area have been derived. It enables real-time detection of data defects, thus helping during decision making processes in cargo transportation enterprise environment. Ill. 2, bibl. 4, tabl. 1 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.119.3.1371

Evolution of a population of random Boolean networks

We investigate the evolution of populations of random Boolean networks under selection for robustness of the dynamics with respect to the perturbation of the state of a node. The fitness landscape contains a huge plateau of maximum fitness that spans the entire network space. When selection is so strong that it dominates over drift, the evolutionary process is accompanied by a slow increase in the mean connectivity and a slow decrease in the mean fitness. Populations evolved with higher mutation rates show a higher robustness under mutations. This means that even though all the evolved populations exist close to the plateau of maximum fitness, they end up in different regions of network space.

Stochastic shortest path with unlimited hops

We present new results for the Stochastic Shortest Path problem when an unlimited number of hops may be used. Nodes and links in the network may be congested or uncongested, and their states change over time. The goal is to adaptively choose the next node to visit such that the expected cost to the destination is minimized. Since the state of a node may change, having the option to revisit a node can improve the expected cost. Therefore, the option to use an unbounded number of hops may be required to achieve the minimum expected cost. We show that when revisits are prohibited, the optimal routing problem is np-hard. We also prove properties about networks for which continual improvement may occur. We study the related routing problem which asks whether it is possible to determine the optimal next node based on the current node and state, when an unlimited number of hops is allowed. We show that as the number of hops increases, this problem may not converge to a solution.

Unanimity rule on networks

We present a model for innovation, evolution, and opinion dynamics whose spreading is dictated by a unanimity rule. The underlying structure is a directed network, the state of a node is either activated or inactivated. An inactivated node will change only if all of its incoming links come from nodes that are activated, while an activated node will remain activated forever. It is shown that a transition takes place depending on the initial condition of the problem. In particular, a critical number of initially activated nodes is necessary for the whole system to get activated in the long-time limit. The influence of the degree distribution of the nodes is naturally taken into account. For simple network topologies we solve the model analytically; the cases of random and small world are studied in detail. Applications for food-chain dynamics and viral marketing are discussed.

Guaranteed fault containment and local stabilization in routing

We formulate concepts that characterize network properties in the presence of high-frequency faults, and we present CPV, a path-vector routing protocol that locally contains high-frequency faults and locally stabilizes. Local containment enables CPV to guarantee that distant nodes be unaffected by faults. Local stabilization enables CPV to stabilize the network within time depending only on the perturbation size instead of the network size. In CPV, the distance to which the state of a node propagates is proportional to the time the state remains valid. These properties are achieved by reacting to a new fault only after first containing the response to the previous fault. In addition to analytically proving these properties, we evaluate CPV by simulating Internet-type networks with up to 75 autonomous systems; we observe that CPV reduces the number of fault-affected nodes by a factor of 71 and the network convergence time by a factor of 9.2 when compared with BGP.

Reactive congestion control algorithm in an ATM node

This paper discusses the congestion phenomenon on anAtm node and presents an analytical approach to follow the cumulated delays on the network. It proposes an adaptation algorithm of the sources rate with the evolving congestion state of a node by a reactive approach.

A prediction-based adaptive location policy for distributed load balancing

The location policy in distributed load balancing is to locate the destination nodes to or from which tasks will be transferred. An efficient location policy is required to achieve high performance on distributed load balancing. In this paper, we propose a new distributed adaptive location policy based on predictable state knowledge. The predictable state knowledge in each node is composed of the system state information collected at run time and the predefined static information that is a priority order of each node for transferring tasks. The proposed scheme systematically maintains the preditable state knowledge in each node by using an efficient data structure and a rule for collecting state information with low overheads. When the state of a node becomes heavily-loaded, the proposed scheme predicts both heavily-loaded nodes and lightly-loaded nodes by exploiting predictable state knowledge and then finds a good lightly-loaded node that minimizes useless polling and maximizes even load distribution. An analytic model is developed to compare the presented scheme with other well known schemes. The validity of the model is checked with an event-driven simulation, and it is shown that the presented scheme exhibits a significant performance improvement over other schemes, especially at high system loads. Also, the presented scheme is shown to significantly improve polling hit ratio and to avoid system instability.

A queueing analysis of the performance of DQDB

A queueing model for the DQDB protocol with a single priority level is presented and analyzed. The state of a node is defined by the number of requests in the distributed queue prior to, and post generation of a segment. It is shown that the number of states that a node can be in, is finite and position dependent. Moreover, it is shown that the request rate and the network traffic that a node perceives at any particular moment are highly dependent on its state. An iterative technique is presented to calculate the request rate and network traffic for all the nodes in a DQDB network by carefully modeling the relationship between adjacent nodes. The node average waiting time and average throughput are analyzed as functions of the node position and state in the network. Our results illustrate the effect of the various parameters on the waiting time and throughput. Comparisons with simulation and existing analytical results are presented.