972043
Stochastic Modeling of Cytoplasmic Reactions in Complex Biological Systems
The use of “in silico” stochastic event based modeling can identify the dynamic interactions of different processes in a complex biological system. This requires the computation of the time taken by different events in the system based on their biological functions. One such important event is the reactions between the molecules inside the cytoplasm of a cell. We present a mathematical formulation for the estimation of the reaction time between two molecules within a cell based on the system state. We derive expressions for the average and second moment of the time for reaction to be used by our stochastic event-based simulation. Unlike rate equations, the proposed model does not require the assumption of concentration stability for multiple molecule reactions.
1283500
GaMa: an evolutionary algorithmic approach for the design of mesh-based radio access networks
Wireless mesh based access networks are destined to play a pivotal role in next generation broadband systems. With the proliferation of mesh networks, a key issue for network designers is the design of an optimal mesh topology which minimizes cost while maintaining carrier-class features. In this paper, we formulate the design of an optimal mesh, taking network deployment cost, topological properties and carrier-grade reliability into account. Next, we present a genetic algorithm based algorithm (GaMa) for mesh topology design. We show that GaMa is capable of determining a generic mesh topology with carrier-class network features. The performance of the algorithm is compared with existing mesh topologies and gives improved results without the constraints of maintaining a regular topology
14429447
iSimBioSys: a discrete event simulation platform for 'in silico' study of biological systems
With the availability of huge databases cataloguing the various molecular "parts" of complex biological systems, researchers from multiple disciplines have focused on developing modeling and simulation tools for studying the variability of cellular behavior at a system level - encompassing the dynamics arising from many species of interacting molecules. In this work, we present a system engineering approach to model biological processes. In this approach, a biological process is modeled as a collection of interacting functions driven in time by a set of discrete events. We focus on the discrete event simulation platform, called "iSimBioSys", which we have developed for studying the dynamics of cellular processes in silico. As a test-bed for studying our approach we model the two component PhoPQ system, responsible for the expression of several virulence genes in Salmonella Typhimurium. We analyzed the effect of extra cellular magnesium on the behavioral dynamics of this pathway using our framework and compared the results with an experimental system. We also analyze the performance of iSimBioSys, based on the model biological system, in terms of system usage and response.
32010004
Binary Self-Stabilization in Distributed Systems
Abstract Dijkstra introduced the problem of self-stabilization in distributed systems as an interesting exercise for achieving global convergence using local actions. In all the papers dealing with self-stabilizing systems so far, it has been assumed that each component machine of the distributed system has at least three states. This paper illustrates that it is feasible to design a self-stabilizing system with two-state or binary machines at the expense of a larger atomicity.
1421735
Scalable Self-Stabilization
This paper presents a methodology for a synchronous non-reactive distributed system on a tree topology to stabilize from a k-faulty configuration in a time independent of the size n of the system. In the proposed methodology, processes first measure and compare the sizes of the faulty regions, and then use this information to schedule actions in such a way that the size of the faulty regions progressively shrink, until they completely disappear. We demonstrate that when k processes fail, the stabilization time is O(k2). Apart from its applicability to a wide class of problems, the proposed method achieves scalability with a low space complexity of O(�.(�.k+log2n)) per process, where � is the maximum degree of a node.
11369250
Self-optimizing Peer-to-Peer Networks with Selfish Processes
Request patterns in peer-to-peer networks are not uniform, and the cost of communication depends on the traffic flows among peers. This paper illustrates how processes in an overlay network can use the information about traffic flow pattern and spontaneously adjust the topology to minimize their communication costs. Four different self-optimization protocols are presented. The selfish protocols allow peers to modify their routing tables to suit their individual needs, and are easy to implement, but the improvements are limited. Compared to this, the altruistic protocols that allow peers to adjust the routing tables based on the needs of other processes, promise a better performance. Since selfish peers may not comply, a penalty mechanism is proposed to discourage selfishness.
62100403
Scalable self-stabilization
The paper presents a method by which an asynchronous non-reactive distributed system can stabilize from a k-faulty configuration in a time that is a monotonically increasing function of k and independent of the size of the system. In the proposed methodology processes first measure the size of the faulty regions, and then use this information to schedule actions in such a way that the faulty regions progressively shrink, until they completely disappear. When k contiguous processes fail, the stabilization time is O(k/sup 3/). Otherwise, for small values of k, the stabilization time can be exponential in k, but it has an upper bound of O(n/sup 3/). The added space complexity per process is O(/spl delta/ log/sub 2/n), where /spl delta/ is the maximum degree of a node.
25755124
An Algorithm for Computing a Convex and Simple Path of Bounded Curvature in a Simple Polygon

AbstractAbstract. In this paper we study the collision-free path planning problem for a point robot, whose path is of bounded curvature(i.e., constrained to have curvature at most 1), moving in the plane inside an n -sided simple polygon P . Given two points sand tinside Pand two directions of travel, one at sand one at t , the problem is to compute a convex and simple path of bounded curvature inside Pfrom sto tconsisting of straight-line segments and circular arcs such that (i) the radius of each circular arc is at least 1, (ii) each segment on the path is the tangent between the two consecutive circular arcs on the path, (iii) the given initial direction at sis tangent to the path at sand (iv) the given final direction at tis tangent to the path at t . We propose an O(n4)time algorithm for this problem. Using the notion of complete visibility, Pis pruned to another polygon P'such that any convex and simple path from sto tinside Palso remains inside P' . Then our algorithm constructs the locus of center of a circle of unit radius translating along the boundary of P'and, using this locus, the algorithm constructs a convex and simple path of bounded curvature. Our algorithm is based on the relationship between the Euclidean shortest path, link paths and paths of bounded curvature, and uses several properties derived here on convex and simple paths of bounded curvature. We also show that the path computed by our algorithm can be transformed in O(n)time to a minimalconvex and simple path of bounded curvature. Using this transformation and two necessary conditions proposed here for the shortest convex and simple path of bounded curvature, a minimalbounded curvature path is located in O(n4)time whose length, except in special situations involving arcs of length greater than π , is at most twice the length of a shortest convex and simple path of bounded curvature.
