A Cooperative Network Packing Game with Simple Paths

We consider a cooperative packing game in which the characteristic function is defined as the maximum number of independent simple paths of a fixed length included in a given coalition. The conditions under which the core exists in this game are established, and its form is obtained. For several particular graphs, the explicit form of the core is presented.

About many independent paths in a Graph

The article deals with a problem that often occurs when calculating stability which one of the main technical characteristics of a communication network with channel switching. The problem is related to the search for backup routes of a given information direction of communication and lies in the fact that for extensive large-scale communication networks, known pathfinding algorithms that are usually used to solve this problem become ineffective. Various algorithms are proposed that will allow finding all the available independent simple paths between the given vertices of the graph, thereby providing a more accurate estimate of stability and throughput. In addition, the solution of this problem, in view of finding the maximum set of independent simple paths, can find application in solving a range of routing problems.

Shadows of a Closed Curve

A shadow of a geometric object A in a given direction v is the orthogonal projection of A on the hyperplane orthogonal to v. We show that any topological embedding of a circle into Euclidean d-space can have at most two shadows that are simple paths in linearly independent directions. The proof is topological and uses an analog of basic properties of degree of maps on a circle to relations on a circle. This extends a previous result that dealt with the case d = 3.

Computing Signal Transduction in Signaling Networks modeled as Boolean Networks, Petri Nets, and Hypergraphs

Mathematical frameworks circumventing the need of mechanistic detail to build models of signal transduction networks include graphs, hypergraphs, Boolean Networks, and Petri Nets. Predicting how a signal transduces in a signaling network is essential to understand cellular functions and disease. Different formalisms exist to describe how a signal transduces in a given intracellular signaling network represented in the aforementioned modeling frameworks: elementary signaling modes, T-invariants, extreme pathway analysis, elementary flux modes, and simple paths. How do these formalisms compare?We present an overview of how signal transduction networks have been modelled using graphs, hypergraphs, Boolean Networks, and Petri Nets in the literature. We provide a review of the different formalisms for capturing signal transduction in a given model of an intracellular signaling network. We also discuss the existing translations between the different modeling frameworks, and the relationships between their corresponding signal transduction representations that have been described in the literature. Furthermore, as a new formalism of signal transduction, we show how minimal functional routes proposed for signaling networks modeled as Boolean Networks can be captured by computing topological factories, a methodology found in the metabolic networks literature. We further show that in the case of signaling networks represented with an acyclic B-hypergraph structure, the definitions are equivalent. In signaling networks represented as directed graphs, it has been shown that computations of elementary modes via its incidence matrix correspond to computations of simple paths and feedback loops. We show that computing elementary modes based on the incidence matrix of a B-hypergraph fails to capture minimal functional routes.