Druggability profile of stilbene-derived PPAR agonists: Determination of physicochemical properties and PAMPA study

PPAR agonists represent a new therapeutic opportunity for the prevention and treatment of neurodegenerative disorders, but their pharmacological success depends on favourable pharmacokinetic properties and capability to cross the BBB. In this study, we assayed some PPAR agonists previously synthesized by us for their physicochemical properties, with particular references to lipophilicity, solubility and permeability profiles, using the PAMPA. Although tested compounds showed high lipophilicity and low aqueous solubility, the results revealed a good overall druggability profile, encouraging further studies in the field of neurodegenerative diseases

Using edge-valued decision diagrams for symbolic generation of shortest paths

Abstract. We present a new method for the symbolic construction of shortest paths in reachability graphs. Our algorithm relies on a variant of edge–valued decision diagrams that supports efficient fixed–point iterations for the joint computation of both the reachable states and their distance from the initial states. Once the distance function is known, a shortest path from an initial state to a state satisfying a given condition can be easily obtained. Using a few representative examples, we show how our algorithm is vastly superior, in terms of both memory and space, to alternative approaches that compute the same information, such as ordinary or algebraic decision diagrams.

Distributed and structured analysis approaches to study large and complex systems

Abstract. Both the logic and the stochastic analysis of discrete-state systems are hindered by the combinatorial growth of the state space underlying a high-level model. In this work, we consider two orthogonal approaches to cope with this “state-space explosion”. Distributed algorithms that make use of the processors and memory overall available on a network of N workstations can manage models with state spaces approximately N times larger than what is possible on a single workstation. A second approach, constituting a fundamental paradigm shift, is instead based on decision diagrams and related implicit data structures that efficiently encode the state space or the transition rate matrix of a model, provided that it has some structure to guide its decomposition; with these implicit methods, enormous sets can be managed efficiently, but the numerical solution of the stochastic model, if desired, is still a bottleneck, as it requires vectors of the size of the state space.

A framework to decompose GSPN models

This paper presents a framework to decompose a single GSPN model into a set of small interacting models. This decomposition technique can be applied to any GSPN model with a finite set of tangible markings and a generalized tensor algebra (Kronecker) representation can be produced automatically. The numerical impact of all the possible decompositions obtained by our technique is discussed. To do so we draw the comparison of the results for some practical examples. Finally, we present all the computational gains achieved by our technique, as well as the future extensions of this concept for other structured formalisms.</p