Optimal management of microgrids

Desenvolupament dels models matemàtics necessaris per a controlar de forma òptima la microxarxa existent als laboratoris del Institut de Recerca en Energia de Catalunya. Els algoritmes s'implementaran per tal de simular el comportament i posteriorment es programaran directament sobre els elements de la microxarxa per verificar el seu correcte funcionament.. Desenvolupament dels models matemàtics necessaris per a controlar de forma òptima la microxarxa existent als laboratoris del Institut de Recerca en Energia de Catalunya. Els algoritmes s'implementaran per tal de simular el comportament i posteriorment es programaran directament sobre els elements de la microxarxa per verificar el seu correcte funcionament.

A T42A Ran Mutation: Differential Interactions with Effectors and Regulators, and Defect in Nuclear Protein Import

Ran, the small, predominantly nuclear GTPase, has been implicated in the regulation of a variety of cellular processes including cell cycle progression, nuclear-cytoplasmic trafficking of RNA and protein, nuclear structure, and DNA synthesis. It is not known whether Ran functions directly in each process or whether many of its roles may be secondary to a direct role in only one, for example, nuclear protein import. To identify biochemical links between Ran and its functional target(s), we have generated and examined the properties of a putative Ran effector mutation, T42A-Ran. T42A-Ran binds guanine nucleotides as well as wild-type Ran and responds as well as wild-type Ran to GTP or GDP exchange stimulated by the Ran-specific guanine nucleotide exchange factor, RCC1. T42A-Ran·GDP also retains the ability to bind p10/NTF2, a component of the nuclear import pathway. In contrast to wild-type Ran, T42A-Ran·GTP binds very weakly or not detectably to three proposed Ran effectors, Ran-binding protein 1 (RanBP1), Ran-binding protein 2 (RanBP2, a nucleoporin), and karyopherin ß (a component of the nuclear protein import pathway), and is not stimulated to hydrolyze bound GTP by Ran GTPase-activating protein, RanGAP1. Also in contrast to wild-type Ran, T42A-Ran does not stimulate nuclear protein import in a digitonin permeabilized cell assay and also inhibits wild-type Ran function in this system. However, the T42A mutation does not block the docking of karyophilic substrates at the nuclear pore. These properties of T42A-Ran are consistent with its classification as an effector mutant and define the exposed region of Ran containing the mutation as a probable effector loop.

Optimal estimation of two-qubit pure-state entanglement

We present optimal measuring strategies for an estimation of the entanglement of unknown two-qubit pure states and of the degree of mixing of unknown single-qubit mixed states, of which N identical copies are available. The most general measuring strategies are considered in both situations, to conclude in the first case that a local, although collective, measurement suffices to estimate entanglement, a nonlocal property, optimally.

Report on theorerical results of power generation and conversion

Experimental results of a new controller able to support bidirectional power flow in a full-bridge rectifier with boost-like topology are obtained. The controller is computed using port Hamiltonian passivity techniques for a suitable generalized state space averaging truncation system, which transforms the control objectives, namely constant output voltage dc-bus and unity input power factor, into a regulation problem. Simulation results for the full system show the essential correctness of the simplifications introduced to obtain the controller, although some small experimental discrepancies point to several aspects that need further improvement.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.