Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flowcomputation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow computation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flow computation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we de- velop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional mul- tilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrec- tional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimiza- tion search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow com- putation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

Topological comparison of methods for predicting transcriptional cooperativity in yeast

Background: The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks. Results: Our results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes. Conclusion: Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.

Deployment of self-expandable stents in aneurysmatic cerebral vessels: comparison of different computational approaches for interventional planning

In the last few years, there has been a growing focus on faster computational methods to support clinicians in planning stenting procedures. This study investigates the possibility of introducing computational approximations in modelling stent deployment in aneurysmatic cerebral vessels to achieve simulations compatible with the constraints of real clinical workflows. The release of a self-expandable stent in a simplified aneurysmatic vessel was modelled in four different initial positions. Six progressively simplified modelling approaches (based on Finite Element method and Fast Virtual Stenting – FVS) have been used. Comparing accuracy of the results, the final configuration of the stent is more affected by neglecting mechanical properties of materials (FVS) than by adopting 1D instead of 3D stent models. Nevertheless, the differencesshowed are acceptable compared to those achieved by considering different stent initial positions. Regarding computationalcosts, simulations involving 1D stent features are the only ones feasible in clinical context.

Obtenció de nous anàlegs amb activitat brassinoesteroide mitjançant modelització molecular i síntesi

Els brassinoesteroides són productes naturals que actuen com a potents reguladors del creixement vegetal. Presenten aplicacions prometedores en l’agricultura degut a que, aplicats exògenament, augmenten la qualitat i la quantitat de les collites. Ara bé, el seu ús s’ha vist restringit degut a la seva costosa obtenció. Aquest fet ha motivat la recerca de nous compostos actius més assequibles. En aquest projecte es planteja el disseny i obtenció de nous anàlegs seguint diferents estratègies que impliquen tant l’ús de mètodes de modelització molecular com de síntesi orgànica. La primera d’aquestes estratègies consisteix en buscar compostos actius en bases de dades de compostos comercials a través de processos de Virtual Screening desenvolupats amb mètodes computacionals basats en Camps d’Interacció Molecular. Així, es van establir i interpretar models de Relacions Quantitatives Estructura-Activitat (QSAR) emprant descriptors independents de l’alineament (GRIND) i, amb col•laboració amb la Universitat de Perugia, aquest criteri de cerca es va ampliar amb l’aplicació de descriptors FLAP de nova generació. Una altra estratègia es va basar en intentar substituir l’esquelet esteroide dels brassinoesteroides per una estructura equivalent, fixant com a cadena lateral el grup (R)-hexahidromandelil. S’han aplicat dos criteris: mètodes computacionals basats en models QSAR establerts amb descriptors GRIND i també en la metodologia SHOP (scaffold hopping), i, per altra banda, anàlegs proposats racionalment a partir d’un estudi efectuat sobre disruptors endocrins no esteroïdals. Sobre les estructures trobades s’hi va unir la cadena lateral comercial esmentada per via sintètica, en la qual s’ha hagut de fer un èmfasi especial en grups protectors. En total, 49 estructures es proposen per a ser obtingudes sintèticament. També s’ha treballat en l’obtenció un agonista derivat de l’hipotètic antagonista KM-01. Totes les molècules candidates, ja siguin comercials o obtingudes sintèticament, estant sent avaluades en el test d’inclinació de la làmina d’arròs (RLIT).

Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation

High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational methods have been employed for that purpose, although most were originally developed to distinguish disease-related nonsynonymous single nucleotide variants (nsSNVs) from polymorphisms. Our new method, transformed Functional Impact score for Cancer (transFIC), improves the assessment of the functional impact of tumor nsSNVs by taking into account the baseline tolerance of genes to functional variants.

Differences in the evolutionary history of disease genes affected by dominant or recessive mutations

Background: Global analyses of human disease genes by computational methods have yielded important advances in the understanding of human diseases. Generally these studies have treated the group of disease genes uniformly, thus ignoring the type of disease-causing mutations (dominant or recessive). In this report we present a comprehensive study of the evolutionary history of autosomal disease genes separated by mode of inheritance.Results: We examine differences in protein and coding sequence conservation between dominant and recessive human disease genes. Our analysis shows that disease genes affected by dominant mutations are more conserved than those affected by recessive mutations. This could be a consequence of the fact that recessive mutations remain hidden from selection while heterozygous. Furthermore, we employ functional annotation analysis and investigations into disease severity to support this hypothesis. Conclusion: This study elucidates important differences between dominantly- and recessively-acting disease genes in terms of protein and DNA sequence conservation, paralogy and essentiality. We propose that the division of disease genes by mode of inheritance will enhance both understanding of the disease process and prediction of candidate disease genes in the future.

In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

Background: Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC). Results: Our goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure. Conclusion: We propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models.

Developing discontinuous Galerkin methods for the resolution of the incompressible Navier-Stokes equations

Informe de investigación elaborado a partir de una estancia en el Laboratorio de Diseño Computacional en Aeroespacial en el Massachusetts Institute of Technology (MIT), Estados Unidos, entre noviembre de 2006 y agosto de 2007. La aerodinámica es una rama de la dinámica de fluidos referida al estudio de los movimientos de los líquidos o gases, cuya meta principal es predecir las fuerzas aerodinámicas en un avión o cualquier tipo de vehículo, incluyendo los automóviles. Las ecuaciones de Navier-Stokes representan un estado dinámico del equilibrio de las fuerzas que actúan en cualquier región dada del fluido. Son uno de los sistemas de ecuaciones más útiles porque describen la física de una gran cantidad de fenómenos como corrientes del océano, flujos alrededor de una superficie de sustentación, etc. En el contexto de una tesis doctoral, se está estudiando un flujo viscoso e incompresible, solucionando las ecuaciones de Navier- Stokes incompresibles de una manera eficiente. Durante la estancia en el MIT, se ha utilizado un método de Galerkin discontinuo para solucionar las ecuaciones de Navier-Stokes incompresibles usando, o bien un parámetro de penalti para asegurar la continuidad de los flujos entre elementos, o bien un método de Galerkin discontinuo compacto. Ambos métodos han dado buenos resultados y varios ejemplos numéricos se han simulado para validar el buen comportamiento de los métodos desarrollados. También se han estudiado elementos particulares, los elementos de Raviart y Thomas, que se podrían utilizar en una formulación mixta para obtener un algoritmo eficiente para solucionar problemas numéricos complejos.

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

The role of computational fluid dynamics in the management of unruptured intracranial aneurysms: a clinicians' view

Objective: The importance of hemodynamics in the etiopathogenesis of intracranial aneurysms (IAs) is widely accepted.Computational fluid dynamics (CFD) is being used increasingly for hemodynamic predictions. However, alogn with thecontinuing development and validation of these tools, it is imperative to collect the opinion of the clinicians. Methods: A workshopon CFD was conducted during the European Society of Minimally Invasive Neurological Therapy (ESMINT) Teaching Course,Lisbon, Portugal. 36 delegates, mostly clinicians, performed supervised CFD analysis for an IA, using the @neuFuse softwaredeveloped within the European project @neurIST. Feedback on the workshop was collected and analyzed. The performancewas assessed on a scale of 1 to 4 and, compared with experts’ performance. Results: Current dilemmas in the management ofunruptured IAs remained the most important motivating factor to attend the workshop and majority of participants showedinterest in participating in a multicentric trial. The participants achieved an average score of 2.52 (range 0–4) which was 63% (range 0–100%) of an expert user. Conclusions: Although participants showed a manifest interest in CFD, there was a clear lack ofawareness concerning the role of hemodynamics in the etiopathogenesis of IAs and the use of CFD in this context. More effortstherefore are required to enhance understanding of the clinicians in the subject.

Computational Approach to the Study of Thermal Spin Crossover Phenomena

The key parameters associated to the thermally induced spin crossover process have been calculated for a series of Fe(II) complexes with mono-, bi-, and tridentate ligands. Combination of density functional theory calculations for the geometries and for normal vibrational modes, and highly correlated wave function methods for the energies, allows us to accurately compute the entropy variation associated to the spin transition and the zero-point corrected energy difference between the low- and high-spin states. From these values, the transition temperature, T 1/2, is estimated for different compounds.

CBS: an open platform that integrates predictive methods and epigenetics information to characterize conserved regulatory features in multiple Drosophila genomes.

Background: Information about the composition of regulatory regions is of great value for designing experiments to functionally characterize gene expression. The multiplicity of available applications to predict transcription factor binding sites in a particular locus contrasts with the substantial computational expertise that is demanded to manipulate them, which may constitute a potential barrier for the experimental community. Results: CBS (Conserved regulatory Binding Sites, http://compfly.bio.ub.es/CBS) is a public platform of evolutionarily conserved binding sites and enhancers predicted in multiple Drosophila genomes that is furnished with published chromatin signatures associated to transcriptionally active regions and other experimental sources of information. The rapid access to this novel body of knowledge through a user-friendly web interface enables non-expert users to identify the binding sequences available for any particular gene, transcription factor, or genome region. Conclusions: The CBS platform is a powerful resource that provides tools for data mining individual sequences and groups of co-expressed genes with epigenomics information to conduct regulatory screenings in Drosophila.

Bidding support for computational resources

Peer-reviewed