Un nuovo modello computazionale di fibra di Purkinje umana: dal modello medio alla popolazione di modelli e ritorno.

Il lavoro presentato in questa tesi è stato svolto presso il Department of Computer Science, University of Oxford, durante il mio periodo all’estero nel Computational Biology Group. Scopo del presente lavoro è stato lo sviluppo di un modello matematico del potenziale d’azione per cellule umane cardiache di Purkinje. Tali cellule appartengono al sistema di conduzione elettrico del cuore, e sono considerate molto importanti nella genesi di aritmie. Il modello, elaborato in linguaggio Matlab, è stato progettato utilizzando la tecnica delle Popolazione di Modelli, un innovativo approccio alla modellazione cellulare sviluppato recentemente proprio dal Computational Biology Group. Tale modello è stato sviluppato in 3 fasi: • Inizialmente è stato sviluppato un nuovo modello matematico di cellula umana del Purkinje cardiaco, tenendo in considerazione i modelli precedenti disponibili in letteratura e le più recenti pubblicazioni in merito alle caratteristiche elettrofisiologiche proprie della cellula cardiaca umana di Purkinje. Tale modello è stato costruito a partire dall’attuale gold standard della modellazione cardiaca ventricolare umana, il modello pubblicato da T. O’Hara e Y. Rudy nel 2011, modificandone sia le specifiche correnti ioniche che la struttura interna cellulare. • Il modello così progettato è stato, poi, utilizzato come “modello di base” per la costruzione di una popolazione di 3000 modelli, tramite la variazione di alcuni parametri del modello all’interno di uno specifico range. La popolazione così generata è stata calibrata sui dati sperimentali di cellule umane del Purkinje. A valle del processo di calibrazione si è ottenuta una popolazione di 76 modelli. • A partire dalla popolazione rimanente, è stato ricavato un nuovo modello ai valori medi, che riproduce le principali caratteristiche del potenziale d’azione di una cellula di Purkinje cardiaca umana, e che rappresenta il dataset sperimentale utilizzato nel processo di calibrazione.

Protein and Drug Design Algorithms Using Improved Biophysical Modeling

This thesis focuses on the development of algorithms that will allow protein design calculations to incorporate more realistic modeling assumptions. Protein design algorithms search large sequence spaces for protein sequences that are biologically and medically useful. Better modeling could improve the chance of success in designs and expand the range of problems to which these algorithms are applied. I have developed algorithms to improve modeling of backbone flexibility (DEEPer) and of more extensive continuous flexibility in general (EPIC and LUTE). I’ve also developed algorithms to perform multistate designs, which account for effects like specificity, with provable guarantees of accuracy (COMETS), and to accommodate a wider range of energy functions in design (EPIC and LUTE).

Whole Genome Analysis of Gene Expression Reveals Coordinated Activation of Signaling and Metabolic Pathways during Pollen-Pistil Interactions in Arabidopsis

Plant reproduction depends on the concerted activation of many genes to ensure correct communication between pollen and pistil. Here, we queried the whole transcriptome of Arabidopsis (Arabidopsis thaliana) in order to identify genes with specific reproductive functions. We used the Affymetrix ATH1 whole genome array to profile wild-type unpollinated pistils and unfertilized ovules. By comparing the expression profile of pistils at 0.5, 3.5, and 8.0 h after pollination and applying a number of statistical and bioinformatics criteria, we found 1,373 genes differentially regulated during pollen-pistil interactions. Robust clustering analysis grouped these genes in 16 time-course clusters representing distinct patterns of regulation. Coregulation within each cluster suggests the presence of distinct genetic pathways, which might be under the control of specific transcriptional regulators. A total of 78% of the regulated genes were expressed initially in unpollinated pistil and/or ovules, 15% were initially detected in the pollen data sets as enriched or preferentially expressed, and 7% were induced upon pollination. Among those, we found a particular enrichment for unknown transcripts predicted to encode secreted proteins or representing signaling and cell wall-related proteins, which may function by remodeling the extracellular matrix or as extracellular signaling molecules. A strict regulatory control in various metabolic pathways suggests that fine-tuning of the biochemical and physiological cellular environment is crucial for reproductive success. Our study provides a unique and detailed temporal and spatial gene expression profile of in vivo pollen-pistil interactions, providing a framework to better understand the basis of the molecular mechanisms operating during the reproductive process in higher plants.

Relating bisimulations with attractors in boolean network models

When studying a biological regulatory network, it is usual to use boolean network models. In these models, boolean variables represent the behavior of each component of the biological system. Taking in account that the size of these state transition models grows exponentially along with the number of components considered, it becomes important to have tools to minimize such models. In this paper, we relate bisimulations, which are relations used in the study of automata (general state transition models) with attractors, which are an important feature of biological boolean models. Hence, we support the idea that bisimulations can be important tools in the study some main features of boolean network models.We also discuss the differences between using this approach and other well-known methodologies to study this kind of systems and we illustrate it with some examples.

A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2A for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.

An updated evolutionary classification of CRISPR–Cas systems

The evolution of CRISPR–cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR–cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR–Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized.

Integer Linear Programming for Sequence Problems: A general approach to reduce the problem size

Sequence problems belong to the most challenging interdisciplinary topics of the actuality. They are ubiquitous in science and daily life and occur, for example, in form of DNA sequences encoding all information of an organism, as a text (natural or formal) or in form of a computer program. Therefore, sequence problems occur in many variations in computational biology (drug development), coding theory, data compression, quantitative and computational linguistics (e.g. machine translation). In recent years appeared some proposals to formulate sequence problems like the closest string problem (CSP) and the farthest string problem (FSP) as an Integer Linear Programming Problem (ILPP). In the present talk we present a general novel approach to reduce the size of the ILPP by grouping isomorphous columns of the string matrix together. The approach is of practical use, since the solution of sequence problems is very time consuming, in particular when the sequences are long.

Creación de marca mediante la utilización de mecanismos estratégicos comunitarios y marketing

El presente documento pretende mostrar la manera como se debe ejecutar la creación de marca mediante la utilización de mecanismos estratégicos comunitarios y marketing. El objetivo del estudio se basa en encontrar los mecanismos adecuados para el desarrollo y creación de una marca enfocándose en el análisis de las principales prácticas y modelos desarrollados en el área del marketing, examinando el impacto que la marca pueda generar en la comunidad en la cual la organización está incluida, estableciendo además un conexión directa con el modo de vida de los consumidores. Durante el desarrollo del documento se demuestra que las estrategias de marketing aplicadas por cada compañía, sirven para construir una relación estrecha y fuerte con todos los agentes involucrados en la construcción de una marca, principalmente con los clientes, ya que la forma más efectiva de establecer relaciones a largo plazo, es enfocándose exclusivamente en las necesidades desarrolladas por los consumidores, y a partir de ellas ajustar los valores (misión, visión, cultura organizacional, objetivos) de la organización. Estas estrategias comunitarias son también influenciadas por varios factores internos y externos a la organización, los cuales deben ser tenidos en cuenta al momento de elegir la estrategia adecuada. Los mecanismos estratégicos que desarrollan las empresas pueden cambiar significativamente de un sector comercial a otro, la importancia de las necesidades que se deben suplir y el consumidor final se deben evaluar desde un aspecto comunitario, entendiendo como comunidad como el conjunto de grupos sociales y comerciales que tienen relación directa o indirecta con la empresa. Con la investigación llevada a cabo acerca de las estrategias que deben aplicar las compañías se concluye que las marcas reflejan la imagen que la empresa transmite a sus compradores estableciendo una relación emocional entre los consumidores y la marca desarrollada, además de estimular la oferta y demanda del negocio. Se espera que por medio de la obtención de información teórica y conceptual, se pueda aclarar la manera como se puede desarrollar la creación de una marca por medio de la correcta utilización de mecanismos estratégicos comunitarios y de marketing.

Exploring the multifactorial nature of autism through computational systems biology: Calcium and the Rho GTPase RAC1 under the spotlight

Autism is a neurodevelopmental disorder characterized by impaired social interaction and communication accompanied with repetitive behavioral patterns and unusual stereotyped interests. Autism is considered a highly heterogeneous disorder with diverse putative causes and associated factors giving rise to variable ranges of symptomatology. Incidence seems to be increasing with time, while the underlying pathophysiological mechanisms remain virtually uncharacterized (or unknown). By systematic review of the literature and a systems biology approach, our aims were to examine the multifactorial nature of autism with its broad range of severity, to ascertain the predominant biological processes, cellular components, and molecular functions integral to the disorder, and finally, to elucidate the most central contributions (genetic and/or environmental) in silico. With this goal, we developed an integrative network model for gene-environment interactions (GENVI model) where calcium (Ca2+) was shown to be its most relevant node. Moreover, considering the present data from our systems biology approach together with the results from the differential gene expression analysis of cerebellar samples from autistic patients, we believe that RAC1, in particular, and the RHO family of GTPases, in general, could play a critical role in the neuropathological events associated with autism. © 2013 Springer Science+Business Media New York.

An integrated molecular biology and computational approach to CYP1A1 expression in human brain

The cytochromes P450 comprise a superfamily of heme-containing mono-oxygenases. These enzymes metabolize numerous xenobiotics, but also play a role in metabolism of endogenous compounds. The P450 1A1 enzyme generally metabolizes polycyclic aromatic hydrocarbons, and its expression can be induced by aryl hydrocarbon receptor (AhR) activation. CYP1A1 is an exception to the generality that the majority of CYPs demonstrate highest expression in liver; CYP1Al is present in numerous extrahepatic tissues, including brain. This P450 has been observed in two forms, wildtype (WT) and brain variant (BV), arising from alternatively spliced mRNA transcripts. The CYP1A1 BV mRNA presented an exon deletion and was detected in human brain but not liver tissue of the same individuals. ^ Quantitative PCR analyses were performed to determine CYP1A1 WT and BV transcript expression levels in normal, bipolar disorder or schizophrenic groups. In our samples, we show that CYP1A1 BV mRNA, when present, is found alongside the full-length form. Furthermore, we demonstrate a significant decrease in expression of CYP1A1 in patients with bipolar disorder or schizophrenia. The expression level was not influenced by post-mortem interval, tissue pH, age, tobacco use, or lifetime antipsychotic medication load. ^ There is no indication of increased brain CYP1A1 expression in normal smokers versus non-smokers in these samples. We observed slightly increased CYP1A1 expression only in bipolar and schizophrenic smokers versus non-smokers. This may be indicative of complex interactions between neuronal chemical environments and AhR-mediated CYP1A1 induction in brain. ^ Structural homology modeling demonstrated that P450 1A1 BV has several alterations to positions/orientations of substrate recognition site residues compared to the WT isoform. Automated substrate docking was employed to investigate the potential binding of neurological signaling molecules and neurotropic drugs, as well as to differentiate specificities of the two P450 1A1 isoforms. We consistently observed that the BV isoform produced energetically favorable substrate dockings in orientations not observed for the same substrate in the WT isoform. These results demonstrated that structural differences, namely an expanded substrate access channel and active site, confer greater capacity for unique compound docking positions suggesting a metabolic profile distinct from the wildtype form for these test compounds. ^

Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013,