Geometry and physics of catenanes applied to the study of DNA replication.

The concept of ideal geometric configurations was recently applied to the classification and characterization of various knots. Different knots in their ideal form (i.e., the one requiring the shortest length of a constant-diameter tube to form a given knot) were shown to have an overall compactness proportional to the time-averaged compactness of thermally agitated knotted polymers forming corresponding knots. This was useful for predicting the relative speed of electrophoretic migration of different DNA knots. Here we characterize the ideal geometric configurations of catenanes (called links by mathematicians), i.e., closed curves in space that are topologically linked to each other. We demonstrate that the ideal configurations of different catenanes show interrelations very similar to those observed in the ideal configurations of knots. By analyzing literature data on electrophoretic separations of the torus-type of DNA catenanes with increasing complexity, we observed that their electrophoretic migration is roughly proportional to the overall compactness of ideal representations of the corresponding catenanes. This correlation does not apply, however, to electrophoretic migration of certain replication intermediates, believed up to now to represent the simplest torus-type catenanes. We propose, therefore, that freshly replicated circular DNA molecules, in addition to forming regular catenanes, may also form hemicatenanes.

Synchrotron-based X-ray tomographic microscopy for rock physics investigations

Synchrotron radiation X-ray tomographic microscopy is a nondestructive method providing ultra-high-resolution 3D digital images of rock microstructures. We describe this method and, to demonstrate its wide applicability, we present 3D images of very different rock types: Berea sandstone, Fontainebleau sandstone, dolomite, calcitic dolomite, and three-phase magmatic glasses. For some samples, full and partial saturation scenarios are considered using oil, water, and air. The rock images precisely reveal the 3D rock microstructure, the pore space morphology, and the interfaces between fluids saturating the same pore. We provide the raw image data sets as online supplementary material, along with laboratory data describing the rock properties. By making these data sets available to other research groups, we aim to stimulate work based on digital rock images of high quality and high resolution. We also discuss and suggest possible applications and research directions that can be pursued on the basis of our data.

Computational anatomy for studying use-dependant brain plasticity.

In this article we provide a comprehensive literature review on the in vivo assessment of use-dependant brain structure changes in humans using magnetic resonance imaging (MRI) and computational anatomy. We highlight the recent findings in this field that allow the uncovering of the basic principles behind brain plasticity in light of the existing theoretical models at various scales of observation. Given the current lack of in-depth understanding of the neurobiological basis of brain structure changes we emphasize the necessity of a paradigm shift in the investigation and interpretation of use-dependent brain plasticity. Novel quantitative MRI acquisition techniques provide access to brain tissue microstructural properties (e.g., myelin, iron, and water content) in-vivo, thereby allowing unprecedented specific insights into the mechanisms underlying brain plasticity. These quantitative MRI techniques require novel methods for image processing and analysis of longitudinal data allowing for straightforward interpretation and causality inferences.

Causal explanations and scientific realism in particle physics

Particle physics studies highly complex processes which cannot be directly observed. Scientific realism claims that we are nevertheless warranted in believing that these processes really occur and that the objects involved in them really exist. This dissertation defends a version of scientific realism, called causal realism, in the context of particle physics. I start by introducing the central theses and arguments in the recent philosophical debate on scientific realism (chapter 1), with a special focus on an important presupposition of the debate, namely common sense realism. Chapter 2 then discusses entity realism, which introduces a crucial element into the debate by emphasizing the importance of experiments in defending scientific realism. Most of the chapter is concerned with Ian Hacking's position, but I also argue that Nancy Cartwright's version of entity realism is ultimately preferable as a basis for further development. In chapter 3,1 take a step back and consider the question whether the realism debate is worth pursuing at all. Arthur Fine has given a negative answer to that question, proposing his natural ontologica! attitude as an alternative to both realism and antirealism. I argue that the debate (in particular the realist side of it) is in fact less vicious than Fine presents it. The second part of my work (chapters 4-6) develops, illustrates and defends causal realism. The key idea is that inference to the best explanation is reliable in some cases, but not in others. Chapter 4 characterizes the difference between these two kinds of cases in terms of three criteria which distinguish causal from theoretical warrant. In order to flesh out this distinction, chapter 5 then applies it to a concrete case from the history of particle physics, the discovery of the neutrino. This case study shows that the distinction between causal and theoretical warrant is crucial for understanding what it means to "directly detect" a new particle. But the distinction is also an effective tool against what I take to be the presently most powerful objection to scientific realism: Kyle Stanford's argument from unconceived alternatives. I respond to this argument in chapter 6, and I illustrate my response with a discussion of Jean Perrin's experimental work concerning the atomic hypothesis. In the final part of the dissertation, I turn to the specific challenges posed to realism by quantum theories. One of these challenges comes from the experimental violations of Bell's inequalities, which indicate a failure of locality in the quantum domain. I show in chapter 7 how causal realism can further our understanding of quantum non-locality by taking account of some recent experimental results. Another challenge to realism in quantum mechanics comes from delayed-choice experiments, which seem to imply that certain aspects of what happens in an experiment can be influenced by later choices of the experimenter. Chapter 8 analyzes these experiments and argues that they do not warrant the antirealist conclusions which some commentators draw from them. It pays particular attention to the case of delayed-choice entanglement swapping and the corresponding question whether entanglement is a real physical relation. In chapter 9,1 finally address relativistic quantum theories. It is often claimed that these theories are incompatible with a particle ontology, and this calls into question causal realism's commitment to localizable and countable entities. I defend the commitments of causal realism against these objections, and I conclude with some remarks connecting the interpretation of quantum field theory to more general metaphysical issues confronting causal realism.

Experimental and computational approaches to quantitative proteomics: status quo and outlook.

Proteomics has come a long way from the initial qualitative analysis of proteins present in a given sample at a given time ("cataloguing") to large-scale characterization of proteomes, their interactions and dynamic behavior. Originally enabled by breakthroughs in protein separation and visualization (by two-dimensional gels) and protein identification (by mass spectrometry), the discipline now encompasses a large body of protein and peptide separation, labeling, detection and sequencing tools supported by computational data processing. The decisive mass spectrometric developments and most recent instrumentation news are briefly mentioned accompanied by a short review of gel and chromatographic techniques for protein/peptide separation, depletion and enrichment. Special emphasis is placed on quantification techniques: gel-based, and label-free techniques are briefly discussed whereas stable-isotope coding and internal peptide standards are extensively reviewed. Another special chapter is dedicated to software and computing tools for proteomic data processing and validation. A short assessment of the status quo and recommendations for future developments round up this journey through quantitative proteomics.

Computational fluid dynamics of the right ventricular outflow tract and of the pulmonary artery: a bench model of flow dynamics.

OBJECTIVES: The reconstruction of the right ventricular outflow tract (RVOT) with valved conduits remains a challenge. The reoperation rate at 5 years can be as high as 25% and depends on age, type of conduit, conduit diameter and principal heart malformation. The aim of this study is to provide a bench model with computer fluid dynamics to analyse the haemodynamics of the RVOT, pulmonary artery, its bifurcation, and left and right pulmonary arteries that in the future may serve as a tool for analysis and prediction of outcome following RVOT reconstruction. METHODS: Pressure, flow and diameter at the RVOT, pulmonary artery, bifurcation of the pulmonary artery, and left and right pulmonary arteries were measured in five normal pigs with a mean weight of 24.6 ± 0.89 kg. Data obtained were used for a 3D computer fluid-dynamics simulation of flow conditions, focusing on the pressure, flow and shear stress profile of the pulmonary trunk to the level of the left and right pulmonary arteries. RESULTS: Three inlet steady flow profiles were obtained at 0.2, 0.29 and 0.36 m/s that correspond to the flow rates of 1.5, 2.0 and 2.5 l/min flow at the RVOT. The flow velocity profile was constant at the RVOT down to the bifurcation and decreased at the left and right pulmonary arteries. In all three inlet velocity profiles, low sheer stress and low-velocity areas were detected along the left wall of the pulmonary artery, at the pulmonary artery bifurcation and at the ostia of both pulmonary arteries. CONCLUSIONS: This computed fluid real-time model provides us with a realistic picture of fluid dynamics in the pulmonary tract area. Deep shear stress areas correspond to a turbulent flow profile that is a predictive factor for the development of vessel wall arteriosclerosis. We believe that this bench model may be a useful tool for further evaluation of RVOT pathology following surgical reconstructions.

Expanding molecular modeling and design tools to non-natural sidechains.

Protein-protein interactions encode the wiring diagram of cellular signaling pathways and their deregulations underlie a variety of diseases, such as cancer. Inhibiting protein-protein interactions with peptide derivatives is a promising way to develop new biological and therapeutic tools. Here, we develop a general framework to computationally handle hundreds of non-natural amino acid sidechains and predict the effect of inserting them into peptides or proteins. We first generate all structural files (pdb and mol2), as well as parameters and topologies for standard molecular mechanics software (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to increase peptide ligand binding affinity. Our results obtained on non-natural mutants of a BCL9 peptide targeting beta-catenin show very good correlation between predicted and experimental binding free-energies, indicating that such predictions can be used to design new inhibitors. Data generated in this work, as well as PyMOL and UCSF Chimera plug-ins for user-friendly visualization of non-natural sidechains, are all available at http://www.swisssidechain.ch. Our results enable researchers to rapidly and efficiently work with hundreds of non-natural sidechains.

iLOGP: A Simple, Robust, and Efficient Description of n-Octanol/Water Partition Coefficient for Drug Design Using the GB/SA Approach.

The n-octanol/water partition coefficient (log Po/w) is a key physicochemical parameter for drug discovery, design, and development. Here, we present a physics-based approach that shows a strong linear correlation between the computed solvation free energy in implicit solvents and the experimental log Po/w on a cleansed data set of more than 17,500 molecules. After internal validation by five-fold cross-validation and data randomization, the predictive power of the most interesting multiple linear model, based on two GB/SA parameters solely, was tested on two different external sets of molecules. On the Martel druglike test set, the predictive power of the best model (N = 706, r = 0.64, MAE = 1.18, and RMSE = 1.40) is similar to six well-established empirical methods. On the 17-drug test set, our model outperformed all compared empirical methodologies (N = 17, r = 0.94, MAE = 0.38, and RMSE = 0.52). The physical basis of our original GB/SA approach together with its predictive capacity, computational efficiency (1 to 2 s per molecule), and tridimensional molecular graphics capability lay the foundations for a promising predictor, the implicit log P method (iLOGP), to complement the portfolio of drug design tools developed and provided by the SIB Swiss Institute of Bioinformatics.

Mutational and computational analysis of the alpha(1b)-adrenergic receptor. Involvement of basic and hydrophobic residues in receptor activation and G protein coupling.

To investigate their role in receptor coupling to G(q), we mutated all basic amino acids and some conserved hydrophobic residues of the cytosolic surface of the alpha(1b)-adrenergic receptor (AR). The wild type and mutated receptors were expressed in COS-7 cells and characterized for their ligand binding properties and ability to increase inositol phosphate accumulation. The experimental results have been interpreted in the context of both an ab initio model of the alpha(1b)-AR and of a new homology model built on the recently solved crystal structure of rhodopsin. Among the twenty-three basic amino acids mutated only mutations of three, Arg(254) and Lys(258) in the third intracellular loop and Lys(291) at the cytosolic extension of helix 6, markedly impaired the receptor-mediated inositol phosphate production. Additionally, mutations of two conserved hydrophobic residues, Val(147) and Leu(151) in the second intracellular loop had significant effects on receptor function. The functional analysis of the receptor mutants in conjunction with the predictions of molecular modeling supports the hypothesis that Arg(254), Lys(258), as well as Leu(151) are directly involved in receptor-G protein interaction and/or receptor-mediated activation of the G protein. In contrast, the residues belonging to the cytosolic extensions of helices 3 and 6 play a predominant role in the activation process of the alpha(1b)-AR. These findings contribute to the delineation of the molecular determinants of the alpha(1b)-AR/G(q) interface.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.