Differential binding studies applying functional protein microarrays and surface plasmon resonance

A variety of different in vivo and in vitro technologies provide comprehensive insights in protein-protein interaction networks. Here we demonstrate a novel approach to analyze, verify and quantify putative interactions between two members of the S100 protein family and 80 recombinant proteins derived from a proteome-wide protein expression library. Surface plasmon resonance (SPR) using Biacore technology and functional protein microarrays were used as two independent methods to study protein-protein interactions. With this combined approach we were able to detect nine calcium-dependent interactions between Arg-Gly-Ser-(RGS)-His6 tagged proteins derived from the library and GST-tagged S100B and S100A6, respectively. For the protein microarray affinity-purified proteins from the expression library were spotted onto modified glass slides and probed with the S100 proteins. SPR experiments were performed in the same setup and in a vice-versa approach reversing analytes and ligands to determine distinct association and dissociation patterns of each positive interaction. Besides already known interaction partners, several novel binders were found independently with both detection methods, albeit analogous immobilization strategies had to be applied in both assays

The 20S Proteasome Splicing Activity Discovered by SpliceMet

The identification of proteasome-generated spliced peptides (PSP) revealed a new unpredicted activity of the major cellular protease. However, so far characterization of PSP was entirely dependent on the availability of patient-derived cytotoxic CD8+ T lymphocytes (CTL) thus preventing a systematic investigation of proteasome-catalyzed peptide splicing (PCPS). For an unrestricted PSP identification we here developed SpliceMet, combining the computer-based algorithm ProteaJ with in vitro proteasomal degradation assays and mass spectrometry. By applying SpliceMet for the analysis of proteasomal processing products of four different substrate polypeptides, derived from human tumor as well as viral antigens, we identified fifteen new spliced peptides generated by PCPS either by cis or from two separate substrate molecules, i.e., by trans splicing. Our data suggest that 20S proteasomes represent a molecular machine that, due to its catalytic and structural properties, facilitates the generation of spliced peptides, thereby providing a pool of qualitatively new peptides from which functionally relevant products may be selected

Essays in Corporate Finance and Innovation

Las inversiones corporativas en innovación crean nuevos conocimientos tecnológicos, que son la causa principal del crecimiento económico. Por lo tanto, es fundamental comprender los determinantes de las inversiones en innovación y la difusión de conocimientos innovadores, y sus consecuencias para la economía y la sociedad en general. En esta tesis, contribuyo a nuestra comprensión de estos determinantes y consecuencias. Primero, estudio la influencia heterogénea de diferentes propietarios institucionales sobre las estrategias de innovación y la producción de las empresas (Capítulo 1). En segundo lugar, analizo cómo la propiedad común de los inversionistas institucionales influye en la combinación de empresas en el mercado de tecnología, impactando la reasignación de conocimientos innovadores (Capítulo 2). En tercer lugar, investigo el efecto de la automatización en la reasignación sectorial de trabajo y capital en la economía (Capítulo 3).Corporate investments in innovation create new technological know-how, which is the primary cause of economic growth. Therefore, it is critical to understand the determinants of investments in innovation and the dissemination of innovative know-how, and its consequences for the economy and the broader society. In this thesis, I contribute to our understanding of these determinants and consequences. First, I study the heterogeneous influence of different institutional owners on firms' innovation strategies and output (Chapter 1). Second, I analyze how common ownership by institutional investors influences firm matching in the market for technology, impacting the reallocation of innovative know-how (Chapter 2). Third, I investigate the effect of automation on the sectoral reallocation of labor and capital in the economy (Chapter 3)

Fusion moves for graph matching

We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching. Inspired by the success of the fusion moves technique developed for multilabel discrete Markov random fields, we investigate its applicability to graph matching. In particular, we show how fusion moves can be efficiently combined with the dedicated state-of-the-art dual methods that have recently shown superior results in computer vision and bioimaging applications. As our empirical evaluation on a wide variety of graph matching datasets suggests, fusion moves significantly improve performance of these methods in terms of speed and quality of the obtained solutions. Our method sets a new state-of-the-art with a notable margin with respect to its competitors

A comparative study of graph matching algorithms in computer vision

The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: 1.) popular problem instances are exactly solvable in substantially less than 1 second and, therefore, are insufficient for future empirical evaluations; 2.) the most popular baseline methods are highly inferior to the best available methods; 3.) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices