Computational analysis of alternative splicing in human and mice

Im ersten Teil wurden Transkript-Spleißstellen untersucht, mit dem Ziel, alternative und Referenzspleißstellen zu unterscheiden. Die Ergebnisse belegen, dass sich beide Klassen von Spleißstellen durch einen Spleißstellen-Score und vermehrtes Auftreten von Spleißfaktor-Bindemotiven in Umgebung der Spleißstellen abgrenzen lassen. Zusätzlich konnte eine positive Korrelation zwischen der Häufigkeit der Nutzung bestimmter Spleißstellen und dem Spleißstellen-Score in beiden Vergleichsklassen nachgewiesen werden. Diese Abhängigkeit impliziert, dass die Genauigkeit der Annotation alternativer Spleißvarianten mit der Anzahl beobachteter Transkripte steigt. Im zweiten Teil wurde das Spleißsignalmotiv GYNNGY untersucht, welches mehr als 40% aller überlappenden Donor-Spleißsignale ausmacht. Mittels in silico Analysen und experimenteller Validierung wurde die Plausibilität dieses subtilen Spleißmusters bestätigt. Der Vergleich mit anderen humanen Spleißvarianten sowie mit Tandem Donoren in Maus-Transkripten zeigte zudem ausgeprägte Unterschiede bezüglich des Spleißstellen-Scores, der Konservierung, sowie dem Vorkommen von Spleißfaktoren-Bindemotiven. Die Verschiebung des Leserasters durch alternatives Spleißen an GYNNGY-Donoren lässt auf eine komplexe Rolle im RNA-Reifungsprozess schließen. Im dritten Teil wurden Reaktionen des spleißosomalen Makrokomples aus publizierten, experimentellen Daten zusammengestellt und mit Hilfe der Petri-Netz-Theorie in einem qualitativen Modell dargestellt. Unter Annahme eines Steady-State Systems wurden minimale, semipositive T-Invarianten berechnet und zur Validierung des Modells herangezogen. Auf Grundlage der vollständigen Abdeckung des Reaktionsnetzwerks mit T-Invarianten konnten weitere Strukturmerkmale, wie Maximal-Gemeinsame Transitions.Mengen und T-Cluster berechnet werden, welche wichtige Stadien des Spleißosomaufbaus widerspiegeln

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

PSA 2016

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2016

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

THE SUM OF THE PARTS: HEURISTIC STRATEGIES IN SYSTEMS BIOLOGY

This thesis addresses philosophical issues regarding the young field of systems biology. Systems biologists commonly present their approach as a superior alternative to \u2018traditional\u2019 molecular biology that they describe as being overly \u2018reductionist.\u2019 However, the heterogeneity of systems approaches makes it difficult to understand what \u2018the\u2019 approach of systems biology exactly consists in. Here I propose a framework for the systematic comparison of different scientific approaches in biology. I argue that the relevant issues arise at the level of strategies of mechanistic discovery. These strategies are best understood as \u2018heuristic,\u2019 that is, as tools to reduce the complexity of a given research task. While having the virtue of making the search for mechanisms more efficient, heuristic strategies rely on particular assumptions about the system under study. This can introduce bias and lead biologists to underestimate the actual complexity of the system. Framing the analysis in terms of heuristic strategies pro- vides a precise way to distinguish between different approaches and to better understand the ongoing rhetoric battles. I discuss a number of case studies, both from molecular biology and from systems biology. I argue that the traditional approach of molecular biology relies on a relatively well-defined set of heuristics that corresponds to a particular idea of the organization and complexity of living systems. Approaches in systems biology relax some of the underlying assumptions of the traditional approach, notably by applying tools of mathematical modeling, but they have to make use of alternative heuristics in order to be efficient. As a result, they rely on different assumptions about organization and complexity. My detailed discussion of case studies reveals that there are a number of different systems approaches that can be distinguished by analyzing their heuristic character. The ambition of systems biologists to build formal models of biological mechanisms, however, has the virtue of making many of the underlying assumptions explicit which helps to recognize and reduce bias, and moreover facilitates the integration of different approaches. Some of the issues touched upon also have relevance for more general questions in the philosophy of biology. Assumptions about biological organization and complexity can heavily influence what we think of as a good scientific explanation. Since systems biology puts into question some of these assumptions, we might be forced to revise our ideas about mechanistic explanation. I argue that notably the concept of biological robustness has to be taken into account by philosophers who are thinking about mechanisms in biology

Wings in Orbit: Scientific and Engineering Legacies of the Space Shuttle, 1971-2010

The Space Shuttle is an engineering marvel perhaps only exceeded by the station itself. The shuttle was based on the technology of the 1960s and early 1970s. It had to overcome significant challenges to make it reusable. Perhaps the greatest challenges were the main engines and the Thermal Protection System. The program has seen terrible tragedy in its 3 decades of operation, yet it has also seen marvelous success. One of the most notable successes is the Hubble Space Telescope, a program that would have been a failure without the shuttle's capability to rendezvous, capture, repair, as well as upgrade. Now Hubble is a shining example of success admired by people around the world. As the program comes to a close, it is important to capture the legacy of the shuttle for future generations. That is what "Wings In Orbit" does for space fans, students, engineers, and scientists. This book, written by the men and women who made the program possible, will serve as an excellent reference for building future space vehicles. We are proud to have played a small part in making it happen. Our journey to document the scientific and engineering accomplishments of this magnificent winged vehicle began with an audacious proposal: to capture the passion of those who devoted their energies to its success while answering the question "What are the most significant accomplishments?" of the longestoperating human spaceflight program in our nation s history. This is intended to be an honest, accurate, and easily understandable account of the research and innovation accomplished during the era

PSA 2018

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018