Refine
Year of publication
- 2014 (46) (remove)
Document Type
- Article (29)
- Doctoral Thesis (10)
- Conference Proceeding (4)
- Monograph/Edited Volume (1)
- Part of a Book (1)
- Preprint (1)
Language
- English (41)
- German (4)
- Multiple languages (1)
Keywords
- Abstraction (1)
- Adaptivity (1)
- Algorithmenablaufplanung (1)
- Algorithmenkonfiguration (1)
- Algorithmenselektion (1)
- Antwortmengenprogrammierung (1)
- Augmentation (1)
- Batch processing (1)
- Boolean constraint solver (1)
- Campus (1)
Institute
- Institut für Informatik und Computational Science (46) (remove)
Lessons Learned
(2014)
This chapter summarizes the experience and the lessons we learned concerning the application of the jABC as a framework for design and execution of scientific workflows. It reports experiences from the domain modeling (especially service integration) and workflow design phases and evaluates the resulting models statistically with respect to the SIB library and hierarchy levels.
The Course's SIB Libraries
(2014)
This chapter gives a detailed description of the service framework underlying all the example projects that form the foundation of this book. It describes the different SIB libraries that we made available for the course “Process modeling in the natural sciences” to provide the functionality that was required for the envisaged applications. The students used these SIB libraries to realize their projects.
In this article, we present our experience with over a decade of strict simplicity orientation in the development and evolution of plug-ins. The point of our approach is to enable our graphical modeling framework jABC to capture plug-in development in a domain-specific setting. The typically quite tedious and technical plug-in development is shifted this way from a programming task to the modeling level, where it can be mastered also by application experts without programming expertise. We show how the classical plug-in development profits from a systematic domain-specific API design and how the level of abstraction achieved this way can be further enhanced by defining adequate building blocks for high-level plug-in modeling. As the resulting plug-in models can be compiled and deployed automatically, our approach decomposes plug-in development into three phases where only the realization phase requires plug-in-specific effort. By using our modeling framework jABC, this effort boils down to graphical, tool-supported process modeling. Furthermore, we support the automatic completion of process sketches for executability. All this will be illustrated along the most recent plug-in-based evolution of the jABC framework, which witnessed quite some bootstrapping effects.
Cloud-RAID
(2014)
Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.