Solving the TTC 2011 Reengineering Case with GReTL

This paper discusses the GReTL reference solution of the TTC 2011 Reengineering case. Given a Java syntax graph, a simple state machine model has to be extracted. The submitted solution covers both the core task and the two extension tasks.Comment: In Proceedings TTC 2011, arXiv:1111.440

Saying Hello World with GReTL - A Solution to the TTC 2011 Instructive Case

This paper discusses the GReTL solution of the TTC 2011 Hello World case. The submitted solution covers all tasks including the optional ones.Comment: In Proceedings TTC 2011, arXiv:1111.440

Migration of Acanthamoeba through Legionella biofilms is regulated by the bacterial Lqs-LvbR network, effector proteins and the flagellum

The environmental bacterium Legionella pneumophila causes the pneumonia Legionnaires' disease. The opportunistic pathogen forms biofilms and employs the Icm/Dot type IV secretion system (T4SS) to replicate in amoebae and macrophages. A regulatory network comprising the Legionella quorum sensing (Lqs) system and the transcription factor LvbR controls bacterial motility, virulence and biofilm architecture. Here we show by comparative proteomics that in biofilms formed by the L. pneumophila ΔlqsR or ΔlvbR regulatory mutants the abundance of proteins encoded by a genomic 'fitness island', metabolic enzymes, effector proteins and flagellar components (e.g. FlaA) varies. ∆lqsR or ∆flaA mutants form 'patchy' biofilms like the parental strain JR32, while ∆lvbR forms a 'mat-like' biofilm. Acanthamoeba castellanii amoebae migrated more slowly through biofilms of L. pneumophila lacking lqsR, lvbR, flaA, a functional Icm/Dot T4SS (∆icmT), or secreted effector proteins. Clusters of bacteria decorated amoebae in JR32, ∆lvbR or ∆icmT biofilms but not in ∆lqsR or ∆flaA biofilms. The amoeba-adherent bacteria induced promoters implicated in motility (PflaA ) or virulence (PsidC , PralF ). Taken together, the Lqs-LvbR network (quorum sensing), FlaA (motility) and the Icm/Dot T4SS (virulence) regulate migration of A. castellanii through L. pneumophila biofilms, and - apart from the T4SS - govern bacterial cluster formation on the amoebae

Functional Analysis of the Legionella pneumophila\textit{Legionella pneumophila} Nitric Oxide Regulatory Network and its Link to Quorum Sensing

Legionella pneumophila is a ubiquitous Gram-negative bacterium and the causative agent of Legionnaires’ disease and Pontiac fever. The facultative intracellular bacterium colonizes natural and man-made water systems. In the environment, Legionella resides in complex biofilms, that comprise several bacterial species and eukaryotic cells. Moreover, the pathogen can infect free-living amoebae such as Acanthamoeba castellanii and replicate intracellularly. Human infection occurs via inhalation of contaminated aerosols. In the lungs, the opportunistic pathogen infects alveolar macrophages and translocates more than 300 effector proteins into the host cell through an Icm/Dot type IV secretion system (Icm/Dot T4SS). This allows the pathogen to multiply within a unique compartment called the Legionella-containing vacuole (LCV). L. pneumophila employs the Legionella quorum sensing (Lqs) system to regulate various traits including the growth phase switch, virulence, and bacterial motility. The Lqs system is linked to the bacterial c-di-GMP metabolism through the pleiotropic transcription factor LvbR. The Lqs system comprises the autoinducer synthase LqsA, which produces the α-hydroxyketone LAI-1 (Legionella autoinducer-1, 3-hydroxypentadecan-4-one), the homologous sensor kinases LqsS and LqsT, and the response regulator LqsR. The interactions between L. pneumophila and its natural host cells in biofilms are poorly understood. In this study, we investigated the interactions of Legionella and the amoeba A. castellanii in mono-species L. pneumophila biofilms using confocal microscopy and flow cytometry. We found that the transcription factor LvbR, the response regulator LqsR, the bacterial flagellum (FlaA) and the Icm/Dot T4SS regulate the migration of A. castellanii through L. pneumophila biofilms. Furthermore, LvbR, LqsR and FlaA were found to govern the adherence and cluster formation of L. pneumophila on the surface of amoebae. The bacterial clusters were found to comprise motile (PflaA-positive) and virulent (PsidC-positive) bacteria. Macrophages and amoeba hosts of L. pneumophila synthesize nitric oxide (NO), a highly reactive gaseous molecule, which freely diffuses across membranes. NO has a bactericidal effect at high (micromolar) concentrations, however at low (nanomolar) concentrations, NO has been shown to act as a signalling molecule in many bacterial species. L. pneumophila possesses three NO sensors, Hnox1, Hnox2 and NosP. To investigate the role of the NO receptors in L. pneumophila signaling, marker-less L. pneumophila mutant strains lacking individual (Hnox1, Hnox2, or NosP) or all three NO receptors (triple knockout, TKO) were generated. We found that in the ΔnosP mutant, the lvbR promoter was upregulated, indicating that NosP negatively regulates LvbR. Furthermore, the NO receptor mutants were impaired for growth in A. castellanii and macrophages. In addition, we found that phenotypic heterogeneity of non-growing/growing bacteria in amoeba was regulated by the NO receptors. Moreover, the NO receptor mutant strains revealed an altered biofilm architecture and no longer responded to NO in biofilms. In summary, the NO receptors Hnox1, Hnox2 and NosP were shown to be involved in NO detection, replication in host cells, intracellular phenotypic heterogeneity, as well as biofilm formation and dispersion. Our results provide a deeper understanding of NO signaling in L. pneumophila and form the basis for further studies on the molecular components and mechanisms involved in intra-species and inter-kingdom NO signaling

A Rewriting Logic Semantics for ATL

As the complexity of model transformation (MT) grows, the need to rely on formal semantics of MT languages becomes a critical issue. Formal semantics provide precise speci cations of the expected behavior of transformations, allowing users to understand them and to use them properly, and MT tool builders to develop correct MT engines, compilers, etc. In addition, formal semantics allow modelers to reason about the MTs and to prove their correctness, something specially important in case of large and complex MTs (with, e.g., hundreds or thousands of rules) for which manual debugging is no longer possible. In this paper we give a formal semantics of the ATL 3.0 model transformation language using rewriting logic and Maude, which allows addressing these issues. Such formalization provides additional bene ts, such as enabling the simulation of the speci cations or giving access to the Maude toolkit to reason about them

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

Program Understanding: A Reengineering Case for the Transformation Tool Contest

In Software Reengineering, one of the central artifacts is the source code of the legacy system in question. In fact, in most cases it is the only definitive artifact, because over the time the code has diverged from the original architecture and design documents. The first task of any reengineering project is to gather an understanding of the system's architecture. Therefore, a common approach is to use parsers to translate the source code into a model conforming to the abstract syntax of the programming language the system is implemented in which can then be subject to querying. Despite querying, transformations can be used to generate more abstract views on the system's architecture. This transformation case deals with the creation of a state machine model out of a Java syntax graph. It is derived from a task that originates from a real reengineering project.Comment: In Proceedings TTC 2011, arXiv:1111.440

Assessing and improving the quality of model transformations

Software is pervading our society more and more and is becoming increasingly complex. At the same time, software quality demands remain at the same, high level. Model-driven engineering (MDE) is a software engineering paradigm that aims at dealing with this increasing software complexity and improving productivity and quality. Models play a pivotal role in MDE. The purpose of using models is to raise the level of abstraction at which software is developed to a level where concepts of the domain in which the software has to be applied, i.e., the target domain, can be expressed e??ectively. For that purpose, domain-speci??c languages (DSLs) are employed. A DSL is a language with a narrow focus, i.e., it is aimed at providing abstractions speci??c to the target domain. This makes that the application of models developed using DSLs is typically restricted to describing concepts existing in that target domain. Reuse of models such that they can be applied for di??erent purposes, e.g., analysis and code generation, is one of the challenges that should be solved by applying MDE. Therefore, model transformations are typically applied to transform domain-speci??c models to other (equivalent) models suitable for di??erent purposes. A model transformation is a mapping from a set of source models to a set of target models de??ned as a set of transformation rules. MDE is gradually being adopted by industry. Since MDE is becoming more and more important, model transformations are becoming more prominent as well. Model transformations are in many ways similar to traditional software artifacts. Therefore, they need to adhere to similar quality standards as well. The central research question discoursed in this thesis is therefore as follows. How can the quality of model transformations be assessed and improved, in particular with respect to development and maintenance? Recall that model transformations facilitate reuse of models in a software development process. We have developed a model transformation that enables reuse of analysis models for code generation. The semantic domains of the source and target language of this model transformation are so far apart that straightforward transformation is impossible, i.e., a semantic gap has to be bridged. To deal with model transformations that have to bridge a semantic gap, the semantics of the source and target language as well as possible additional requirements should be well understood. When bridging a semantic gap is not straightforward, we recommend to address a simpli??ed version of the source metamodel ??rst. Finally, the requirements on the transformation may, if possible, be relaxed to enable automated model transformation. Model transformations that need to transform between models in di??erent semantic domains are expected to be more complex than those that merely transform syntax. The complexity of a model transformation has consequences for its quality. Quality, in general, is a subjective concept. Therefore, quality can be de??ned in di??erent ways. We de??ned it in the context of model transformation. A model transformation can either be considered as a transformation de??nition or as the process of transforming a source model to a target model. Accordingly, model transformation quality can be de??ned in two di??erent ways. The quality of the de??nition is referred to as its internal quality. The quality of the process of transforming a source model to a target model is referred to as its external quality. There are also two ways to assess the quality of a model transformation (both internal and external). It can be assessed directly, i.e., by performing measurements on the transformation de??nition, or indirectly, i.e., by performing measurements in the environment of the model transformation. We mainly focused on direct assessment of internal quality. However, we also addressed external quality and indirect assessment. Given this de??nition of quality in the context of model transformations, techniques can be developed to assess it. Software metrics have been proposed for measuring various kinds of software artifacts. However, hardly any research has been performed on applying metrics for assessing the quality of model transformations. For four model transformation formalisms with di??fferent characteristics, viz., for ASF+SDF, ATL, Xtend, and QVTO, we de??ned sets of metrics for measuring model transformations developed with these formalisms. While these metric sets can be used to indicate bad smells in the code of model transformations, they cannot be used for assessing quality yet. A relation has to be established between the metric sets and attributes of model transformation quality. For two of the aforementioned metric sets, viz., the ones for ASF+SDF and for ATL, we conducted an empirical study aiming at establishing such a relation. From these empirical studies we learned what metrics serve as predictors for di??erent quality attributes of model transformations. Metrics can be used to quickly acquire insights into the characteristics of a model transformation. These insights enable increasing the overall quality of model transformations and thereby also their maintainability. To support maintenance, and also development in a traditional software engineering process, visualization techniques are often employed. For model transformations this appears as a feasible approach as well. Currently, however, there are few visualization techniques available tailored towards analyzing model transformations. One of the most time-consuming processes during software maintenance is acquiring understanding of the software. We expect that this holds for model transformations as well. Therefore, we presented two complementary visualization techniques for facilitating model transformation comprehension. The ??rst-technique is aimed at visualizing the dependencies between the components of a model transformation. The second technique is aimed at analyzing the coverage of the source and target metamodels by a model transformation. The development of the metric sets, and in particular the empirical studies, have led to insights considering the development of model transformations. Also, the proposed visualization techniques are aimed at facilitating the development of model transformations. We applied the insights acquired from the development of the metric sets as well as the visualization techniques in the development of a chain of model transformations that bridges a number of semantic gaps. We chose to solve this transformational problem not with one model transformation, but with a number of smaller model transformations. This should lead to smaller transformations, which are more understandable. The language on which the model transformations are de??ned, was subject to evolution. In particular the coverage visualization proved to be bene??cial for the co-evolution of the model transformations. Summarizing, we de??ned quality in the context of model transformations and addressed the necessity for a methodology to assess it. Therefore, we de??ned metric sets and performed empirical studies to validate whether they serve as predictors for model transformation quality. We also proposed a number of visualizations to increase model transformation comprehension. The acquired insights from developing the metric sets and the empirical studies, as well as the visualization tools, proved to be bene??cial for developing model transformations

Global network of computational biology communities: ISCB's regional student groups breaking barriers [version 1; peer review: Not peer reviewed]

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”.Fil: Shome, Sayane. University of Iowa; Estados UnidosFil: Parra, Rodrigo Gonzalo. European Molecular Biology Laboratory; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fatima, Nazeefa. Uppsala Universitet; SueciaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Cuypers, Bart. Universiteit Antwerp; BélgicaFil: Moosa, Yumna. University of KwaZulu Natal; SudáfricaFil: Da Rocha Coimbra, Nilson. Universidade Federal de Minas Gerais; BrasilFil: Assis, Juliana. Universidade Federal de Minas Gerais; BrasilFil: Giner Delgado, Carla. Universitat Autònoma de Barcelona; EspañaFil: Dönertaş, Handan Melike. European Molecular Biology Laboratory. European Bioinformatics Institute; Reino UnidoFil: Cuesta Astroz, Yesid. Universidad de Antioquia; Colombia. Universidad Ces. Facultad de Medicina.; ColombiaFil: Saarunya, Geetha. University of South Carolina; Estados UnidosFil: Allali, Imane. Universite Mohammed V. Rabat; Otros paises de África. University of Cape Town; SudáfricaFil: Gupta, Shruti. Jawaharlal Nehru University; IndiaFil: Srivastava, Ambuj. Indian Institute of Technology Madras; IndiaFil: Kalsan, Manisha. Jawaharlal Nehru University; IndiaFil: Valdivia, Catalina. Universidad Andrés Bello; ChileFil: Olguín Orellana, Gabriel José. Universidad de Talca; ChileFil: Papadimitriou, Sofia. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Parisi, Daniele. Katholikie Universiteit Leuven; BélgicaFil: Kristensen, Nikolaj Pagh. Technical University of Denmark; DinamarcaFil: Rib, Leonor. Universidad de Copenhagen; DinamarcaFil: Guebila, Marouen Ben. University of Luxembourg; LuxemburgoFil: Bauer, Eugen. University of Luxembourg; LuxemburgoFil: Zaffaroni, Gaia. University of Luxembourg; LuxemburgoFil: Bekkar, Amel. Universite de Lausanne; SuizaFil: Ashano, Efejiro. APIN Public Health Initiatives; NigeriaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Moreyra, Nicolás Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin