The Process-Interaction-Model: a common representation of rule-based and logical models allows studying signal transduction on different levels of detail

A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input–output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous.We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical and eventually to logic-based ODE models. Importantly, systems and network properties determined in the rougher representation are conserved during these transformations.

Attenuation and scattering of light are responsible for haziness in images of underwater scenes. To reduce this effect, we propose an approach for single-image dehazing by multilevel weighted enhancement of the image. The underlying principle is that enhancement at different levels of detail can undo the degradation caused by underwater haze. The depth information is captured implicitly while going through different levels of details due to the depth-variant nature of haze. Hence, we judiciously assign weights to different levels of image details and reveal that their linear combination along with the coarsest information can successfully restore the image. Results demonstrate the efficacy of our approach as compared to state-of-the-art underwater dehazing methods.

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models.

The advantages of using conceptual models for database design are well known. In particular, they facilitate the communication between users and designers since they do not require the knowledge of specific features of the underlying implementation platform. Further, schemas developed using conceptual models can be mapped to different logical models, such as the relational, objectrelational, or object-oriented models, thus simplifying technological changes. Finally, the logical model is translated into a physical one according to the underlying implementation platform. Nevertheless, the domain of conceptual modeling for data warehouse applications is still at a research stage. The current state of affairs is that logical models are used for designing data warehouses, i.e., using star and snowflake schemas in the relational model. These schemas provide a multidimensional view of data where measures (e.g., quantity of products sold) are analyzed from different perspectives or dimensions (e.g., by product) and at different levels of detail with the help of hierarchies. On-line analytical processing (OLAP) systems allow users to perform automatic aggregations of measures while traversing hierarchies: the roll-up operation transforms detailed measures into aggregated values (e.g., daily into monthly sales) while the drill-down operation does the contrary. Star and snowflake schemas have several disadvantages, such as the inclusion of implementation details and the inadequacy of representing different kinds of hierarchies existing in real-world applications. In order to facilitate users to express their analysis needs, it is necessary to represent data requirements for data warehouses at the conceptual level. A conceptual multidimensional model should provide a graphical support (Rizzi, 2007) and allow representing facts, measures, dimensions, and different kinds of hierarchies.

PurposeIn most cases, the conventional assessment of value streams is based on key performance indicators (KPIs) like the share of added value, the degree of flow or a comprehensive lead time analysis. To evaluate cross-enterprise value streams of manufacturing, business or service processes in detail, a holistic methodology is needed. The paper aims to discuss this issue.Design/methodology/approachIn this research paper, the assessment of value streams within complex cross-company networks is described. After a presentation of relevant KPIs in the fields of value stream management (VSM) and supply chain management (SCM), an approach for a cross-enterprise evaluation of value streams on different levels of detail is shown. In addition, the use of an absolute VSM evaluation, in contrast to a relative VSM assessment, is examined.FindingsBased on a uniform and well-balanced set of KPIs and other VSM and SCM parameters, a performance assessment on different levels of value stream detail is enabled. Further investigations reveal the advantages of a relative compared with an absolute VSM assessment.Research limitations/implicationsIn addition to a comprehensive overview of existing KPIs for a value stream assessment beyond company borders, a holistic and multi-level VSM approach is presented in this paper. In contrast to existing VSM approaches, the described method allows an evaluation and subsequent improvement of value streams within supply chain networks. Up to now, the presented approach for the assessment of cross-enterprise value streams has only been tested in specific industrial environments. In future, the proposed methodology shall also be validated for other process types like business, service or further manufacturing processes.Practical implicationsThe described cross-company performance measurement approach shows a high practical relevance for organizations operating in supply chain networks. Due to the integrated use of different VSM parameters, the evaluation of highly interconnected value streams across corporate boundaries is facilitated. By means of a case study, the proposed methodology is validated under real industry conditions and proves its practical applicability.Originality/valueOne of the novel features of this research is the extension of the traditional VSM method with respect to a relative evaluation of value streams based on a set of significant KPIs. In addition, the allocation of these KPIs to different value stream layers and categories leads to an innovative approach for a multi-level assessment according to the needs of the specific VSM application, e.g. a more standardized use of VSM in complex supply chain networks.

Multi-scale rock surface area quantification—a systematic method to evaluate the reactive surface area of rocks

Characterization of shaking intensity distribution and seismic assessment of RC buildings for the Kashmir (Pakistan) earthquake of October 2005

Geographic Information Systems manage data with respect to spatial location and that data is presented graphically as a map or sketch. Typically GIS tasks require graphical presentations at different levels of detail, ranging from overview scenes to detailed views. This is a general problem of visualization of information. Map generalization would solve this problem and efforts to achieve automated cartographic generalization were successful for specific aspects, but no complete solution is known. In this paper we propose to store renderings of objects at different levels of detail in a database, which can then be used to compose a map. This is a trade-off between storage and computation. 1. WHY A MULTI-SCALE DATA STRUCTURE FOR CARTOGRAPHIC OBJECTS Graphical sketches have become increasingly important as ad-hoc means of visual output from Geographic Information Systems (GIS). The requirements for these graphics are low in cartographic terms, namely fast visualization and presentation of the pertinent data only. In contrast, traditional maps have always been very sophisticated multi-purpose instruments. Typically GIS tasks require graphical representations at different levels of detail, ranging from overview scene to detailed views [12]. They also call for a single database as basis for the tasks to perform [3]. Automated cartographic generalization would fulfill these requirements. Unfortunately no complete solution is known, although efforts were successful for specific aspects [6]. Generalization in cartography is a complex set of operations on graphical representations of objects, not on the object itself. The result is a commonly accepted or standardized representation of the object in a certain scale. In this paper we propose to store renderings of objects at different levels of detail in a database. The proposed structure is a directed acyclic graph (dag). The renderings can then be used to compose a map. We restrict ourselves to the task of zooming and to the topographic scales (1:5000 - 1:100 000). The method is a trade-off between storage and computation.

Many biological molecules exist in multiple variants, such as proteins with different posttranslational modifications, DNAs with different sequences, and phospholipids with different chain lengths. Representing these variants as distinct species, as most biochemical simulators do, leads to the problem that the number of species, and chemical reactions that interconvert them, typically increase combinatorially with the number of ways that the molecules can vary. This can be alleviated by "rule-based modeling methods," in which software generates the chemical reaction network from relatively simple "rules." This chapter presents a new approach to rule-based modeling. It is based on wildcards that match to species names, much as wildcards can match to file names in computer operating systems. It is much simpler to use than the formal rule-based modeling approaches developed previously but can lead to unintended consequences if not used carefully. This chapter demonstrates rule-based modeling with wildcards through examples for signaling systems, protein complexation, polymerization, nucleic acid sequence copying and mutation, the "SMILES" chemical notation, and others. The method is implemented in Smoldyn, a spatial and stochastic biochemical simulator, for both generate-first and on-the-fly expansion, meaning whether the reaction network is generated before or during the simulation.

A hybrid mutiresolution representation for fast tree modeling and rendering

Reasoning about spatio-temporal phenomena requires the adoption of common granularities that facilitate and enhance the comprehension of a particular phenomenon. In our day-to-day activities, spatial granules like state, province or country, and temporal granules like day, month or year, are used to index facts and to allow reasoning adopting the level of detail considered appropriate in a particular analytical context. In an era where huge amounts of spatio-temporal data are collected every day, it is crucial to model the spatio-temporal phenomena expressed in such data sets having in mind that different levels of detail can be useful in the analysis of such phenomena and that different levels of detail are related, for instance, through a spatial or temporal hierarchy. As the size and level of details of the data sets increase, the need to use multiple levels of detail that enhance our capability to achieve useful insights from data also increases. This paper presents a granularity theory devised to model spatio-temporal phenomena at different levels of detail. This granularity theory is more general than the existing granularities proposals. In fact, we relate those proposals with the presented granularity theory.

This paper outlines a framework to support the demand-driven analysis of spatio-temporal data. It will support decision making involving complex, multidimensional data and addresses the following challenges: (1) the demand-driven acquisition of context data, (2) the combination of context and user data, (3) the consideration of different aspects and levels of detail on the analysis and (4) the storage and integration of analysis results for further use. The framework will provide web-services based on open standards to populate and explore multidimensional spatio-temporal structures interactively. Online Analitical Pro-cesing (OLAP) concepts will serve as model for storing and querying the data. Roaming-services will be used to update contents coming from different Spatial Data Infrastructures. Semantical descriptions will allow switching analysis operations at different levels of detail. Research questions and challenges related to the underlying model and implementation aspects will be discussed.

BackgroundMathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions.ResultsHere, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map.ConclusionsWith the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.

The Mondex Electronic Purse is an outstanding example of industrial scale formal refinement, and was the first verification to achieve ITSEC level E6 certification. A formal abstract model and a formal concrete model were developed, and a formal refinement was hand-proved between them. Nevertheless, certain requirements issues were set beyond the scope of the formal development, or handled in an unnatural manner. The retrenchment tower pattern is used to address one such issue in detail: the use of a hash function rather than a total injective function when clearing the highly constrained purse logs. A retrenchment is constructed from the lowest level model to a model using a hash, and is then lifted to create two refinement developments, working at different levels of detail, and connected via retrenchments. The tower development is appropriately validated, vindicating the design used.

The Mondex Electronic Purse is an outstanding example of industrial scale formal refinement, and was the first verification to achieve ITSEC level E6 certification. A formal abstract model and a formal concrete model were developed, and a formal refinement was hand-proved between them. Nevertheless, certain requirements issues were set beyond the scope of the formal development, or handled in an unnatural manner. The retrenchment Tower Pattern is used to address one such issue in detail: the finiteness of the purse log (which records unsuccessful transactions). A retrenchment is constructed from the lowest level model of the purse system to a model in which logs are finite, and is then lifted to create two refinement developments of the purse, working at different levels of detail, and connected via retrenchments, forming the tower. The tower development is appropriately validated, vindicating the design used.