Modeling formalisms in systems biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.Research supported by grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the development of improved microbial cell factories" (MIT-Pt/BS-BB/0082/2008)

Making Legacy LMS adaptable using Policy and Policy templates

Koesling, A., Herder, E., De Coi, J., & Abel, F. (2008). Making Legacy LMS adaptable using Policy and Policy templates. In J. Baumeister & M. Atzmüller, Proceedings of the 16th Workshop on Adaptivity and User Modeling in Interactive System, ABIS 2008 (pp. 35-40). October, 6-8, 2008, Würzburg, Germany: University of Würzburg. Website with link to proceedings: http://lwa08.informatik.uni-wuerzburg.de/Wiki.jsp?page=FGABIS08In this paper, we discuss how users and designers of existing learning management systems (LMSs) can make use of policies to enhance adaptivity and adaptability. Many widespread LMSs currently only use limited and proprietary rule systems defining the system behaviour. Personalization of those systems is done based on those rule systems allowing only for fairly restricted adaptation rules. Policies allow for more sophisticated and flexible adaptation rules, provided by multiple stakeholders and they can be integrated into legacy systems. We present the benefits and feasibility of our ongoing approach of extending an existing LMS with policies. We will use the LMS ILIAS as a hands-on example to allow users to make use of system personalization.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

SeaFlows – A Compliance Checking Framework for Supporting the Process Lifecycle

Compliance-awareness is undoubtedly of utmost importance for companies nowadays. Even though an automated approach to compliance checking and enforcement has been advocated in recent literature as a means to tame the high costs for compliance-awareness, the potential of automated mechanisms for supporting business process compliance is not yet depleted. Business process compliance deals with the question whether business processes are designed and executed in harmony with imposed regulations. In this thesis, we propose a compliance checking framework for automating business process compliance verification within process management systems (PrMSs). Such process-aware information systems constitute an ideal environment for the systematic integration of automated business process compliance checking since they bring together different perspectives on a business process and provide access to process data. The objective of this thesis is to devise a framework that enhances PrMSs with compliance checking functionality. As PrMSs enable both the design and the execution of business processes, the designated compliance checking framework must accommodate mechanisms to support these different phases of the process lifecycle. A compliance checking framework essentially consists of two major building blocks: a compliance rule language to capture compliance requirements in a checkable manner and compliance checking mechanisms for verification of process models and process instances. Key to the practical application of a compliance checking framework will be its ability to provide comprehensive and meaningful compliance diagnoses. Based on the requirements analysis and meta-analyses, we developed the SeaFlows compliance checking framework proposed in this thesis. We introduce the compliance rule graph (CRG) language for modeling declarative compliance rules. The language provides modeling primitives with a notation based on nodes and edges. A compliance rule is modeled by defining a pattern of activity executions activating a compliance rule and consequences that have to apply once a rule becomes activated. In order to enable compliance verification of process models and process instances, the CRG language is operationalized. Key to this approach is the exploitation of the graph structure of CRGs for representing compliance states of the respective CRGs in a transparent and interpretable manner. For that purpose, we introduce execution states to mark CRG nodes in order to indicate which parts of the CRG patterns can be observed in a process execution. By providing rules to alter the markings when a new event is processed, we enable to update the compliance state for each observed event. The beauty of our approach is that both design and runtime can be supported using the same mechanisms. Thus, no transformation of compliance rules in different representations for process model verification or for compliance monitoring becomes necessary. At design time, the proposed approach can be applied to explore a process model and to detect which compliance states with respect to imposed CRGs a process model is able to yield. At runtime, the effective compliance state of process instances can be monitored taking also the future predefined in the underlying process model into account. As compliance states are encoded based on the CRG structure, fine-grained and intelligible compliance diagnoses can be derived in each detected compliance state. Specifically, it becomes possible to provide feedback not only on the general enforcement of a compliance rule but also at the level of particular activations of the rule contained in a process. In case of compliance violations, this can explain and pinpoint the source of violations in a process. In addition, measures to satisfy a compliance rule can be easily derived that can be seized for providing proactive support to comply. Altogether, the SeaFlows compliance checking framework proposed in this thesis can be embedded into an overall integrated compliance management framework

The SLH framework for modeling quantum input-output networks

Many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, eg. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields by an operator triple $(S,L,H)$. Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.Comment: 60 pages, 14 figures. We are still interested in receiving correction

Linear fuzzy gene network models obtained from microarray data by exhaustive search

BACKGROUND: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. RESULTS: We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes. CONCLUSION: Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems

AsmetaF: A Flattener for the ASMETA Framework

Abstract State Machines (ASMs) have shown to be a suitable high-level specification method for complex, even industrial, systems; the ASMETA framework, supporting several validation and verification activities on ASM models, is an example of a formal integrated development environment. Although ASMs allow modeling complex systems in a rather concise way -and this is advantageous for specification purposes-, such concise notation is in general a problem for verification activities as model checking and theorem proving that rely on tools accepting simpler notations. In this paper, we propose a flattener tool integrated in the ASMETA framework that transforms a general ASM model in a flattened model constituted only of update, parallel, and conditional rules; such model is easier to map to notations of verification tools. Experiments show the effect of applying the tool to some representative case studies of the ASMETA repository.Comment: In Proceedings F-IDE 2018, arXiv:1811.09014. The first two authors are supported by ERATO HASUO Metamathematics for Systems Design Project (No. JPMJER1603), JST. Funding Reference number: 10.13039/501100009024 ERAT

A Database Approach for Modeling and Querying Video Data

Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].This is an extended version of the article in: 15th International Conference on Data Engineering, Sydney, Australia, 1999