1,088 research outputs found
On Modeling Signal Transduction Networks
Signal transduction networks are very complex processes employed by the
living cell to suitably react to environmental stimuli. Qualitative and quantitative computational
models play an increasingly important role in the representation of these
networks and in the search of new insights about these phenomena. In this work we analyze
some graph-based models used to discover qualitative properties of such networks.
In turn, we show that MP systems can naturally extend these graph-based models by
adding some qualitative elements. The case study of integrins activation during the lymphocyte
recruitment, a crucial phenomenon in inflammatory processes, is described, and
a first MP graph for this network is designed. Finally, we discuss some open problems
related to the qualitative modeling of signaling networks
An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems
The authors would like to thank the support on this research by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD
Hybrid modeling and optimization of biological processes
Proß S. Hybrid modeling and optimization of biological processes. Bielefeld: Bielefeld University; 2013
SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools
Background:
Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.
Results:
We present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.
Conclusions:
SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks
Third Workshop on Modelling of Objects, Components, and Agents
This booklet contains the proceedings of the Third International Workshop on Modelling of Objects, Components, and Agents (MOCA'04), October 11-13, 2004. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark and the "Theoretical Foundations of Computer Science" group at the University of Hamburg. The home page of the workshop is: http://www.daimi.au.dk/CPnets/workshop0
Complex event types for agent-based simulation
This thesis presents a novel formal modelling language, complex event types (CETs), to describe behaviours
in agent-based simulations. CETs are able to describe behaviours at any computationally
represented level of abstraction. Behaviours can be specified both in terms of the state transition rules of
the agent-based model that generate them and in terms of the state transition structures themselves.
Based on CETs, novel computational statistical methods are introduced which allow statistical dependencies
between behaviours at different levels to be established. Different dependencies formalise
different probabilistic causal relations and Complex Systems constructs such as ‘emergence’ and ‘autopoiesis’.
Explicit links are also made between the different types of CET inter-dependency and the
theoretical assumptions they represent.
With the novel computational statistical methods, three categories of model can be validated and
discovered: (i) inter-level models, which define probabilistic dependencies between behaviours at different
levels; (ii) multi-level models, which define the set of simulations for which an inter-level model
holds; (iii) inferred predictive models, which define latent relationships between behaviours at different
levels.
The CET modelling language and computational statistical methods are then applied to a novel
agent-based model of Colonic Cancer to demonstrate their applicability to Complex Systems sciences
such as Systems Biology. This proof of principle model provides a framework for further development
of a detailed integrative model of the system, which can progressively incorporate biological data from
different levels and scales as these become available
Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery
Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.
Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet.
Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer
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Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery
Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer
Modeling and Control of Discrete Event Systems Using Finite State Machines with Variables and Their Applications in Power Grids
Control theories for discrete event systems modeled as finite state machines have been well developed to address various fundamental control issues. However, finite state machine model has long suffered from the problem of state explosion that renders it unsuitable for some practical applications. In an attempt to mitigate the state explosion problem, we propose an efficient representation that appends finite sets of variables to finite state machines in modeling discrete event systems. We also present the control synthesis techniques for such finite state machines with variables (FSMwV). We first present our notion and means of control under this representation. We next present our algorithms for both offline and online synthesis of safety control policies. We then apply these results to the control of electric power grids
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