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A HigherOrder Graph Calculus for Autonomic Computing
 GRAPH THEORY, COMPUTATIONAL INTELLIGENCE AND THOUGHT. A CONFERENCE CELEBRATING MARTIN CHARLES GOLUMBIC'S 60TH BIRTHDAY (2008)
, 2008
"... In this paper, we present a highlevel formalism based on port graph rewriting, strategic rewriting, and rewriting calculus. We argue that this formalism is suitable for modeling autonomic systems and briefly illustrate its expressivity for modeling properties of such systems. ..."
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In this paper, we present a highlevel formalism based on port graph rewriting, strategic rewriting, and rewriting calculus. We argue that this formalism is suitable for modeling autonomic systems and briefly illustrate its expressivity for modeling properties of such systems.
Rulebased modelling and simulation of biochemical systems with molecular finite automata
 IET Syst. Biol
, 2010
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Bigraphical Languages and their Simulation
"... We study bigraphs as a foundation for practical formal languages and the problem of simulating such bigraphical languages. The theory of bigraphs is a foundational, graphical model of concurrent systems focusing on mobility and connectivity. It is a metamodel in the sense that it is parametrized ov ..."
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We study bigraphs as a foundation for practical formal languages and the problem of simulating such bigraphical languages. The theory of bigraphs is a foundational, graphical model of concurrent systems focusing on mobility and connectivity. It is a metamodel in the sense that it is parametrized over a signature and a set of reaction rules which determine the syntax and dynamic semantics, respectively. This allows for rather direct models and, together with a natural yet formal graphical notation and an elegant theory of behavioral equivalence, this makes bigraphs an enticing foundation for practical formal languages. However, the theory of bigraphs is still young. While direct models of many process calculi have been constructed, it is unclear how suitable bigraphs are for more practical formal languages. Also, the generality of bigraphs comes at a price of complexity in the theory and simulation of bigraphical models is nontrivial. A key problem is that of matching: deciding if and how a reaction rule applies to a bigraph. In this dissertation, we study bigraphs and their simulation for two types of practical formal languages: programming languages and languages for cell biology. First, we study programming languages and binding bigraphs, a variant of bigraphs with a facility for modeling the binders found in most programming languages. Building on an existing
Author manuscript, published in "The Fourth TaiwaneseFrench Conference on Information Technology TFIT'08, Taipei: Taiwan (2008)" Strategic Port Graph Rewriting for Autonomic Computing
, 2008
"... In this paper, we present a highlevel formalism based on port graph rewriting, strategic rewriting, and rewriting calculus. We show that this formalism is suitable for modeling autonomic systems and we illustrate its expressivity for modeling properties of such systems using an example of a mail de ..."
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In this paper, we present a highlevel formalism based on port graph rewriting, strategic rewriting, and rewriting calculus. We show that this formalism is suitable for modeling autonomic systems and we illustrate its expressivity for modeling properties of such systems using an example of a mail delivery system. 1.
found at the ENTCS Macro Home Page. A Port Graph Calculus for Autonomic Computing and Invariant Verification
, 2009
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ProjectTeam PROTHEO Constraints, Mechanized Deduction and Proofs of Software Properties
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Author manuscript, published in "The Third International Conference on Algebraic Biology AB'08, Hagenberg: Austria (2008)" A Biochemical Calculus Based on Strategic Graph
"... When modeling interactions between molecules or proteins, the behaviour of a protein is given by its functional domains that determine which other protein it can bind to or interact with and these domains are usually abstracted as sites that can be bound or free, visible or hidden. Hence a protein i ..."
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When modeling interactions between molecules or proteins, the behaviour of a protein is given by its functional domains that determine which other protein it can bind to or interact with and these domains are usually abstracted as sites that can be bound or free, visible or hidden. Hence a protein is characterized by the collection of interaction sites on its surface and proteins can bind to each other forming molecular complexes. Based on such structures, we considered port graphs [1] which are graphs with ports and with multiple edges and loops attached to ports of nodes. Molecular complexes are port graphs where each port is connected to at most one other port. Such restricted port graphs are called molecular graphs and their ports are called sites. We illustrate below a molecular graph G representing the initial state of the system modeling a fragment of the EGFR signaling cascade [12, 14]. The protagonists of this model are three types of proteins: the signal EGF, the receptor EGFR, and the adapter SHC. The molecular graph G ′ represents a state of the system where two signal proteins are already bound forming a dimer binding in turn a receptor. A node is graphically represent as a box with an unique identifier and a name placed outside the box. A site is represented as a filled, empty, or slashed circle on the surface of the box if its state is respectively bound, free, or hidden.
found at the ENTCS Macro Home Page. Containment in RuleBased Models
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Dynamic Graphbased Relational Learning of Temporal Patterns in Biological Networks Changing over Time
"... We propose a dynamic graphbased relational learning approach using graphrewriting rules to analyze how biological networks change over time. The analysis of dynamic biological networks is necessary to understand life at the systemlevel, because biological networks continuously change their structu ..."
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We propose a dynamic graphbased relational learning approach using graphrewriting rules to analyze how biological networks change over time. The analysis of dynamic biological networks is necessary to understand life at the systemlevel, because biological networks continuously change their structures and properties while an organism performs various biological activities to promote reproduction and sustain our lives. Most current graphbased data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. First, we generate a dynamic graph, which is a sequence of graphs representing biological networks changing over time. Then, our approach discovers graphrewriting rules, which show how to replace subgraphs, between two sequential graphs. These rewriting rules describe the structural difference between two graphs, and describe how the graphs in the dynamic graph change over time. Temporal relational patterns discovered in dynamic graphs representing synthetic networks and metabolic pathways show that our approach enables the discovery of dynamic patterns in biological networks.
ECEASST A Bidirectional Collaboration Framework for BioModel Development
"... Abstract: Highlevel graph data structures have gained favour in representing biological knowledge in a computationally executable form, but the information contained therein must remain accessible to all users no matter their background. Bidirectional graph transformations may be used to synchro ..."
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Abstract: Highlevel graph data structures have gained favour in representing biological knowledge in a computationally executable form, but the information contained therein must remain accessible to all users no matter their background. Bidirectional graph transformations may be used to synchronise and maintain the consistency of these graph data structures as they evolve through the process of creating and refining a biomodel knowledge base. We outline a bidirectional collaboration framework by which users with vastly differing backgrounds may contribute to the development and evolution of such a knowledge base, and examine a simple example to illustrate its merits. We also identify avenues for further research necessary to refine the framework. No prior biological knowledge is assumed.