The Signaling Petri Net-based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

doi:10.1371/journal.pcbi.1000005.eor

Cited by 20 publications

(33 citation statements)

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“…The BioLayout Express 3D software is fully compatible with the mEPN notation and can be used to for pathway visualization and to perform stochastic flow simulations using a modified version of SPN algorithm [4]. Running simulations provides insights into the dynamic behaviour of the system by enabling users to visualize the signal flow within the network.…”

Section: Anticipated Resultsmentioning

confidence: 99%

“…if there are two inputs into a transition and one has tokens and the other does not, tokens will not be lost from the input with tokens. This mode was as originally described by Ruths et al; [4].…”

Section: Draw the Interactions Between Entity Nodes The Nature Of Anmentioning

confidence: 99%

“…BioLayout Express 3D software allows the visualization and analysis of large network graphs in two and three-dimensional space and supports the computational modelling of networks using the signalling Petri net (SPN) algorithm [4]. BioLayout Express 3D runs on Windows, Mac OS X and Linux platforms.…”

Section: Installation Of Biolayout Express 3dmentioning

confidence: 99%

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Petri Net-Based Graphical and Computational Modelling of Biological Systems

Livigni

O’Hara

Polak

et al. 2016

Preprint

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In silico modelling of biological pathways is a major endeavour of systems biology. Here we present a methodology for construction of pathway models from the literature and other sources using a biologistfriendly graphical modelling system. The pathway notation scheme, called mEPN, is based on the principles of the process diagrams and Petri nets, and facilitates both the graphical representation of complex systems as well as dynamic simulation of their activity. The protocol is divided into four sections: 1) assembly of the pathway in the yEd software package using the mEPN scheme, 2) conversion of the pathway into a computable format, 3) pathway visualisation and in silico simulation using the BioLayout Express 3D software, 4) optimisation of model parameterisation. This method allows reconstruction of any metabolic, signalling and transcriptional pathway as a means of knowledge management, as well as supporting the systems level modelling of their dynamic activity. 2010 [2,3] and is presented here in its current form (Box 1). The scheme consists of entity nodes (which represent biochemical components), process nodes (which represent interactions between the components) and edges, which link entity and process nodes. Pathway maps can be drawn in the free graph editing software yED (yWorks, Tübingen, Germany; www.yworks.com). The bipartite (alternating entity and process node) nature of a pathway model drawn according to the rules of mEPN allows it to be parameterised for computational modelling as a Petri net (a mathematical approach for describing distributed systems, Box 2) by addition of tokens to chosen entry entity nodes in the pathway representing its initial state. When a mEPN model is imported into the open-source software BioLayout Express 3D , a simulation is performed that results in the flow of these tokens through the pathway and generation of quantitative outputs at each entity node that can be visualised as graphs of token flow over time. Altering the simulation parameters can change the flow of tokens, allowing the dynamics of pathways to be modelled under different conditions.

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Section: Anticipated Resultsmentioning

confidence: 99%

Section: Draw the Interactions Between Entity Nodes The Nature Of Anmentioning

confidence: 99%

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Petri Net-Based Graphical and Computational Modelling of Biological Systems

Livigni

O’Hara

Polak

et al. 2016

Preprint

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“…Coloured Petri Nets, where tokens are allowed to have different data types, are used by [103] to model signaling networks with only activations and reactions. The Signaling Petri Net [75], a synchronized Petri Net with an event generator, appeared in [67] to model the response of Langerhans cells to interferon regulatory factors. For a general overview of Petri Nets in biology, see [15,50].…”

Section: The Basics Of Petri Netsmentioning

confidence: 99%

Computing Signal Transduction in Signaling Networks modeled as Boolean Networks, Petri Nets, and Hypergraphs

Vieira¹,

Vera-Licona²

2018

Preprint

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Abstract. Background Different objects exist to describe how a signal transduces in a given intracellular signaling network, such as elementary signaling modes, T-invariants, extreme pathway analysis, elementary modes and simple paths. For modeling frameworks such as Boolean networks, Petri nets and hypergraphs, these signal transduction objects are broadly used in their respective frameworks but few studies have been done emphasizing how these signal transduction objects compare or relate to each other. Results We provide an overview of the different methodologies for capturing signal transduction in a given model of an intracellular signaling network. We show how minimal functional routes proposed for signaling networks modeled as Boolean networks can be captured by computing topological factories, a methodology found in the metabolic networks literature. We further show that in the case of an acyclic B-hypergraph, the definitions are equivalent. Furthermore, we show that computing elementary modes based on the incidence matrix of a B-hypergraph fails to capture minimal functional routes, whereas in directed graphs, it has been shown that these computations of elementary modes correspond to computations of simple paths. Conclusions The different objects introduced in the literature to capture signal transduction are deeply related to each other, although they are based on different biological assumptions. Furthermore, methodology in metabolic networks and signaling networks are deeply related to each other.

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“…Hypergraphs [27,28] are generalizations of directed graphs that allow multiple inputs and outputs, and their realization as a model for signaling pathways is emerging [9,11,29]. Other models such as Petri nets [30] and logic networks [31,32] move away from structural network analysis and towards discrete dynamic modeling. Many of these modeling frameworks have an underlying bipartite graph structure.…”

Section: Introductionmentioning

confidence: 99%

Connectivity Measures for Signaling Pathway Topologies

Franzese

Groce

Murali

et al. 2019

Preprint

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Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules "connected" in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway's downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals two case studies of pathway influence, and we describe the specific reactions that contribute to the large influence score. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.

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The Signaling Petri Net-based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Cited by 20 publications

References 0 publications

Petri Net-Based Graphical and Computational Modelling of Biological Systems

Petri Net-Based Graphical and Computational Modelling of Biological Systems

Computing Signal Transduction in Signaling Networks modeled as Boolean Networks, Petri Nets, and Hypergraphs

Connectivity Measures for Signaling Pathway Topologies

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