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A graphical and computational modelling platform for biological pathways

A graphical and computational modelling platform for biological pathways
A graphical and computational modelling platform for biological pathways
A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net–based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.
Pathway, notation system, model, dynamic modelling, Petri N et, simulation, SBGN, mEPN
1754-2189
705–722
Livigni, Alexandra
dc2e9e3c-131c-4d0d-9e40-53c850e17e4e
O'Hara, Laura
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Polak, Marta E.
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Angus, Tim
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Wright, Derek W.
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Smith, Lee B.
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Freeman, Tom C.
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Livigni, Alexandra
dc2e9e3c-131c-4d0d-9e40-53c850e17e4e
O'Hara, Laura
3150fd0a-90e9-45b8-8674-1fdeaa69df0f
Polak, Marta E.
e0ac5e1a-7074-4776-ba23-490bd4da612d
Angus, Tim
d8199315-439b-470b-9d86-ed20b22b1ad8
Wright, Derek W.
0a7c92e4-4e9d-49ad-a2f6-b0504e023725
Smith, Lee B.
7a7ce054-7aef-49d6-9835-dd653906bbe1
Freeman, Tom C.
47aef616-55cc-4ea4-8eeb-1ca4629e987b

Livigni, Alexandra, O'Hara, Laura, Polak, Marta E., Angus, Tim, Wright, Derek W., Smith, Lee B. and Freeman, Tom C. (2018) A graphical and computational modelling platform for biological pathways. Nature Protocols, 13 (4), 705–722. (doi:10.1038/nprot.2017.144).

Record type: Article

Abstract

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net–based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.

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Livigni_2017_final_to PURE - Accepted Manuscript
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Accepted/In Press date: 25 October 2017
e-pub ahead of print date: 15 March 2018
Published date: April 2018
Additional Information: Related publication: Polak, M., Ung, C. Y., Masapust, J., Freeman, T. C., & Ardern-Jones, M. (2017). Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation. Nature Scientific Reports, 7, [668]. DOI: 10.1038/s41598-017-00651-5
Keywords: Pathway, notation system, model, dynamic modelling, Petri N et, simulation, SBGN, mEPN

Identifiers

Local EPrints ID: 419102
URI: http://eprints.soton.ac.uk/id/eprint/419102
ISSN: 1754-2189
PURE UUID: e1e4e1bb-9159-4c18-90bc-b50126bc7444

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Date deposited: 29 Mar 2018 16:30
Last modified: 26 Nov 2021 05:26

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Contributors

Author: Alexandra Livigni
Author: Laura O'Hara
Author: Marta E. Polak
Author: Tim Angus
Author: Derek W. Wright
Author: Lee B. Smith
Author: Tom C. Freeman

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