A framework for mapping, visualisation and automatic model creation of signal‐transduction networks

Tiger, Carl Fredrik; Krause, Falko; Cedersund, Gunnar; Palmer, Robert Sterling; Klipp, Edda; Hohmann, Stefan; Kitano, Hiroaki; Krantz, Marcus

doi:10.1038/msb.2012.12

Cited by 57 publications

(94 citation statements)

References 212 publications

(111 reference statements)

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“…Because BNGL is based on a graphical formalism for representing molecules and interactions, models and the formal elements of models can be readily visualized with precision. Indeed, automatic visualization capabilities are available for both individual rules and entire rule-based models [76–78, 108, 109]. It is even possible to use a simple drawing tool to specify a rule-based model [75, 105].…”

Section: Discussionmentioning

confidence: 99%

“…There are also BNGL extensions and other languages that enable modeling capabilities beyond what is available within the BNGL framework [67, 68, 102–107]. We note that rules and rule-based models are readily visualized [76–78, 93, 96, 105, 108, 109] and software tools exist that enable a visual approach to model specification, such as RuleBuilder [75] and Simmune [63, 104, 105]. (RuleBuilder is not currently supported but the Java code is available.…”

Section: Methodsmentioning

confidence: 99%

“…The understandability of BNGL stems from its underlying graphical formalism [36, 74, 110], which makes individual rules and collections of rules (models) amenable to visualization [76–78, 93, 96, 105, 108, 109], as well as annotation [96]. Computer-aided analysis of BNGL-encoded models is enabled by general-purpose BNGL-compatible software tools that can process model specifications to obtain simulation results.…”

Section: Methodsmentioning

confidence: 99%

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Modeling for (physical) biologists: an introduction to the rule-based approach

et al. 2015

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Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Modeling for (physical) biologists: an introduction to the rule-based approach

et al. 2015

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“…11 During the reconstruction process, we collected two kinds of data from literature: mechanistic and physiological/functional data. The mechanistic data were further divided into elemental reactions and contingencies.…”

Section: Methodsmentioning

confidence: 99%

Network reconstruction and validation of the Snf1/AMPK pathway in baker’s yeast based on a comprehensive literature review

et al. 2015

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Background/Objectives:The SNF1/AMPK protein kinase has a central role in energy homeostasis in eukaryotic cells. It is activated by energy depletion and stimulates processes leading to the production of ATP while it downregulates ATP-consuming processes. The yeast SNF1 complex is best known for its role in glucose derepression.Methods:We performed a network reconstruction of the Snf1 pathway based on a comprehensive literature review. The network was formalised in the rxncon language, and we used the rxncon toolbox for model validation and gap filling.Results:We present a machine-readable network definition that summarises the mechanistic knowledge of the Snf1 pathway. Furthermore, we used the known input/output relationships in the network to identify and fill gaps in the information transfer through the pathway, to produce a functional network model. Finally, we convert the functional network model into a rule-based model as a proof-of-principle.Conclusions:The workflow presented here enables large scale reconstruction, validation and gap filling of signal transduction networks. It is analogous to but distinct from that established for metabolic networks. We demonstrate the workflow capabilities, and the direct link between the reconstruction and dynamic modelling, with the Snf1 network. This network is a distillation of the knowledge from all previous publications on the Snf1/AMPK pathway. The network is a knowledge resource for modellers and experimentalists alike, and a template for similar efforts in higher eukaryotes. Finally, we envisage the workflow as an instrumental tool for reconstruction of large signalling networks across Eukaryota.

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“…Second, it could reflect an experimental bias: At least in yeast, we know much more about the modifying reactions than about the reactions that reverse the modification (e.g. phosphorylation vs dephosphorylation, 27 ). If so, the formalism we present here helps us make this information bias explicit, and will allow us to integrate the regulation on these reactions as the knowledge becomes available.…”

Section: Discussionmentioning

confidence: 99%

A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models

Romers¹,

Thieme²,

Muenzner³

et al. 2017

Preprint

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The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. In contrast, current established methods do not support genome-scale mechanistic models of signal transduction networks. These networks encode information through internal states, and dealing with these states leads to scalability issues both in model formulation and execution. While rule based modelling can be used for efficient model definition (through rules) and simulation (through agent based execution), these quantitative models require parametrisation. This introduces yet another layer of uncertainty, due to the sparsity of reliably measured parameters. Hence, parameter-free simulation and validation will be important to support large-scale reconstruction and analysis of signal transduction network models. Here, we present a scalable method for parameterfree simulation of mechanistic signal transduction network models. It is based on rxncon, the reaction-contingency language, which describes the signalling network in terms of elemental reactions and states. We develop two generic update rules for states and reactions, based on detail analysis of two minimal reaction motifs, that can be used to map an arbitrary rxncon network on fully defined bipartite Boolean model. Locally defined update rules are assembled into a functional model without system level optimisation, making the methods suitable for network validation. Furthermore, an underlying model defined solely in terms of molecular reactions and causalities can be used to explain and predict system level behaviour. Taken together, we present a method for parameter-free simulation of mechanistic signal transduction models. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.

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A framework for mapping, visualisation and automatic model creation of signal‐transduction networks

Abstract: An intuitive formalism for reconstructing cellular networks from empirical data is presented, and used to build a comprehensive yeast MAP kinase network. The accompanying rxncon software tool can convert networks to a range of standard graphical formats and mathematical models.

Cited by 57 publications

References 212 publications

Modeling for (physical) biologists: an introduction to the rule-based approach

Modeling for (physical) biologists: an introduction to the rule-based approach

Network reconstruction and validation of the Snf1/AMPK pathway in baker’s yeast based on a comprehensive literature review

A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models

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