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

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Protocol publication

[…] As described in detail in the section (), the reconstruction was performed using the rxncon language and tool. 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. The elemental reactions define possible state transition events that produce or consume elemental states. Importantly, the elemental states define only a single property of a component, such as a specific modification or binding. Hence, they correspond to the full set of specific states for which that modification and binding is true, and, correspondingly, an elemental reaction corresponds to a set of reactions (reviewed in ref. ). These decontextualised reactions are equivalent to the protein–protein interactions in e.g., the BioGRID database. The contingency information defines how elemental reactions depend on elemental states, and hence defines the causality in the network. The distinction between reactions and contingencies is the same as in the SBGN entity relationship diagrams, and together the reactions and contingencies fully define the network and can be used for automatic model generation (; ref. ).The physiological/functional data were used for validation of the network reconstruction. We searched for inputs known to activate Snf1 and for the downstream Snf1-dependent responses to these inputs, which we collected as a set of input/output relationships. For validation, we generated and simulated the corresponding bipartite Boolean model (bBM) with the rxncon toolbox, and visualised the attractor states on the regulatory graph in Cytoscape., We analysed only the attractor states, which are the end results of the simulations, due to the very crude time concept in Boolean models. The attractor states correspond to a qualitative steady state, which can be used to determine if the signal is transduced through the network or not. We scored functionality for each input–output relationship by determining if that output responded appropriately when the input was changed between on and off (). When necessary, we adapted the network definition to resolve blocks and/or constitutive activities as detailed in the Results section. All such adaptations have been clearly labelled as hypotheses in the updated network definition (). Finally, we translated the gap-filled network into a rule-based model in the BioNetGen language. All methods are described in more detail in the section. […]

Pipeline specifications

Software tools rxncon, BioNetGen
Databases BioGRID
Application Protein interaction analysis
Organisms Saccharomyces cerevisiae
Chemicals Adenosine Triphosphate, Glucose