Stepwise construction of a metabolic network in Event-B: The heat shock response

Scalable Reaction Network Modeling with Automatic Validation of Consistency in Event-B

“…We show that simple mathematical functions can be used to hide some of the details of the refinement, leading to a reduction in the number of events of the model. We show that a model of the heat shock response can be described through 17 events, instead of the 57 events in an earlier model 14 . More drastically, we show that a model of the ErbB signaling pathway consisting of 1320 reactions can be written through only 242 events.…”

“…In data refinement 2 , some variables in the abstract machine are replaced by other variables in the refined machine; in this case, a gluing invariant is added in the refined machine to define the relation between the abstract variables and the newly introduced, concrete ones. Refinement in Event-B has been used to model numerous protocols and systems, including smart cash card systems 31 , vehicle platoons 32 , topology discovery in graphs 33 , self-recovery in sensor-actor networks 34 , spacecraft systems 35 , coordination in peer-to-peer networks 36 , smart grid recoverability 37 , proactive routing in wireless networks 38 , reaction networks 14 , etc.…”

“…, X n } is the set of reactants and m 1 , ..., m n , m ′ 1 , ..., m ′ n ∈ N are non-negative integers. Reaction networks can be modeled straightforwardly in Event-B 14 : every reactant is modeled by a variable and every reaction is modeled by an event. Invariants ensure the consistency of the model as well as other biological properties of interest, for instance the mass conservation rule that requires that the number of certain reactants remains constant in the system.…”

“…Invariants ensure the consistency of the model as well as other biological properties of interest, for instance the mass conservation rule that requires that the number of certain reactants remains constant in the system. The general form of an Event-B model corresponding to reaction ( 1) is presented in Table 1; more reactions in the network model lead to more events in the Event-B model 14 . X 1 , X 2 , ..., X n are the variables of the model, whose initial values are set in the initialization event.…”

“…In this paper we extend the Event-B-based modeling technique 14 to give the first scalable demonstration of Event-B-based biomodeling. We show that simple mathematical functions can be used to hide some of the details of the refinement, leading to a reduction in the number of events of the model.…”