MOTIVATION : Boolean network models are suitable to simulate generegulatory networks (GRNs) in the absence of detailed kinetic information.However, reducing the biological reality implies makingassumptions on how genes interact (interaction rules) and how theirstate is updated during the simulation (update scheme). The exactchoice of the assumptions largely determines the outcome of thesimulations. In most cases, however, the biologically correct assumptionsare unknown. An ideal simulation thus implies testing differentrules and schemes to determine those that best capture an observedbiological phenomenon. This is not trivial, since most current methodsto simulate Boolean network models of GRNs and to compute theirattractors impose specific assumptions that cannot be easily altered,as they are built into the system.Results : To allow for a more flexible simulation framework, wedeveloped ASP-G. We show the correctness of ASP-G in simulatingBoolean network models and obtaining attractors under differentassumptions by successfully recapitulating the detection of attractorsof previously published studies. We also provide an example of howperforming simulation of network models under different settings helpdetermine the assumptions under which a certain conclusion holds.The main added value of ASP-G is in its modularity and declarativity,making it more flexible and less error-prone than traditional approaches.The declarative nature of ASP-G comes at the expense ofbeing slower than the more dedicated systems but still achieves agood efficiency w.r.t. computational time.
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