Automated property synthesis of ODEs based bio-pathways models

Abstract

Identifying non-trivial requirements for large complex dynamical systems is a challenging but fruitful task. Once identified such requirements can be used to validate updated versions of the system and verify functionally similar systems. Here we present a technique for discovering behavioural properties of bio-pathway models whose dynamics is modelled as a system of ordinary differential equations (ODEs). These models are usually accompanied at best by high level functional requirements while undergoing many revisions as new experimental data becomes available. In this setting we first specify a set of property templates using bounded linear-time temporal logic (BLTL). A template will have the skeletal structure of a BLTL formula but the time bounds associated with the temporal operator as well as the value bounds associated with the system variables encoded as atomic propositions will be unknown parameters. We classify a given model’s behavior as corresponding to one of these templates using a convolutional neural network. We then synthesize a concrete property from this template by estimating its parameters via a standard search procedure combined with statistical model checking (SMC). We have synthesized and validated properties of a number of pathway models of varying complexity using our method.