Putting humpty-dumpty together: Mining causal mechanistic biochemical models from big data

In traditional engineering disciplines, the construction of a system is usually preceded by a formal or informal specification of the design of the system being developed. In biochemical applications, however, a detailed specification of the system´s structure and dynamics is usually unavailable. Thus, mechanistic details of biochemical systems must be mined from experimental observations. In this paper, we adopt a formal methods approach towards deriving causal mechanistic models from time-series observations of biochemical systems. The mined model captures causality among multiple biological events and also allows causal relationships between sets of events. We exploit results from trace theory and use the power of powerful constraint solvers to develop a new framework for causality identification and reasoning that captures dynamic relationships among species in biochemical reaction networks.