Hybrid computational modeling methods for systems biology

Abstract

Systems biology models are typically considered across a spectrum from mechanistic to abstracted description; however, the lines between these forms of modeling are increasingly blurred. Ever-increasing computational power is providing novel opportunities for bridging time and length scales. Furthermore, despite biological mechanisms or network topology often ill-defined, the acquisition of high-throughput data leaves modelers with the desire to leverage available measurements. This review surveys modeling tools in which two or more mathematical forms are blended to describe time-dependent processes in a multivariate system. While most commonly manifested as continuous/discrete description, other forms such as mechanistic/inference or deterministic/stochastic hybrid models can be generated. Recent innovations in hybrid modeling methodologies and new applications illustrate advantages for combining model formats to gaining biological systems level insight.