Questions, data and models underpinning metabolic engineering

Abstract

Model-driven design has shown great promise for shortening the development time of cell factories by complementing and guiding metabolic engineering efforts. Still, implementation of the prized cycle of model predictions followed by experimental validation remains elusive. The development of modelling frameworks that can lead to actionable knowledge and subsequent integration of experimental efforts requires a conscious effort. In this review, we will explore some of the pitfalls that might derail this process and the critical role of achieving alignment between the selected modelling framework, the available data, and the ultimate purpose of the research. Using recent examples of studies successfully using modelling or other methods of data integration, we will then review the various types of data that can support different modelling formalisms, and in which scenarios these different models are at their most useful.