In silico crops and multi-omic approaches to meet agricultural challenges

For decades, plant geneticists have attempted to link crop genes to phenotypes of interest. Advanced technologies in molecular biology, biochemistry and high-performance computing have provided unprecedented depth of knowledge about biological systems, and have accelerated molecular breeding and bioengineering of crop species. Single-omic level technologies that explore genomes, transcriptomes, proteomes and metabolomes have led to gene discovery and have revealed important signalling and regulatory mechanisms that influence crop response to the environment. Data from individual biological scales have been used in mathematical models to predict how crops will respond to untested environmental conditions. However, many of these models are empirical and lack predictive capability that can extrapolate beyond the conditions used to optimize the model. There is a need to move beyond modelling at single-scales to achieve integrative, multiscale modelling that takes full advantage of our understanding of molecular mechanisms and the wealth of genome-wide data that has been generated over the last three decades. In this review, we survey the most recent literature exploring the collection and analysis of multi-omic data, and provide examples of attempts to integrate these data in various ways. It is apparent that integrative and multiscale modelling improve crop model prediction accuracy, and it is anticipated that the movement towards in silico crops will aid in the development of crop ideotypes that can thrive under challenging environmental conditions.