Realizing Crops in silico

How to link heterogeneous models, build a community, and acquire necessary data in the Wild West of Plant Biology research.

Crops in silico logo

The Crops in silico initiative, co-led by in silico Plants Editors Stephen Long and Amy Marshall-Colón, proposes that multi-scale models ‘have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts’ in plant science.

A review from members of SynthSys, the Centre for Synthetic and Systems Biology at the University of Edinburgh, provides practical steps towards model integration to achieve Crops in silico by applying Systems Biology approaches.

Crops in silico aims to link several, current approaches, such as functional–structural plant models that have organ-scale spatial resolution and process-based crop models with lower spatial resolution. The integration of multi-scale models “have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security.”

Linking these models is challenging because diverse models in plant biology are “are as natural in a digital organism as the many biological processes that contribute to a physical organism (or the many research perspectives to understand it).” The diversity comes in two forms. First, there are many different modelling approaches (eg. constraint-based, quantitative, and graphical). Second, and most importantly, this initiative will involve scientists across the boundaries of previously separate fields and isolated efforts. Consequently, candidate models are as lawless as the Wild West: there are no established scripting language, programming conventions, modelling standards, or model repositories.

Two extreme approaches are currently being pursued to link diverse models: (1) rewrite all the models in a common modelling language and (2) devise an integration system that links the models in their diverse, native forms, as loosely coupled ‘black boxes.’

The formal organization of a cross-disciplinary community would reduce the lawlessness and speed up the process of model integration. Establishing community standards, such as adopting SBML, a standard for constraint-based and quantitative models, would enable the use of existing online repositories and software tools that use the standard format as input and/or output. The community would benefit from shared models, tools and data resources, guided by employing Open Science practices and/or following ‘FAIR Guiding Principles for scientific data management and stewardship’ – an initiative to make data Findable, Accessible, Interoperable, and Re-usable.

Further reading

Marshall-Colon, A., Long, S. P., Allen, D. K., Allen, G., Beard, D. A., Benes, B., … Zhu, X.-G. (2017). Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform. Frontiers in Plant Science, 8. https://doi.org/10.3389/fpls.2017.00786

Millar, A. J., Urquiza, J. U., Freeman, P. L., Hume, A., Plotkin, G. D., Sorokina, O., … Zielinski, T. (2019). Practical steps to digital organism models, from laboratory model species to ‘Crops in silico. Journal of Experimental Botany. https://doi.org/10.1093/jxb/ery435

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., … Bourne, P. E. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18