A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux, metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.

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Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.

Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.

The central motivation for mechanistic crop growth simulation has remained the same for decades: to reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species, and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms, and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.