Combining gene network, metabolic and leaf-level models shows means to future-proof soybean photosynthesis under rising CO2

Abstract

Global population increase coupled with rising urbanization underlies the predicted need for 60% more food by 2050, but produced on the same amount of land as today. Improving photosynthetic efficiency is a largely untapped approach to addressing this problem. Here, we scale modelling processes from gene expression through photosynthetic metabolism to predict leaf physiology in evaluating acclimation of photosynthesis to rising atmospheric concentrations of CO2 ([CO2]). Model integration with the yggdrasil interface enabled asynchronous message passing between models. The multiscale model of soybean (Glycine max) photosynthesis calibrated to physiological measures at ambient [CO2] successfully predicted the acclimatory changes in the photosynthetic apparatus that were observed at 550 ppm [CO2] in the field. We hypothesized that genetic alteration is necessary to achieve optimal photosynthetic efficiency under global change. Flux control analysis in the metabolic system under elevated [CO2] identified enzymes requiring the greatest change to adapt optimally to the new conditions. This predicted that Rubisco was less limiting under elevated [CO2] and should be down-regulated allowing re-allocation of resource to enzymes controlling the rate of regeneration of ribulose-1,5-bisphosphate (RuBP). By linking the Gene Regulatory Network through protein concentration to the metabolic model, it was possible to identify transcription factors (TFs) that matched the up- and down-regulation of genes needed to improve photosynthesis. Most striking was TF Gm-GATA2, which down-regulated genes for Rubisco synthesis while up-regulating key genes controlling RuBP regeneration and starch synthesis. The changes predicted for this TF most closely matched the physiological ideotype that the modelling predicted as optimal for the future elevated [CO2] world.

Multiscale plant modeling: from genome to phenome and beyond

“…A third area in plant multiscale modeling are models that were developed to explore strategies for engineering plants to achieve specific objectives. For example, a gene regulatory network model, a protein translation model, a mechanistic photosynthesis model, and a leaf-level physiological model were coupled to explore the impacts of genetic modifications to soybean photosynthesis in ambient and elevated CO 2 [33]. The integrated model was used to identify gene regulatory controls of the allocation of resources from Rubisco to RuBP regeneration, which has been shown to improve photosynthesis under elevated CO 2 .…”

“…The yggdrasil framework [50] is an Open source Python package that can couple models written in a variety of programming languages including C/C++, Python, R, Matlab, and Fortran. Kannan et al used the yggdrasil framework to one-way couple a gene regulatory network model (R), a protein translation model (Python), and a leaf photosynthesis model (Matlab) [33]. yggdrasil is also capable of two-way coupling between models with different timescales, throughout a simulation.…”

A low-cost automated growth chamber system for continuous measurements of gas exchange at canopy scale in dynamic conditions

“…For example, simulating dynamic light conditions is necessary to retrieve canopy scale data that would reflect environmental variability [ 4 ]. In fact, whereas most of the past experiments and models considered photosynthesis at the steady state [ 10 , 11 , 17 , 43 , 48 , 53 ], the importance of considering some photosynthetic processes in their transient states has been recognized [ 9 , 14 , 31 , 46 , 47 ]. Plants are exposed to fluctuating irradiance due to the movements of clouds, the effect of wind and the gaps within the canopy [ 35 , 39 ].…”

Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat

“…The model was also used to predict phenotypic responses due to altered circadian timing in clock-mutant plants [30]. More advanced multiscale models have been used to explore the impacts of genetic modifications to soybean photosynthesis in ambient and elevated carbon dioxide [31] and to explore potential gene engineering strategies for producing trees with improved bioenergy traits while mitigating negative impacts on tree growth [32].…”