ePlant for quantitative and predictive plant science research in the big data era—Lay the foundation for the future model guided crop breeding, engineering and agronomy

From leaf to multiscale models of photosynthesis: applications and challenges for crop improvement

To keep up with the growth of human population and to circumvent deleterious effects of global climate change, it is essential to enhance crop yield to achieve higher production. Here we review mathematical models of oxygenic photosynthesis that are extensively used, and discuss in depth a subset that accounts for diverse approaches providing solutions to our objective. These include models (1) to study different ways to enhance photosynthesis, such as fine-tuning antenna size, photoprotection and electron transport; (2) to bioengineer carbon metabolism; and (3) to evaluate the interactions between the process of photosynthesis and the seasonal crop dynamics, or those that have included statistical whole-genome prediction methods to quantify the impact of photosynthesis traits on the improvement of crop yield. We conclude by emphasizing that the results obtained in these studies clearly demonstrate that mathematical modelling is a key tool to examine different approaches to improve photosynthesis for better productivity, while effective multiscale crop models, especially those that also include remote sensing data, are indispensable to verify different strategies to obtain maximized crop yields.

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

Modelling plants across scales of biological organisation for guiding crop improvement

Grain yield improvement in globally important staple crops is critical in the coming decades if production is to keep pace with growing demand; so there is increasing interest in understanding and manipulating plant growth and developmental traits for better crop productivity. However, this is confounded by complex cross-scale feedback regulations and a limited ability to evaluate the consequences of manipulation on crop production. Plant/crop modelling could hold the key to deepening our understanding of dynamic trait-crop-environment interactions and predictive capabilities for supporting genetic manipulation. Using photosynthesis and crop growth as an example, this review summarises past and present experimental and modelling work, bringing about a model-guided crop improvement thrust, encompassing research into: (1) advancing cross-scale plant/crop modelling that connects across biological scales of organisation using a trait dissection-integration modelling principle; (2) improving the reliability of predicted molecular-trait-crop-environment system dynamics with experimental validation; and (3) innovative model application in synergy with cross-scale experimentation to evaluate G×M×E and predict yield outcomes of genetic intervention (or lack of it) for strategising further molecular and breeding efforts. The possible future roles of cross-scale plant/crop modelling in maximising crop improvement are discussed.

Enhancing control systems of higher plant culture chambers via multilevel structural mechanistic modelling

Modelling higher plant growth is of strategic interest for modern agriculture as well as for the development of bioregenerative life support systems for space applications, where crop growth is expected to play an essential role. The capability of constraint-based metabolic models to cope the diel dynamics of plants growth is integrated into a multilevel modelling approach including mass and energy transfer and enzyme kinetics. Lactuca sativa is used as an exemplary crop to validate, with experimental data, the approach presented as well as to design a novel model-based predictive control strategy embedding metabolic information. The proposed modelling strategy predicts with high accuracy the dynamics of gas exchange and the distribution of fluxes in the metabolic network whereas the control architecture presented can be useful to manage higher plants chambers and open new ways of merging metabolome and control algorithms.

The use of R in photosynthesis research

R is one of the most commonly used analytical tools in the plant sciences. To identify key trends in general reported R use and patterns in photosynthesis research, we explored the frequency of R use in 2966 articles published in the 377 journals with 'photosynthesis' in the title from 2010 to 2019 using the Web of Science search. Solutions provided by each R package cited in the articles or online sources was recorded and classified. The percentage of research articles reporting R use increased linearly from 3.6% in 2010 to 12.5% in 2019. The three main categories of R package solutions were 'general statistical calculations and graph packages' (G); 'photosynthesis special-purpose packages' (S); and 'genetic and evolutionary packages' (E). The top five R packages cited were nlme (G), lme4 (G), multcomp (G), plantecophys (S), and ape (E). The increasing popularity of R use in photosynthesis research is due to its user-friendly and abundant open-source codes online for handling specific issues, particularly in fitting photosynthesis models. These findings are limited by the number of articles and online sources, but they reveal a significant increase in usage in photosynthesis research over the past decade and have a bright prospect in the future.

Alterations in stomatal response to fluctuating light increase biomass and yield of rice under drought conditions

The acceleration of stomatal closure upon high to low light transition could improve plant water use efficiency and drought tolerance. Herein, using genome-wide association study, we showed that the genetic variation in OsNHX1 was strongly associated with the changes in τ_cl , the time constant of stomatal closure, in 206 rice accessions. OsNHX1 overexpression in rice resulted in a decrease in τ_cl , and an increase in biomass, grain yield under drought. Conversely, OsNHX1 knockout by CRISPR/CAS9 shows opposite trends for these traits. We further found three haplotypes spanning the OsNHX1 promoter and CDS regions. Two among them, HapII and HapIII, were found to be associated with a high and low τ_cl , respectively. A near-isogenic line (NIL, S464) was developed through replacing the genomic region harboring HapII (~10 kb) from MH63 (recipient) rice cultivar by the same sized genomic region containing Hap III from 02428 (donor). Compared with MH63, S464 shows a reduction by 35% in τ_cl and an increase by 40% in the grain yield under drought. However, under normal conditions, S464 maintains closely similar grain yield as MH63. The global distribution of the two OsNHX1 haplotypes is associated with the local precipitation. Taken together, the natural variation in OsNHX1 could be utilized to manipulate the stomatal dynamics for an improved rice drought tolerance.

Identify QTLs and candidate genes underlying source-, sink-, and grain yield-related traits in rice by integrated analysis of bi-parental and natural populations

The source-sink relationship determines the ultimate grain yield of rice. In this study, we used a set of reciprocal introgression lines (ILs) derived from Xuishui09 × IR2061 to map quantitative trait loci (QTLs) that were associated with sink-, source-, and grain yield-related traits. A total of 95 QTLs influencing eight measured traits were identified using 6181 high-quality single nucleotide polymorphism markers. Nine background-independent QTLs were consistently detected in seven chromosomal regions in different genetic backgrounds. Seven QTLs clusters simultaneously affected sink-, source-, and grain yield-related traits, probably due to the genetic basis of significant correlations of grain yield with source and sink traits. We selected 15 candidate genes in the four QTLs consistently identified in the two populations by performing gene-based association and haplotype analyses using 2288 accessions from the 3K project. Among these, LOC_Os03g48970 for qTSN3b, LOC_Os06g04710 for qFLL6a, and LOC_Os07g32510 for qTGW7 were considered as the most likely candidate genes based on functional annotations. These results provide a basis for further study of candidate genes and for the development of high-yield rice varieties by balancing source–sink relationships using marker-assisted selection.

Towards a multiscale crop modelling framework for climate change adaptation assessment

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

In silico study of the role of cell growth factors in photosynthesis using a virtual leaf tissue generator coupled to a microscale photosynthesis gas exchange model

Computational tools that allow in silico analysis of the role of cell growth and division on photosynthesis are scarce. We present a freely available tool that combines a virtual leaf tissue generator and a two-dimensional microscale model of gas transport during C3 photosynthesis. A total of 270 mesophyll geometries were generated with varying degrees of growth anisotropy, growth extent, and extent of schizogenous airspace formation in the palisade mesophyll. The anatomical properties of the virtual leaf tissue and microscopic cross-sections of actual leaf tissue of tomato (Solanum lycopersicum L.) were statistically compared. Model equations for transport of CO2 in the liquid phase of the leaf tissue were discretized over the geometries. The virtual leaf tissue generator produced a leaf anatomy of tomato that was statistically similar to real tomato leaf tissue. The response of photosynthesis to intercellular CO2 predicted by a model that used the virtual leaf tissue geometry compared well with measured values. The results indicate that the light-saturated rate of photosynthesis was influenced by interactive effects of extent and directionality of cell growth and degree of airspace formation through the exposed surface of mesophyll per leaf area. The tool could be used further in investigations of improving photosynthesis and gas exchange in relation to cell growth and leaf anatomy.

A sixty-year tryst with photosynthesis and related processes: an informal personal perspective

After briefly describing my early collaborative work at the University of Allahabad, that had laid the foundation of my research life, I present here some of our research on photosynthesis at the University of Illinois at Urbana-Champaign, randomly selected from light absorption to NADP⁺ reduction in plants, algae, and cyanobacteria. These include the fact that (i) both the light reactions I and II are powered by light absorbed by chlorophyll (Chl) a of different spectral forms; (ii) light emission (fluorescence, delayed fluorescence, and thermoluminescence) by plants, algae, and cyanobacteria provides detailed information on these reactions and beyond; (iii) primary photochemistry in both the photosystems I (PS I) and II (PS II) occurs within a few picoseconds; and (iv) most importantly, bicarbonate plays a unique role on the electron acceptor side of PS II, specifically at the two-electron gate of PS II. Currently, the ongoing research around the world is, and should be, directed towards making photosynthesis better able to deal with the global issues (such as increasing population, dwindling resources, and rising temperature) particularly through genetic modification. However, basic research is necessary to continue to provide us with an understanding of the molecular mechanism of the process and to guide us in reaching our goals of increasing food production and other chemicals we need for our lives.

Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops

Recent years witnessed a stagnation in yield enhancement in major staple crops, which leads plant biologists and breeders to focus on an urgent challenge to dramatically increase crop yield to meet the growing food demand. Systems models have started to show their capacity in guiding crops improvement for greater biomass and grain yield production. Here we argue that systems models, phenomics and genomics combined are three pillars for the future breeding for high-yielding photosynthetically efficient crops (HYPEC). Briefly, systems models can be used to guide identification of breeding targets for a particular cultivar and define optimal physiological and architectural parameters for a particular crop to achieve high yield under defined environments. Phenomics can support collection of architectural, physiological, biochemical and molecular parameters in a high-throughput manner, which can be used to support both model validation and model parameterization. Genomic techniques can be used to accelerate crop breeding by enabling more efficient mapping between genotypic and phenotypic variation, and guide genome engineering or editing for model-designed traits. In this paper, we elaborate on these roles and how they can work synergistically to support future HYPEC breeding.

Systems model-guided rice yield improvements based on genes controlling source, sink, and flow

A large number of genes related to source, sink, and flow have been identified after decades of research in plant genetics. Unfortunately, these genes have not been effectively utilized in modern crop breeding. This perspective paper aims to examine the reasons behind such a phenomenon and propose a strategy to resolve this situation. Specifically, we first systematically survey the currently cloned genes related to source, sink, and flow; then we discuss three factors hindering effective application of these identified genes, which include the lack of effective methods to identify limiting or critical steps in a signaling network, the misplacement of emphasis on properties, at the leaf, instead of the whole canopy level, and the non-linear complex interaction between source, sink, and flow. Finally, we propose the development of systems models of source, sink and flow, together with a detailed simulation of interactions between them and their surrounding environments, to guide effective use of the identified elements in modern rice breeding. These systems models will contribute directly to the definition of crop ideotype and also identification of critical features and parameters that limit the yield potential in current cultivars.