Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

Resource allocation modeling for autonomous prediction of plant cell phenotypes

Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO2 and O2, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.

Temporally Regulated Plant-Nematode Gene Networks Implicate Metabolic Pathways

Root-knot nematodes (RKN) (Meloidogyne spp.) constantly communicate with their host to establish and maintain specialized feeding cells. They likely regulate this interaction by monitoring host biology. As plant host biology is influenced by light and gene expression varies correspondingly, RKN gene transcription and biology likely follow similar patterns. We profiled RKN transcripts over a period of 24 h and identified approximately 1,000 differentially expressed genes (DEG) in nematode and model host Medicago truncatula, with the majority of DEG occurring in the middle of the dark period. Many of the plant DEG are involved in defense-response pathways, while the nematode DEG are involved in establishing infection, suggesting a strong host-nematode interaction occurring during the dark. To identify interacting genes, we developed a plant-nematode gene network based on DEG signals. The phenylpropanoid pathway was identified as a significant plant-nematode interacting pathway, representing four of 33 genes in the network. We further examined if this pathway interacts similarly in another host, tomato, by quantifying phenolic and flavonoid compounds produced by this pathway. Phenolic compounds showed a significant increase in production during the day in uninoculated plants as compared with during the night. However, during the dark period, there was an increase in flavonoid content in infected plants when compared with uninfected controls, indicating potential host defense mechanisms active during the height of nematode activity at night. This study elucidated cross-species interacting pathways that could be targeted to develop novel management strategies to these important pests.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Hybrid modelling of biological systems: current progress and future prospects

Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.

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

Senescence is the final stage of leaf development and is critical for plants' fitness as nutrient relocation from leaves to reproductive organs takes place. Although senescence is key in nutrient relocation and yield determination in cereal grain production, there is limited understanding of the genetic and molecular mechanisms that control it in major staple crops such as wheat. Senescence is a highly orchestrated continuum of interacting pathways throughout the lifecycle of a plant. Levels of gene expression, morphogenesis, and phenotypic development all play key roles. Yet, most studies focus on a short window immediately after anthesis. This approach clearly leaves out key components controlling the activation, development, and modulation of the senescence pathway before anthesis, as well as during the later developmental stages, during which grain development continues. Here, a computational multiscale modelling approach integrates multi-omics developmental data to attempt to simulate senescence at the molecular and plant level. To recreate the senescence process in wheat, core principles were borrowed from Arabidopsis Thaliana, a more widely researched plant model. The resulted model describes temporal gene regulatory networks and their effect on plant morphology leading to senescence. Digital phenotypes generated from images using a phenomics platform were used to capture the dynamics of plant development. This work provides the basis for the application of computational modelling to advance understanding of the complex biological trait senescence. This supports the development of a predictive framework enabling its prediction in changing or extreme environmental conditions, with a view to targeted selection for optimal lifecycle duration for improving resilience to climate change.

Multiscale plant modeling: from genome to phenome and beyond

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.

Phytochromes control metabolic flux, and their action at the seedling stage determines adult plant biomass

Phytochrome photoreceptors are known to regulate plastic growth responses to vegetation shade. However, recent reports also suggest an important role for phytochromes in carbon resource management, metabolism, and growth. Here, we use 13CO2 labelling patterns in multiallele phy mutants to investigate the role of phytochrome in the control of metabolic fluxes. We also combine quantitative data of 13C incorporation into protein and cell wall polymers, gas exchange measurements, and system modelling to investigate why biomass is decreased in adult multiallele phy mutants. Phytochrome influences the synthesis of stress metabolites such as raffinose and proline, and the accumulation of sugars, possibly through regulating vacuolar sugar transport. Remarkably, despite their modified metabolism and vastly altered architecture, growth rates in adult phy mutants resemble those of wild-type plants. Our results point to delayed seedling growth and smaller cotyledon size as the cause of the adult-stage phy mutant biomass defect. Our data signify a role for phytochrome in metabolic stress physiology and carbon partitioning, and illustrate that phytochrome action at the seedling stage sets the trajectory for adult biomass production.

Catalytic liquefaction of sewage sludge to small molecular weight chemicals

The catalytic hydrotreatment of sewage sludge, the wet solid byproducts from wastewater treatment plants, using supported Ir, Pt, Pd, Ru catalysts had been investigated with different solvent conditions. Reactions were carried out in a batch set-up at elevated temperatures (400 °C) using a hydrogen donor (formic acid (FA) in isopropanol (IPA) or hydrogen gas), with sewage sludge obtained from different sampling places. Sewage sludge conversions of up to 83.72% were achieved using Pt/C, whereas the performance for the others catalysts is different and solvent had a strong effect on the conversion rate and product constitution. The sewage sludge oils were characterised using a range of analytical techniques (GC, GC-MS, GCxGC, GPC) and were shown to consist of monomers, mainly alkanes and higher oligomers.

Getting to the roots of aeroponic indoor farming

Vertical farming is a type of indoor agriculture where plants are cultivated in stacked systems. It forms a rapidly growing sector with new emerging technologies. Indoor farms often use soil-free techniques such as hydroponics and aeroponics. Aeroponics involves the application to roots of a nutrient aerosol, which can lead to greater plant productivity than hydroponic cultivation. Aeroponics is thought to resolve a variety of plant physiological constraints that occur within hydroponic systems. We synthesize existing studies of the physiology and development of crops cultivated under aeroponic conditions and identify key knowledge gaps. We identify future research areas to accelerate the sustainable intensification of vertical farming using aeroponic systems.

Digital life, a theory of minds, and mapping human and machine cultural universals

Emerging cybertechnologies, such as social digibots, bend epistemological conventions of life and culture already complicated by human and animal relationships. Virtually-augmented niches of machines and organic life promise new free-energy-governed selection of intelligent digital life. These provocative eco-evolutionary contexts demand a theory of (natural and artificial) minds to characterize and validate the immersive social phenomena universally-shaping cultural affordances.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

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.

Multi-tissue to whole plant metabolic modelling

Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.

An explanatory model of temperature influence on flowering through whole-plant accumulation of FLOWERING LOCUS T in Arabidopsis thaliana

We assessed mechanistic temperature influence on flowering by incorporating temperature-responsive flowering mechanisms across developmental age into an existing model. Temperature influences the leaf production rate as well as expression of FLOWERING LOCUS T (FT), a photoperiodic flowering regulator that is expressed in leaves. The Arabidopsis Framework Model incorporated temperature influence on leaf growth but ignored the consequences of leaf growth on and direct temperature influence of FT expression. We measured FT production in differently aged leaves and modified the model, adding mechanistic temperature influence on FT transcription, and causing whole-plant FT to accumulate with leaf growth. Our simulations suggest that in long days, the developmental stage (leaf number) at which the reproductive transition occurs is influenced by day length and temperature through FT, while temperature influences the rate of leaf production and the time (in days) the transition occurs. Further, we demonstrate that FT is mainly produced in the first 10 leaves in the Columbia (Col-0) accession, and that FT accumulation alone cannot explain flowering in conditions in which flowering is delayed. Our simulations supported our hypotheses that: (i) temperature regulation of FT, accumulated with leaf growth, is a component of thermal time, and (ii) incorporating mechanistic temperature regulation of FT can improve model predictions when temperatures change over time.

Practical steps to digital organism models, from laboratory model species to 'Crops in silico

A recent initiative named 'Crops in silico' 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, particularly directed to crop species. To that end, the group called for 'a paradigm shift in plant modelling, from largely isolated efforts to a connected community'. 'Wet' (experimental) research has been especially productive in plant science, since the adoption of Arabidopsis thaliana as a laboratory model species allowed the emergence of an Arabidopsis research community. Parts of this community invested in 'dry' (theoretical) research, under the rubric of Systems Biology. Our past research combined concepts from Systems Biology and crop modelling. Here we outline the approaches that seem most relevant to connected, 'digital organism' initiatives. We illustrate the scale of experimental research required, by collecting the kinetic parameter values that are required for a quantitative, dynamic model of a gene regulatory network. By comparison with the Systems Biology Markup Language (SBML) community, we note computational resources and community structures that will help to realize the potential for plant Systems Biology to connect with a broader crop science community.

A multi-model framework for the Arabidopsis life cycle

Linking our understanding of biological processes at different scales is a major conceptual challenge in biology and aggravated by differences in research methods. Modelling can be a useful approach to consolidating our understanding across traditional research domains. The laboratory model species Arabidopsis is very widely used to study plant growth processes and has also been tested more recently in ecophysiology and population genetics. However, approaches from crop modelling that might link these domains are rarely applied to Arabidopsis. Here, we combine plant growth models with phenology models from ecophysiology, using the agent-based modelling language Chromar. We introduce a simpler Framework Model of vegetative growth for Arabidopsis, FM-lite. By extending this model to include inflorescence and fruit growth and seed dormancy, we present a whole-life-cycle, multi-model FM-life, which allows us to simulate at the population level in various genotype × environment scenarios. Environmental effects on plant growth distinguish between the simulated life history strategies that were compatible with previously described Arabidopsis phenology. Our results simulate reproductive success that is founded on the broad range of physiological processes familiar from crop models and suggest an approach to simulating evolution directly in future.

An Integrative Systems Perspective on Plant Phosphate Research

The case for improving crop phosphorus-use-efficiency is widely recognized. Although much is known about the molecular and regulatory mechanisms, improvements have been hampered by the extreme complexity of phosphorus (P) dynamics, which involves soil chemistry; plant-soil interactions; uptake, transport, utilization and remobilization within plants; and agricultural practices. The urgency and direction of phosphate research is also dependent upon the finite sources of P, availability of stocks to farmers and reducing environmental hazards. This work introduces integrative systems approaches as a way to represent and understand this complexity, so that meaningful links can be established between genotype, environment, crop traits and yield. It aims to provide a large set of pointers to potential genes and research practice, with a view to encouraging members of the plant-phosphate research community to adopt such approaches so that, together, we can aid efforts in global food security.

Growth rate regulation is associated with developmental modification of source efficiency

Plants modulate their growth rate according to seasonal and environmental cues using a suite of growth repressors known to interact directly with cellular machinery controlling cell division and growth. Mutants lacking growth repressors show increased growth rates^1,2, but the mechanism by which these plants ensure source availability for faster growth is unclear. Here, we undertake a comprehensive analysis of the fast-growth phenotype of a quintuple growth-repressor mutant, using a combination of theoretical and experimental approaches to understand the physiological basis of source-sink coordination. Our results show that, in addition to the control of tissue growth rates, growth repressors also affect tissue composition and leaf thickness, modulating the efficiency of production of new photosynthetic capacity. Modelling suggests that increases in growth efficiency underlie growth-rate differences between the wild type and spatula della growth-repressor mutant, with spatula della requiring less carbon to synthesize a comparable photosynthetic capability to the wild type, and fixing more carbon per unit mass. We conclude that through control of leaf development, growth repressors regulate both source availability and sink strength to achieve growth-rate variation without risking a carbon deficit.

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.

Parameter inference to motivate asymptotic model reduction: An analysis of the gibberellin biosynthesis pathway

Developing effective strategies to use models in conjunction with experimental data is essential to understand the dynamics of biological regulatory networks. In this study, we demonstrate how combining parameter estimation with asymptotic analysis can reveal the key features of a network and lead to simplified models that capture the observed network dynamics. Our approach involves fitting the model to experimental data and using the profile likelihood to identify small parameters and cases where model dynamics are insensitive to changing particular individual parameters. Such parameter diagnostics provide understanding of the dominant features of the model and motivate asymptotic model reductions to derive simpler models in terms of identifiable parameter groupings. We focus on the particular example of biosynthesis of the plant hormone gibberellin (GA), which controls plant growth and has been mutated in many current crop varieties. This pathway comprises two parallel series of enzyme-substrate reactions, which have previously been modelled using the law of mass action (Middleton et al., 2012). Considering the GA20ox-mediated steps, we analyse the identifiability of the model parameters using published experimental data; the analysis reveals the ratio between enzyme and GA levels to be small and motivates us to perform a quasi-steady state analysis to derive a reduced model. Fitting the parameters in the reduced model reveals additional features of the pathway and motivates further asymptotic analysis which produces a hierarchy of reduced models. Calculating the Akaike information criterion and parameter confidence intervals enables us to select a parsimonious model with identifiable parameters. As well as demonstrating the benefits of combining parameter estimation and asymptotic analysis, the analysis shows how GA biosynthesis is limited by the final GA20ox-mediated steps in the pathway and generates a simple mathematical description of this part of the GA biosynthesis pathway.

Robustness of a Reconstituted Escherichia coli Protein Translation System Analyzed by Computational Modeling

Robustness against environmental changes is one of the major features of biological systems, but its origin is not well understood. We recently constructed a large-scale computational model of an Escherichia coli-based reconstituted in vitro translation system that enumerates all protein synthesis processes in detail. Our model synthesizes a formyl-Met-Gly-Gly tripeptide (MGG peptide) from 27 initial molecular components through 968 biochemical reactions. Among the 968 kinetic parameters, 483 are nonzero parameters, and the simulator was used to determine how perturbations of 483 individual reactions affect the complex reaction network. We found that even when the kinetic parameter was changed from 100- to 0.01-fold, 94% of the changes hardly affected the two indicators of reaction dynamics in MGG peptide synthesis, which represent the yield of the MGG peptide and the initial lag-time of the peptide synthesis. Moreover, none of the indicators increased proportionally to these changes: e.g., a 100-fold increase in the kinetic parameter increased the yield by only 2.2-fold at most, indicating the insensitivity of the reaction network to perturbation. Robustness and insensitivity are likely to be a common feature of large-scale biological reaction networks.

Plant Phenomics, From Sensors to Knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

Putting primary metabolism into perspective to obtain better fruits

Background

One of the key goals of fruit biology is to understand the factors that influence fruit growth and quality, ultimately with a view to manipulating them for improvement of fruit traits.

Scope

Primary metabolism, which is not only essential for growth but is also a major component of fruit quality, is an obvious target for improvement. However, metabolism is a moving target that undergoes marked changes throughout fruit growth and ripening.

Conclusions

Agricultural practice and breeding have successfully improved fruit metabolic traits, but both face the complexity of the interplay between development, metabolism and the environment. Thus, more fundamental knowledge is needed to identify further strategies for the manipulation of fruit metabolism. Nearly two decades of post-genomics approaches involving transcriptomics, proteomics and/or metabolomics have generated a lot of information about the behaviour of fruit metabolic networks. Today, the emergence of modelling tools is providing the opportunity to turn this information into a mechanistic understanding of fruits, and ultimately to design better fruits. Since high-quality data are a key requirement in modelling, a range of must-have parameters and variables is proposed.

Next-generation strategies for understanding and influencing source-sink relations in crop plants

Whether plants are source or sink limited, that is, whether carbon assimilation or rather assimilate usage is ultimately responsible for crop yield, has been the subject of intense debate over several decades. Here we provide a short review of this debate before focusing on the use of transgenic intervention as a means to influence yield by modifying either source or sink function (or both). Given the relatively low success rates of strategies targeting single genes we highlight the success of multi-target transformations. The emergence of whole plant models and the potential impact that these will have in aiding yield improvement strategies are then discussed. We end by providing our perspective for next generation strategies for improving crop plants by means of manipulating their source-sink relations.

A 3-D functional-structural grapevine model that couples the dynamics of water transport with leaf gas exchange

Background and Aims

Predicting both plant water status and leaf gas exchange under various environmental conditions is essential for anticipating the effects of climate change on plant growth and productivity. This study developed a functional-structural grapevine model which combines a mechanistic understanding of stomatal function and photosynthesis at the leaf level (i.e. extended Farqhuhar-von Caemmerer-Berry model) and the dynamics of water transport from soil to individual leaves (i.e. Tardieu-Davies model).

Methods

The model included novel features that account for the effects of xylem embolism (fPLC) on leaf hydraulic conductance and residual stomatal conductance (g0), variable root and leaf hydraulic conductance, and the microclimate of individual organs. The model was calibrated with detailed datasets of leaf photosynthesis, leaf water potential, xylem sap abscisic acid (ABA) concentration and hourly whole-plant transpiration observed within a soil drying period, and validated with independent datasets of whole-plant transpiration under both well-watered and water-stressed conditions.

Key Results

The model well captured the effects of radiation, temperature, CO2 and vapour pressure deficit on leaf photosynthesis, transpiration, stomatal conductance and leaf water potential, and correctly reproduced the diurnal pattern and decline of water flux within the soil drying period. In silico analyses revealed that decreases in g0 with increasing fPLC were essential to avoid unrealistic drops in leaf water potential under severe water stress. Additionally, by varying the hydraulic conductance along the pathway (e.g. root and leaves) and changing the sensitivity of stomatal conductance to ABA and leaf water potential, the model can produce different water use behaviours (i.e. iso- and anisohydric).

Conclusions

The robust performance of this model allows for modelling climate effects from individual plants to fields, and for modelling plants with complex, non-homogenous canopies. In addition, the model provides a basis for future modelling efforts aimed at describing the physiology and growth of individual organs in relation to water status.

Phytochrome, Carbon Sensing, Metabolism, and Plant Growth Plasticity

Phytochrome signaling controls biomass accumulation, growth plasticity, and metabolism.

The Importance of the Circadian Clock in Regulating Plant Metabolism

Carbohydrates are the primary energy source for plant development. Plants synthesize sucrose in source organs and transport them to sink organs during plant growth. This metabolism is sensitive to environmental changes in light quantity, quality, and photoperiod. In the daytime, the synthesis of sucrose and starch accumulates, and starch is degraded at nighttime. The circadian clock genes provide plants with information on the daily environmental changes and directly control many developmental processes, which are related to the path of primary metabolites throughout the life cycle. The circadian clock mechanism and processes of metabolism controlled by the circadian rhythm were studied in the model plant Arabidopsis and in the crops potato and rice. However, the translation of molecular mechanisms obtained from studies of model plants to crop plants is still difficult. Crop plants have specific organs such as edible seed and tuber that increase the size or accumulate valuable metabolites by harvestable metabolic components. Human consumers are interested in the regulation and promotion of these agriculturally significant crops. Circadian clock manipulation may suggest various strategies for the increased productivity of food crops through using environmental signal or overcoming environmental stress.

Photosynthate partitioning to starch in Arabidopsis thaliana is insensitive to light intensity but sensitive to photoperiod due to a restriction on growth in the light in short photoperiods

Photoperiod duration can be predicted from previous days, but irradiance fluctuates in an unpredictable manner. To investigate how allocation to starch responds to changes in these two environmental variables, Arabidopsis Col-0 was grown in a 6 h and a 12 h photoperiod at three different irradiances. The absolute rate of starch accumulation increased when photoperiod duration was shortened and when irradiance was increased. The proportion of photosynthate allocated to starch increased strongly when photoperiod duration was decreased but only slightly when irradiance was decreased. There was a small increase in the daytime level of sucrose and twofold increases in glucose, fructose and glucose 6-phosphate at a given irradiance in short photoperiods compared to long photoperiods. The rate of starch accumulation correlated strongly with sucrose and glucose levels in the light, irrespective of whether these sugars were responding to a change in photoperiod or irradiance. Whole plant carbon budget modelling revealed a selective restriction of growth in the light period in short photoperiods. It is proposed that photoperiod sensing, possibly related to the duration of the night, restricts growth in the light period in short photoperiods, increasing allocation to starch and providing more carbon reserves to support metabolism and growth in the long night.

Photosynthetic Entrainment of the Circadian Clock Facilitates Plant Growth under Environmental Fluctuations: Perspectives from an Integrated Model of Phase Oscillator and Phloem Transportation

Plants need to avoid carbon starvation and resultant growth inhibition under fluctuating light environments to ensure optimal growth and reproduction. As diel patterns of carbon metabolism are influenced by the circadian clock, appropriate regulation of the clock is essential for plants to properly manage their carbon resources. For proper adjustment of the circadian phase, higher plants utilize environmental signals such as light or temperature and metabolic signals such as photosynthetic products; the importance of the latter as phase regulators has been recently elucidated. A mutant of Arabidopsis thaliana that is deficient in phase response to sugar has been shown, under fluctuating light conditions, to be unable to adjust starch turnover and to realize carbon homeostasis. Whereas, the effects of light entrainment on growth and survival of higher plants are well studied, the impact of phase regulation by sugar remains unknown. Here we show that endogenous sugar entrainment facilitates plant growth. We integrated two mathematical models, one describing the dynamics of carbon metabolism in A. thaliana source leaves and the other growth of sink tissues dependent on sucrose translocation from the source. The integrated model predicted that sugar-sensitive plants grow faster than sugar-insensitive plants under constant as well as changing photoperiod conditions. We found that sugar entrainment enables efficient carbon investment for growth by stabilizing sucrose supply to sink tissues. Our results highlight the importance of clock entrainment by both exogenous and endogenous signals for optimizing growth and increasing fitness.

Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These 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. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.

Advances on plant-pathogen interactions from molecular toward systems biology perspectives

In the past 2 decades, progress in molecular analyses of the plant immune system has revealed key elements of a complex response network. Current paradigms depict the interaction of pathogen-secreted molecules with host target molecules leading to the activation of multiple plant response pathways. Further research will be required to fully understand how these responses are integrated in space and time, and exploit this knowledge in agriculture. In this review, we highlight systems biology as a promising approach to reveal properties of molecular plant-pathogen interactions and predict the outcome of such interactions. We first illustrate a few key concepts in plant immunity with a network and systems biology perspective. Next, we present some basic principles of systems biology and show how they allow integrating multiomics data and predict cell phenotypes. We identify challenges for systems biology of plant-pathogen interactions, including the reconstruction of multiscale mechanistic models and the connection of host and pathogen models. Finally, we outline studies on resistance durability through the robustness of immune system networks, the identification of trade-offs between immunity and growth and in silico plant-pathogen co-evolution as exciting perspectives in the field. We conclude that the development of sophisticated models of plant diseases incorporating plant, pathogen and climate properties represent a major challenge for agriculture in the future.

Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models

Mechanistic understanding of multi-scale biological processes, such as cell proliferation in a changing biological tissue, is readily facilitated by computational models. While tools exist to construct and simulate multi-scale models, the statistical inference of the unknown model parameters remains an open problem. Here, we present and benchmark a parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm, tailored for high-performance computing clusters. pABC SMC is fully automated and returns reliable parameter estimates and confidence intervals. By running the pABC SMC algorithm for ∼10⁶ hr, we parameterize multi-scale models that accurately describe quantitative growth curves and histological data obtained in vivo from individual tumor spheroid growth in media droplets. The models capture the hybrid deterministic-stochastic behaviors of 10⁵-10⁶ of cells growing in a 3D dynamically changing nutrient environment. The pABC SMC algorithm reliably converges to a consistent set of parameters. Our study demonstrates a proof of principle for robust, data-driven modeling of multi-scale biological systems and the feasibility of multi-scale model parameterization through statistical inference.

The Transcription Factor ATHB5 Affects GA-Mediated Plasticity in Hypocotyl Cell Growth during Seed Germination

Gibberellic acid (GA)-mediated cell expansion initiates the seed-to-seedling transition in plants and is repressed by DELLA proteins. Using digital single-cell analysis, we identified a cellular subdomain within the midhypocotyl, whose expansion drives the final step of this developmental transition under optimal conditions. Using network inference, the transcription factor ATHB5 was identified as a genetic factor whose localized expression promotes GA-mediated expansion specifically within these cells. Both this protein and its putative growth-promoting target EXPANSIN3 are repressed by DELLA, and coregulated at single-cell resolution during seed germination. The cellular domains of hormone sensitivity were explored within the Arabidopsis (Arabidopsis thaliana) embryo by putting seeds under GA-limiting conditions and quantifying cellular growth responses. The middle and upper hypocotyl have a greater requirement for GA to promote cell expansion than the lower embryo axis. Under these conditions, germination was still completed following enhanced growth within the radicle and lower axis. Under GA-limiting conditions, the athb5 mutant did not show a phenotype at the level of seed germination, but it did at a cellular level with reduced cell expansion in the hypocotyl relative to the wild type. These data reveal that the spatiotemporal cell expansion events driving this transition are not determinate, and the conditional use of GA-ATHB5-mediated hypocotyl growth under optimal conditions may be used to optionally support rapid seedling growth. This study demonstrates that multiple genetic and spatiotemporal cell expansion mechanisms underlie the seed to seedling transition in Arabidopsis.

Progress in genome sequencing will accelerate molecular breeding in cotton (Gossypium spp.)

Cotton (Gossypium spp.) is the single most important spinning fiber that has economic significance worldwide. Cotton is one of the most value-added crops and an excellent model system for the analysis of polyploidization and cell development. Thus, the Cotton Genome Consortium has made rapid and significant progress in whole genome sequencing studies in the last decade. Developments in cotton genome sequencing and assembly provide powerful tools for dissecting the genetic and molecular bases of agronomically important traits and establishing regulatory networks on these processes, which leads to molecular breeding. Here, we briefly review these advances, emphasizing their implications in the genetic improvement of cotton with a particular focus on fiber quality and yield. Moreover, major progresses in chloroplast and mitochondrial genomes have also been summarized.

Jasmonates: signal transduction components and their roles in environmental stress responses

Jasmonates, oxylipin-type plant hormones, are implicated in diverse aspects of plant growth development and interaction with the environment. Following diverse developmental and environmental cues, jasmonate is produced, conjugated to the amino acid isoleucine and perceived by a co-receptor complex composed of the Jasmonate ZIM-domain (JAZ) repressor proteins and an E3 ubiquitin ligase complex containing the F-box CORONATINE INSENSITIVE 1 (COI1). This event triggers the degradation of the JAZ proteins and the release of numerous transcription factors, including MYC2 and its homologues, which are otherwise bound and inhibited by the JAZ repressors. Here, we will review the role of the COI1, JAZ and MYC2 proteins in the interaction of the plant with its environment, illustrating the significance of jasmonate signalling, and of the proteins involved, for responses to both biotic stresses caused by insects and numerous microbial pathogens and abiotic stresses caused by adverse climatic conditions. It has also become evident that crosstalk with other hormone signals, as well as light and clock signals, plays an important role in the control and fine-tuning of these stress responses. Finally, we will discuss how several pathogens exploit the jasmonate perception and early signalling machinery to decoy the plants defence systems.

Jasmonates: signal transduction components and their roles in environmental stress responses

Jasmonates, oxylipin-type plant hormones, are implicated in diverse aspects of plant growth development and interaction with the environment. Following diverse developmental and environmental cues, jasmonate is produced, conjugated to the amino acid isoleucine and perceived by a co-receptor complex composed of the Jasmonate ZIM-domain (JAZ) repressor proteins and an E3 ubiquitin ligase complex containing the F-box CORONATINE INSENSITIVE 1 (COI1). This event triggers the degradation of the JAZ proteins and the release of numerous transcription factors, including MYC2 and its homologues, which are otherwise bound and inhibited by the JAZ repressors. Here, we will review the role of the COI1, JAZ and MYC2 proteins in the interaction of the plant with its environment, illustrating the significance of jasmonate signalling, and of the proteins involved, for responses to both biotic stresses caused by insects and numerous microbial pathogens and abiotic stresses caused by adverse climatic conditions. It has also become evident that crosstalk with other hormone signals, as well as light and clock signals, plays an important role in the control and fine-tuning of these stress responses. Finally, we will discuss how several pathogens exploit the jasmonate perception and early signalling machinery to decoy the plants defence systems.

Dual transcriptome analysis reveals insights into the response to Rice black-streaked dwarf virus in maize

Maize rough dwarf disease (MRDD) is a viral infection that results in heavy yield losses in maize worldwide, particularly in the summer maize-growing regions of China. MRDD is caused by the Rice black-streaked dwarf virus (RBSDV). In the present study, analyses of microRNAs (miRNAs), the degradome, and transcriptome sequences were used to elucidate the RBSDV-responsive pathway(s) in maize. Genomic analysis indicated that the expression of three non-conserved and 28 conserved miRNAs, representing 17 known miRNA families and 14 novel miRNAs, were significantly altered in response to RBSDV when maize was inoculated at the V3 (third leaf) stage. A total of 99 target transcripts from 48 genes of 10 known miRNAs were found to be responsive to RBSDV infection. The annotations of these target genes include a SQUAMOSA promoter binding (SPB) protein, a P450 reductase, an oxidoreductase, and a ubiquitin-related gene, among others. Characterization of the entire transcriptome suggested that a total of 28 and 1085 differentially expressed genes (DEGs) were detected at 1.5 and 3.0 d, respectively, after artificial inoculation with RBSDV. The expression patterns of cell wall- and chloroplast-related genes, and disease resistance- and stress-related genes changed significantly in response to RBSDV infection. The negatively regulated genes GRMZM2G069316 and GRMZM2G031169, which are the target genes for miR169i-p5 and miR8155, were identified as a nucleolin and a NAD(P)-binding Rossmann-fold superfamily protein in maize, respectively. The gene ontology term GO:0003824, including GRMZM2G031169 and other 51 DEGs, was designated as responsive to RBSDV.

The Intracellular Dynamics of Circadian Clocks Reach for the Light of Ecology and Evolution

A major challenge for biology is to extend our understanding of molecular regulation from the simplified conditions of the laboratory to ecologically relevant environments. Tractable examples are essential to make these connections for complex, pleiotropic regulators and, to go further, to link relevant genome sequences to field traits. Here, I review the case for the biological clock in higher plants. The gene network of the circadian clock drives pervasive, 24-hour rhythms in metabolism, behavior, and physiology across the eukaryotes and in some prokaryotes. In plants, the scope of chronobiology is now extending from the most tractable, intracellular readouts to the clock's many effects at the whole-organism level and across the life cycle, including biomass and flowering. I discuss five research areas where recent progress might be integrated in the future, to understand not only circadian functions in natural conditions but also the evolution of the clock's molecular mechanisms.

50 years of Arabidopsis research: highlights and future directions

922 I. 922 II. 922 III. 925 IV. 925 V. 926 VI. 927 VII. 928 VIII. 929 IX. 930 X. 931 XI. 932 XII. 933 XIII. Natural variation and genome-wide association studies 934 XIV. 934 XV. 935 XVI. 936 XVII. 937 937 References 937 SUMMARY: The year 2014 marked the 25(th) International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. We present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlighting some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome-wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology.

How can we make plants grow faster? A source-sink perspective on growth rate

Growth is a major component of fitness in all organisms, an important mediator of competitive interactions in plant communities, and a central determinant of yield in crops. Understanding what limits plant growth is therefore of fundamental importance to plant evolution, ecology, and crop science, but each discipline views the process from a different perspective. This review highlights the importance of source-sink interactions as determinants of growth. The evidence for source- and sink-limitation of growth, and the ways in which regulatory molecular feedback systems act to maintain an appropriate source:sink balance, are first discussed. Evidence clearly shows that future increases in crop productivity depend crucially on a quantitative understanding of the extent to which sources or sinks limit growth, and how this changes during development. To identify bottlenecks limiting growth and yield, a holistic view of growth is required at the whole-plant scale, incorporating mechanistic interactions between physiology, resource allocation, and plant development. Such a holistic perspective on source-sink interactions will allow the development of a more integrated, whole-system level understanding of growth, with benefits across multiple disciplines.

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

Inference and Prediction of Metabolic Network Fluxes

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.

Seasonal shift in timing of vernalization as an adaptation to extreme winter

The requirement for vernalization, a need for prolonged cold to trigger flowering, aligns reproductive development with favorable spring conditions. In Arabidopsis thaliana vernalization depends on the cold-induced epigenetic silencing of the floral repressor locus FLC. Extensive natural variation in vernalization response is associated with A. thaliana accessions collected from different geographical regions. Here, we analyse natural variation for vernalization temperature requirement in accessions, including those from the northern limit of the A. thaliana range. Vernalization required temperatures above 0°C and was still relatively effective at 14°C in all the accessions. The different accessions had characteristic vernalization temperature profiles. One Northern Swedish accession showed maximum vernalization at 8°C, both at the level of flowering time and FLC chromatin silencing. Historical temperature records predicted all accessions would vernalize in autumn in N. Sweden, a prediction we validated in field transplantation experiments. The vernalization response of the different accessions was monitored over three intervals in the field and found to match that when the average field temperature was given as a constant condition. The vernalization temperature range of 0–14°C meant all accessions fully vernalized before snowfall in N. Sweden. These findings have important implications for understanding the molecular basis of adaptation and for predicting the consequences of climate change on flowering time.

DOI:http://dx.doi.org/10.7554/eLife.06620.001

Plants are not able to move around and so they need to be able to adapt their growth and development to seasonal changes in their environment. For example, prolonged exposure to cold temperatures during winter can prime some plants to flower when temperatures increase in the spring—a process called vernalization. In these plants, extended periods of cold temperatures lead to lower activity of a gene called FLC, which normally inhibits flowering.

In the plant Arabidopsis thaliana, vernalization requires several months of exposure to temperatures between 0–6°C. Recently, A. thaliana plants from southern Europe were found to vary in the temperature requirements for vernalization, responding to temperatures higher than 6°C. This suggested that plants from northern Europe might vernalize preferentially at lower temperatures. Here, Duncan et al. compared vernalization in a collection of A. thaliana plants (or ‘accessions’) sampled from different regions of Sweden and the UK.

The experiments show that all the accessions needed temperatures above 0°C to vernalize and that vernalization still worked relatively well at temperatures as high as 14°C. The optimal temperature range for vernalization differed between the accessions, but plants from more northern areas did not necessarily vernalize at lower temperatures. For example, for one particular accession from northern Sweden, the temperature that is optimum for vernalization was 8°C, a notably higher temperature than expected.

Historical local climate records suggested that this accession would vernalize before the first snowfall of the winter in North Sweden. Duncan et al. confirmed this proposal with field experiments. Plants were grown in natural field sites in September and then moved into a greenhouse. The experiments show that the plants complete vernalization by November, which strongly suggests that FLC is silenced during autumn rather than during winter, as previously thought. This changed temperature response is due, in part, to a small number of tiny genetic differences in regions of the FLC gene that do not code for protein.

These findings have important implications for future studies of vernalization and flowering time, and for understanding how plants will adapt to on going and future climate change. The next step is to understand what causes these changed temperature responses at a molecular level, which should enable selective breeding for flowering and harvest date in a range of crops.

DOI:http://dx.doi.org/10.7554/eLife.06620.002

Measurement of plant growth in view of an integrative analysis of regulatory networks

As the regulatory networks of growth at the cellular level are elucidated at a fast pace, their complexity is not reduced; on the contrary, the tissue, organ and even whole-plant level affect cell proliferation and expansion by means of development-induced and environment-induced signaling events in growth regulatory processes. Measurement of growth across different levels aids in gaining a mechanistic understanding of growth, and in defining the spatial and temporal resolution of sampling strategies for molecular analyses in the model Arabidopsis thaliana and increasingly also in crop species. The latter claim their place at the forefront of plant research, since global issues and future needs drive the translation from laboratory model-acquired knowledge of growth processes to improvements in crop productivity in field conditions.

New mechanistic links between sugar and hormone signalling networks

Plant growth and development must be coordinated with metabolism, notably with the efficiency of photosynthesis and the uptake of nutrients. This coordination requires local connections between hormonal response and metabolic state, as well as long-distance connections between shoot and root tissues. Recently, several molecular mechanisms have been proposed to explain the integration of sugar signalling with hormone pathways. In this work, DELLA and PIF proteins have emerged as hubs in sugar-hormone cross-regulation networks.