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Modeling and simulating the growth of the branching of tree species remains a challenge. With existing approaches, we can reconstruct or rebuild the branching architectures of real tree species, but the simulation of the growth process remains unresolved. First, we present a tree growth model to generate branching architectures that resemble real tree species. Secondly, we use a quantitative morphometric approach to infer the shape similarity of the generated simulations and real tree species. Within a functional-structural plant model, we implement a set of biological parameters that affect the branching architecture of trees. By modifying the parameter values, we aim to generate basic shapes of spruce, pine, oak and poplar. Tree shapes are compared using geometric morphometrics of landmarks that capture crown and stem outline shapes. Five biological parameters, namely xylem flow, shedding rate, proprioception, gravitysense and lightsense, most influenced the generated tree branching patterns. Adjusting these five parameters resulted in the different tree shapes of spruce, pine, oak, and poplar. The largest effect was attributed to gravity, as phenotypic responses to this effect resulted in different growth directions of gymnosperm and angiosperm branching architectures. Since we were able to obtain branching architectures that resemble real tree species by adjusting only a few biological parameters, our model is extendable to other tree species. Furthermore, the model will also allow the simulation of structural tree-environment interactions. Our simplifying approach to shape comparison between tree species, landmark geometric morphometrics, showed that even the crown-trunk outlines capture species differences based on their contrasting branching architectures.

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Pig farming is recognized as an activity with great polluting potential. The aim was to investigate possible environmental risks of effluents from the stabilization pond (SP) and the raw effluent (RE) from the biodigestion process of swine residues, in different concentrations in the models Lactuca sativa and Allium cepa. Seeds were germinated in different dilutions, 100% (C1), 50% (C2), 25% (C3), 12.5% (C4), 6.25% (C5), 3.12% (C6), 0.78% (C7), and 0.39% (C8). Distilled water was used as the negative control (CN) and trifluralin (0.84 g/L-1) as the positive control. Germination (GR), root growth (RG), cell cycle, and oxidative stress (OS) were analyzed. To assess OS, the activity of the enzymes superoxide dismutase (SOD), catalase (CAT), and glutathione S-transferase (GST) and the quantification of glutathione (GSH) and lipid peroxidation (LPO) were analyzed. Data were submitted to ANOVA (one way), followed by the Kruskal-Wallis mean test (P ≤ 0.05). Chemical analysis showed high values of Cu, Fe, Mn, and Zn. Dilutions (C1, C2, C3 RE) and (C1 and C2 SP) inhibited GR and RG of L. sativa and A. cepa than other concentrations. The mitotic index showed a reduction in C5 (RE), C6, and C7 (SP) of L. sativa and C3 and C4 (SP) of A. cepa in relation to CN and higher frequencies of chromosomal alterations. Regarding the OS, only the concentrations of SP treatment showed statistical difference in relation to the NC: in L. sativa model, GSH at (C5 and C8) concentrations and LPO (C7); in A. cepa model, SOD (C3 and C4), GST (C4, C5 and C6), GSH (C5 and C8), and CAT (C3 and C7). The alterations in metabolism are possibly related to the metals, such as zinc and copper, observed in high amounts in the raw waste. The results allowed us to conclude that the raw and stabilization pond effluents offer environmental risks, requiring caution and monitoring in the use of these effluents.

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Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Riesling are developed relying on Bayesian model calibration with the goal to enhance the model towards a fully stochastic model. In this first step, model development targeting phenology, in particular budburst variability, phytomer development rate and internode growth are presented in detail. Multi-objective optimization is applied to estimate a single set of cardinal temperatures, which is used in phenology and growth modeling based on a development days approach. Measurements from two seasons of grapevines grown in a vineyard with free-air carbon dioxide enrichment (FACE) are used; thus, model building and selection are coupled with an investigation as to whether including effects of elevated CO2 conditions to be expected in 2050 would improve the models. The results show how natural variability complicates the detection of possible treatment effects, but demonstrate that Bayesian calibration in combination with mixed models can realistically recover natural shoot growth variability in predictions. We expect these and further stochastic model extensions to result in more realistic virtual plant simulations to study effects, which are used to conduct in silico studies of canopy microclimate and its effects on grape health and quality.

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Muscle and plant dynamics are most important during the high acceleration of saccades. Models have been developed to characterize muscle and plant dynamics. Building these models require an understanding of the length-tension (elastic) and force-velocity (viscous) relationships. Much work has been done to characterize these nonlinear functions, as they are influenced by innervation. However, the active force generator (active-state tension) in the muscle is still poorly understood. Thus, these models serve more to reveal where new studies of muscle behavior are needed than to explain what happens during a saccade.

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Systems biology is the study of biological interactions. These interactions exist between biological entities at every scale, from genes to population, and create incredibly complex networks of feedbacks responsible for emerging behaviors. To study these behaviors, biologists can use models based on mathematical and computational formalisms grounded on vast existing corpus of theoretical work. This chapter develops an overview of this process of plant systems biology study from the point of view of a teaching course, and introduces the methods and studies presented in this second edition of the "Plant Systems Biology" book series.

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Plant systems biology is currently facing several important challenges, whose nature depend on the considered frame of reference and associated scale. This review covers some of the issues associated respectively with the molecular, tissue, and whole-plant scales, as well as discusses the potential for latest advances in synthetic biology and machine-learning methods to be of use in the future of plant systems biology.

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This chapter compiles a list of useful references for aspiring plant systems biologists. It is structured in four sections focusing on systems biology books, reviews for the different types of models and resources at each plant scale, online resources, and plant systems biology communities.

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BACKGROUND: Woody plants (trees and shrubs) play an important role in terrestrial ecosystems, but their size and longevity make them difficult subjects for traditional experiments. In the last 20 years functional-structural plant models (FSPMs) have evolved: they consider the interplay between plant modular structure, the immediate environment and internal functioning. However, computational constraints and data deficiency have long been limiting factors in a broader application of FSPMs, particularly at the scale of forest communities. Recently, terrestrial laser scanning (TLS), has emerged as an invaluable tool for capturing the 3-D structure of forest communities, thus opening up exciting opportunities to explore and predict forest dynamics with FSPMs. SCOPE: The potential synergies between TLS-derived data and FSPMs have yet to be fully explored. Here, we summarize recent developments in FSPM and TLS research, with a specific focus on woody plants. We then evaluate the emerging opportunities for applying FSPMs in an ecological and evolutionary context, in light of TLS-derived data, with particular consideration of the challenges posed by scaling up from individual trees to whole forests. Finally, we propose guidelines for incorporating TLS data into the FSPM workflow to encourage overlap of practice amongst researchers. CONCLUSIONS: We conclude that TLS is a feasible tool to help shift FSPMs from an individual-level modelling technique to a community-level one. The ability to scan multiple trees, of multiple species, in a short amount of time, is paramount to gathering the detailed structural information required for parameterizing FSPMs for forest communities. Conventional techniques, such as repeated manual forest surveys, have their limitations in explaining the driving mechanisms behind observed patterns in 3-D forest structure and dynamics. Therefore, other techniques are valuable to explore how forests might respond to environmental change. A robust synthesis between TLS and FSPMs provides the opportunity to virtually explore the spatial and temporal dynamics of forest communities.

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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.

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BACKGROUND AND AIMS: Improved modelling of carbon assimilation and plant growth to low soil moisture requires evaluation of underlying mechanisms in the soil, roots, and shoots. The feedback between plants and their local environment throughout the whole spectrum soil-root-shoot-environment is crucial to accurately describe and evaluate the impact of environmental changes on plant development. This study presents a 3D functional structural plant model, in which shoot and root growth are driven by radiative transfer, photosynthesis, and soil hydrodynamics through different parameterisation schemes relating soil water deficit and carbon assimilation. The new coupled model is used to evaluate the impact of soil moisture availability on plant productivity for two different groups of flowering plants under different spatial configurations. METHODS: In order to address different aspects of plant development due to limited soil water availability, a 3D FSP model including root, shoot, and soil was constructed by linking three different well-stablished models of airborne plant, root architecture, and reactive transport in the soil. Different parameterisation schemes were used in order to integrate photosynthetic rate with root water uptake within the coupled model. The behaviour of the model was assessed on how the growth of two different types of plants, i.e. monocot and dicot, is impacted by soil water deficit under different competitive conditions: isolated (no competition), intra, and interspecific competition. KEY RESULTS: The model proved to be capable of simulating carbon assimilation and plant development under different growing settings including isolated monocots and dicots, intra, and interspecific competition. The model predicted that (1) soil water availability has a larger impact on photosynthesis than on carbon allocation; (2) soil water deficit has an impact on root and shoot biomass production by up to 90 % for monocots and 50 % for dicots; and (3) the improved dicot biomass production in interspecific competition was highly related to root depth and plant transpiration. CONCLUSIONS: An integrated model of 3D shoot architecture and biomass development with a 3D root system representation, including light limitation and water uptake considering soil hydraulics, was presented. Plant-plant competition and regulation on stomatal conductance to drought were able to be predicted by the model. In the cases evaluated here, water limitation impacted plant growth almost 10 times more than the light environment.

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BACKGROUND: Model organisms are at the core of life science research. Notable examples include the mouse as a model for humans, baker's yeast for eukaryotic unicellular life and simple genetics, or the enterobacteria phage λ in virology. Plant research was an exception to this rule, with researchers relying on a variety of non-model plants until the eventual adoption of Arabidopsis thaliana as primary plant model in the 1980s. This proved to be an unprecedented success, and several secondary plant models have since been established. Currently, we are experiencing another wave of expansion in the set of plant models. SCOPE: Since the 2000s, new model plants have been established to study numerous aspects of plant biology, such as the evolution of land plants, grasses, invasive and parasitic plant life, adaptation to environmental challenges, and the development of morphological diversity. Concurrent with the establishment of new plant models, the advent of the 'omics' era in biology has led to a resurgence of the more complex non-model plants. With this review, we introduce some of the new and fascinating plant models, outline why they are interesting subjects to study, the questions they will help to answer, and the molecular tools that have been established and are available to researchers. CONCLUSIONS: Understanding the molecular mechanisms underlying all aspects of plant biology can only be achieved with the adoption of a comprehensive set of models, each of which allows the assessment of at least one aspect of plant life. The model plants described here represent a step forward towards our goal to explore and comprehend the diversity of plant form and function. Still, several questions remain unanswered, but the constant development of novel technologies in molecular biology and bioinformatics is already paving the way for the next generation of plant models.

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Agent-based modelling (ABM) has become an established methodology in many areas of biology, ranging from the cellular to the ecological population and community levels. In plant science, two different scales have predominated in their use of ABM. One is the scale of populations and communities, through the modelling of collections of agents representing individual plants, interacting with each other and with the environment. The other is the scale of the individual plant, through the modelling, by functional-structural plant models (FSPMs), of agents representing plant building blocks, or metamers, to describe the development of plant architecture and functions within individual plants. The purpose of this review is to show key results and parallels in ABM for growth, mortality, carbon allocation, competition and reproduction across the scales from the plant organ to populations and communities on a range of spatial scales to the whole landscape. Several areas of application of ABMs are reviewed, showing that some issues are addressed by both population-level ABMs and FSPMs. Continued increase in the relevance of ABM to environmental science and management will be helped by greater integration of ABMs across these two scales.

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Carotenoids are the most important biocolor isoprenoids responsible for yellow, orange and red colors found in nature. In plants, they are synthesized in plastids of photosynthetic and sink organs and are essential molecules for photosynthesis, photo-oxidative damage protection and phytohormone synthesis. Carotenoids also play important roles in human health and nutrition acting as vitamin A precursors and antioxidants. Biochemical and biophysical approaches in different plants models have provided significant advances in understanding the structural and functional roles of carotenoids in plants as well as the key points of regulation in their biosynthesis. To date, different plant models have been used to characterize the key genes and their regulation, which has increased the knowledge of the carotenoid metabolic pathway in plants. In this chapter a description of each step in the carotenoid synthesis pathway is presented and discussed.

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BACKGROUND AND AIMS: Plant growth depends on carbon availability and allocation among organs. QualiTree has been designed to simulate carbon allocation and partitioning in the peach tree (Prunus persica), whereas MappleT is dedicated to the simulation of apple tree (Malus × domestica) architecture. The objective of this study was to couple both models and adapt QualiTree to apple trees to simulate organ growth traits and their within-tree variability. METHODS: MappleT was used to generate architectures corresponding to the 'Fuji' cultivar, accounting for the variability within and among individuals. These architectures were input into QualiTree to simulate shoot and fruit growth during a growth cycle. We modified QualiTree to account for the observed shoot polymorphism in apple trees, i.e. different classes (long, medium and short) that were characterized by different growth function parameters. Model outputs were compared with observed 3D tree geometries, considering shoot and final fruit size and growth dynamics. KEY RESULTS: The modelling approach connecting MappleT and QualiTree was appropriate to the simulation of growth and architectural characteristics at the tree scale (plant leaf area, shoot number and types, fruit weight at harvest). At the shoot scale, mean fruit weight and its variability within trees was accurately simulated, whereas the model tended to overestimate individual shoot leaf area and underestimate its variability for each shoot type. Varying the parameter related to the intensity of carbon exchange between shoots revealed that behaviour intermediate between shoot autonomy and a common assimilate pool was required to properly simulate within-tree fruit growth variability. Moreover, the model correctly dealt with the crop load effect on organ growth. CONCLUSIONS: This study provides understanding of the integration of shoot ontogenetic properties, carbon supply and transport between entities for simulating organ growth in trees. Further improvements regarding the integration of retroaction loops between carbon allocation and the resulting plant architecture are expected to allow multi-year simulations.

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Rome, 3rd-5th June 2015, was the setting for the Homeopathy Research Institute's (HRI) second conference with the theme 'Cutting Edge Research in Homeopathy'. Attended by over 250 delegates from 39 countries, this event provided an intense two and a half day programme of presentations and a forum for the sharing of ideas and the creation of international scientific collaborations. With 35 oral presentations from leaders in the field, the scientific calibre of the programme was high and the content diverse. This report summarises the key themes underpinning the cutting edge data presented by the speakers, including six key-note presentations, covering advancements in both basic and clinical research. Given the clear commitment of the global homeopathic community to high quality research, the resounding success of both Barcelona 2013 and Rome 2015 HRI conferences, and the dedicated support of colleagues, the HRI moves confidently forward towards the next biennial conference.

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A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels.

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Impacts of climate on stem growth in trees are studied in anatomical, ecophysiological, and ecological disciplines, but an integrative framework to assess those impacts remains lacking. In this opinion article, we argue that three research efforts are required to provide that integration. First, we need to identify the missing links in diel patterns in stem diameter and stem growth and relate those patterns to the underlying mechanisms that control water and carbon balance. Second, we should focus on the understudied mechanisms responsible for seasonal impacts on such diel patterns. Third, information on stem anatomy and ecophysiology should be integrated in the same experiments and mechanistic plant growth models to capture both diel and seasonal scales.

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