The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool

Background Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution. Results We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles. Conclusions We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management

The Regularizing Capacity of Metabolic Networks

Despite their topological complexity almost all functional properties of metabolic networks can be derived from steady-state dynamics. Indeed, many theoretical investigations (like flux-balance analysis) rely on extracting function from steady states. This leads to the interesting question, how metabolic networks avoid complex dynamics and maintain a steady-state behavior. Here, we expose metabolic network topologies to binary dynamics generated by simple local rules. We find that the networks' response is highly specific: Complex dynamics are systematically reduced on metabolic networks compared to randomized networks with identical degree sequences. Already small topological modifications substantially enhance the capacity of a network to host complex dynamic behavior and thus reduce its regularizing potential. This exceptionally pronounced regularization of dynamics encoded in the topology may explain, why steady-state behavior is ubiquitous in metabolism.Comment: 6 pages, 4 figure

The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool

Background Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution. Results We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles. Conclusions We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management

What is cost-efficient phenotyping? Optimizing costs for different scenarios

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5–26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10–20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs

Organization of Excitable Dynamics in Hierarchical Biological Networks

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks

Consistency analysis of metabolic correlation networks

<p>Abstract</p> <p>Background</p> <p>Metabolic correlation networks are derived from the covariance of metabolites in replicates of metabolomics experiments. They constitute an interesting intermediate between topology (i.e. the system's architecture defined by the set of reactions between metabolites) and dynamics (i.e. the metabolic concentrations observed as fluctuations around steady-state values in the metabolic network).</p> <p>Results</p> <p>Here we analyze, how such a correlation network changes over time, and compare the relative positions of metabolites in the correlation networks with those in established metabolic networks derived from genome databases. We find that network similarity indeed decreases with an increasing time difference between these networks during a day/night course and, counter intuitively, that proximity of metabolites in the correlation network is no indicator of proximity of the metabolites in the metabolic network.</p> <p>Conclusion</p> <p>The organizing principles of correlation networks are distinct from those of metabolic reaction maps. Time courses of correlation networks may in the future prove an important data source for understanding these organizing principles.</p

Analyse der Verbindung von Topologie und Dynamik in abstrakten Graphen und biologischen Netzwerken

In dieser Arbeit wurden abstrakte Graphen und reale Systeme mit einem erregbaren Medium, welches die gerichtete Weitergabe bzw. zufällige Erzeugung von Anregungen in einem Netzwerk steuert, dynamisch untersucht. Anregungsdichte und Anregungsmuster zeigen eine deutliche Abhängigkeit von der Graphentopologie. Die Anregungsdichte hängt im Wesentlichen vom Vernetzungsgrad, bis zu einem gewissen Maß von den Gradkorrelationen und nur im geringen Maße von der Gradverteilung eines Graphen ab. Der rapide Anstieg der Anregungsdichte bei zunehmender Vernetzung lässt sich auf unterschiedliche Verteilungsmuster der Anregungen zurückführen, und zwar global synchronisierte Aktivität (Spikes) bei geringen Vernetzungsdichten und positiv korrelierten Knotengraden und kooperatives dynamisches Verhalten in lokal begrenzten Graphenarealen (Bursts) bei hohen Vernetzungsdichten und negativen Gradkorrelationen. Die dynamische Charakterisierung über beide Anregungsmuster lässt sich auch durch die Rate spontaner Aktivierung steuern, wodurch das Modell interessant für die Untersuchung ähnlich stark vernetzter aber unterschiedlich strukturierter Graphentopologien ist. Modulare Strukturen und Hubs, welche elementare Grapheneigenschaften vieler realer Systeme darstellen, organisieren die synchronisierte Netzwerkfunktion bei unterschiedlichen Raten spontaner Aktivierung. Bei hoher Spontanaktivität sind Netzwerke mit modularen Eigenschaften durch synchronisiertes Verhalten innerhalb ihrer Module charakterisiert. Wenn die Struktur eines Graphen durch einen oder wenige Hubs in seinem Zentrum gekennzeichnet ist, wird bei niedriger Spontanaktivierung ein globales ringähnliches Synchronisationsphänomen sichtbar, welches durch die Formation von wellenähnlichen Propagationen erzeugt wird und das gesamte System über sein Zentrum (Hubs) erreicht. Die Koexistenz beider Organisationsprinzipien konnte in zwei verschiedenen neuronalen Netzwerken, welche biologische Beispiele hierarchischer Bauprinzipien darstellen, nachgewiesen werden. Im einzelnen zeigte sich allerdings, dass die Dynamik im cerebralen kortikalen Systemnetzwerk der Katze eher durch die modulare Organisation des Netzwerkes dominiert ist, während das dynamische Verhalten im zellulären neuronalen Netzwerk des Fadenwurms Caenorhabditis elegans sehr stark von Hubs beeinflusst wird. Im Metabolismus der Pflanze Arabidopsis thaliana wurden topologische und dynamische Eigenschaften auf der Basis der Reaktions- und Korrelations-Netzwerke verglichen. Die Korrelations-Netzwerke, welche auf der Ebene paarweiser Korrelationen einzelner experimentell ermittelter Substanzkonzentrationen erzeugt werden, charakterisieren den dynamischen Zustand des Metabolismus in einem Organismus für einen bestimmten Zeitpunkt. Für verschiedene Probenahmen in einem diurnalen Rhythmus konnte zeitlich konsistentes Verhalten über die höhere Ähnlichkeit zeitlich benachbarter Korrelations-Netzwerke nachgewiesen werden. Betrachtet man jedoch die Ähnlichkeit zwischen beiden Typen metabolischer Netzwerke, so ist die Nähe der Metaboliten im Reaktions-Netzwerk kein Indikator für die Nähe im Korrelations-Netzwerk. Hohe Korrelationen zwischen einzelnen Metabolitenpaaren können zu einem großen Teil nicht aus den Reaktionsfolgen im Reaktions-Netzwerk, sondern nur durch übergeordnete Funktionen, wie z.B. regulatorische Kontrolle, erklärt werden. Die Organisationsprinzipien in beiden Netzwerken unterscheiden sich daher grundlegend, da hier zwei dynamischen Skalen konkurrieren, die der Reaktion und die der Regulation