Plant life history stage and nurse age change the development of ecological networks in an arid ecosystem

Understanding how ecological networks are organised over the course of an organism's lifetime is crucial for predicting the dynamics of interacting populations and communities across temporal scales. However, most studies so far considered only one life history stage at a time, such as adult, when studying networks of interacting species. Therefore, knowledge about how multiple life history stages affect the development and stability of plant–plant association networks is lacking. We measured the understory adult plant community and the soil seed bank across a plant age gradient of the nurse shrub Retama sphaerocarpa in an arid ecosystem in Spain. Using a multilayer network approach, we built adult understory–nurse and seed bank–nurse networks and analysed how network nestedness, species’ role, and species specificity varied between them and with nurse plant age. We found that seed bank and adult understory networks changed depending on nurse plant age in two different ways. With increasing nurse plant age, adult understory networks became significantly more nested than seed bank networks. The nested architecture of seed bank networks was therefore a poor predictor of adult understory network nestedness. The contribution and specificity of species to network nestedness increased with increasing nurse plant age more in the adult understory than in seed bank networks, despite high species turnover. Our data show that life history and ontogeny affect the development of plant–plant association networks. Niche construction and environmental filtering along nurse ontogeny seem pivotal mechanisms structuring adult understory networks while the assembly of seed bank networks seems rather stochastic. We highlight the importance of mature plant communities for maintaining rare species populations and supporting the stability of ecological communities through time.     

Maturation of rhythmic neural network: role of central modulatory inputs.

Modulatory systems are well known for their roles in tuning the cellular and synaptic properties in the adult neuronal networks, and play a major role in the control of the flexibility of functional outputs. However far less is known concerning their role in the maturation of neural networks during the development. In this review, using the stomatogastric nervous system of lobster, we will show that the neuromodulatory system exerts a powerful influence on developing neural networks. In the adult the number of both motor target neurons and their modulatory neurons is restricted to tens of identifiable cells. They are therefore well characterized in terms of cellular, synaptic and morphological properties. In the embryo, these target cells and their neuromodulatory population are already present from mid-embryonic life. However, the motor output generated by the system is quite different: while in the embryo all the target neurons are organized into a single network generating unique motor pattern, in the adult this population splits into two distinct networks generating separate patterns. This ontogenetic partitioning does not rely on progressive acquisition of adult properties but rather on a switch between two possible network operations. Indeed, adult networks are present early in the embryonic life but their expression is repressed by central modulatory neurons. Moreover, embryonic networks can be revealed in the adult system again by altering modulatory influences. Therefore, independently of the developmental age, two potential network phenotypes co-exist within the same neuronal architecture: when one is expressed, the other one is hidden and vice versa. These transitions do not necessarily need dramatic changes such as growth/retraction of processes, acquisition of new intra-membrane proteins etc. but rather, as shown by modelling studies, it may simply rely on a subtle tuning of pre-existing intercellular electrical coupling. This in turn suggests that progressive ontogenetic alteration may not take place at the level of the target network but rather at the level of modulatory input neurons.     

Interactions between overlapping multimale groups of black and white colobus monkeys (Colobus guereza) in the Kakamega Forest,Kenya

Sixteen out of 18 groups of black and white colobus monkeys, or guerezas (Colobus guereza), observed in the Kakamega Forest, Kenya, included more than one adult male. Each group overlapped in its home range with 4–7 other groups, and no group appeared to have exclusive access to any part of its home range. Groups were engaged in intergroup encounters one-quarter of observation time. Encounters often occurred around preferred feeding sites as different groups were attracted to such sites in their overlapping home ranges. Wins and losses during encounters were not dependent on location and a null model suggested that groups were not increasing their encounter rate to defend boundaries, further demonstrating a lack of territoriality. Although guerezas have repeatedly been described as a territorial species living in one-male groups, nonterritorial multimale groups are common in continuous forests. Territorial one-male groups may be usual in narrow riparian forests where resources are limiting, groups are small, and defendability is high, but such organization cannot be regarded as typical of the species. Habitat affects group size, as well as the potential for territoriality, and group size determines the number of adult males per group; therefore, classifying groups as one-male or multimale appears to be an artificial dichotomy. © 1996 Wiley-Liss, Inc.     

Towards zoomable multidimensional maps of the cell

The detailed structure of molecular networks, including their dependence on conditions and time, are now routinely assayed by various experimental techniques. Visualization is a vital aid in integrating and interpreting such data. We describe emerging approaches for representing and visualizing systems data and for achieving semantic zooming, or changes in information density concordant with scale. A central challenge is to move beyond the display of a static network to visualizations of networks as a function of time, space and cell state, which capture the adaptability of the cell. We consider approaches for representing the role of protein complexes in the cell cycle, displaying modules of metabolism in a hierarchical format, integrating experimental interaction data with structured vocabularies such as Gene Ontology categories and representing conserved interactions among orthologous groups of genes.     

Engineering blood vessels from stem cells: recent advances and applications

Endothelial cells organized into blood vessels are critical for the formation and maintenance of most tissues in the body and are involved in regulating physiological processes such as angiogenesis, inflammation and thrombosis. Endothelial cells are of great research interest, because of their potential to treat vascular diseases and to stimulate the growth of ischaemic tissue. They can be used to engineer artificial vessels, repair damaged vessels, and to induce the formation of vessel networks in engineered tissues. For such clinical applications, proliferating human endothelial progenitor cells can be isolated from adult tissues or embryonic stem cells. Recently, these cells were successfully used to engineer single vessels and to stimulate capillary networks, both in vitro and in vivo.     

Alteration of viscoelastic properties is associated with a change in cytoskeleton components of ageing chondrocytes from rabbit knee articular cartilage

The cytoskeleton network is believed to play an important role in the biomechanical properties of the chondrocyte. Ours and other laboratories have demonstrated that chondrocytes exhibit a viscoelastic solid creep behavior in vitro and that viscoelastic properties decrease in osteoarthritic chondrocytes. In this study, we aimed to understand whether the alteration of viscoelastic properties is associated with changes in cytoskeleton components of ageing chondrocytes from rabbit knee articular cartilage. Three age groups were used for this study: young (2-months-old, N=23), adult (8-months-old, N=23), and old (31-months-old, N=23) rabbit groups. Cartilage structure and proteoglycan and type II collagen content were determined by H&E and Toluidine Blue staining, and type II collagen antibody. The detailed structure of the chondrocytes in all groups was visualized using transmission electron microscopy (TEM). Chondrocytes were isolated from full-thickness knee cartilage of rabbits from all groups and their viscoelastic properties were quantified within 2 hours of isolation using a micropipette aspiration technique combined with a standard linear viscoelastic solid model. The components and network of the cytoskeleton within the cells were analyzed by laser scanning confocal microscopy (LSCM) with immunofluorescence staining as well as real time PCR and western blotting. With ageing, articular cartilage contained less chondrocytes and less proteoglycans and type II collagen. TEM observations showed that the cell membranes were not clearly defined, organelles were fewer and the nuclei were deformed or shrunk in the old cells compared with the young and adult cells. In suspension, chondrocytes from all three age groups showed significant viscoelastic creep behavior, but the deformation rate and amplitude of old chondrocytes were increased under the same negative pressure when compared to young and adult chondrocytes. Viscoelastic properties of the old cells, including equilibrium modulus (E infinity), instantaneous modulus (E0) and apparent viscosity (mu) were significantly lower than that those of the young and adult ones (P < 0.001). No significant differences were detected between young and adult chondrocytes (P > 0.05). Moreover, we found that the cytoskeletal networks of old cells were sparser, and that the contents of the various components of the intracellular networks were reduced in old cells, compared with adult and young cells. Aged chondrocytes had a different response to mechanical stimulation when compared to young and adult chondrocytes due to alteration of their viscoelastic properties, which was in turn associated with changes in cell structure and cytoskeleton composition.     

Time Development in the Early History of Social Networks: Link Stabilization,Group Dynamics,and Segregation

Studies of the time development of empirical networks usually investigate late stages where lasting connections have already stabilized. Empirical data on early network history are rare but needed for a better understanding of how social network topology develops in real life. Studying students who are beginning their studies at a university with no or few prior connections to each other offers a unique opportunity to investigate the formation and early development of link patterns and community structure in social networks. During a nine week introductory physics course, first year physics students were asked to identify those with whom they communicated about problem solving in physics during the preceding week. We use these students'' self reports to produce time dependent student interaction networks. We investigate these networks to elucidate possible effects of different student attributes in early network formation. Changes in the weekly number of links show that while roughly half of all links change from week to week, students also reestablish a growing number of links as they progress through their first weeks of study. Using the Infomap community detection algorithm, we show that the networks exhibit community structure, and we use non-network student attributes, such as gender and end-of-course grade to characterize communities during their formation. Specifically, we develop a segregation measure and show that students structure themselves according to gender and pre-organized sections (in which students engage in problem solving and laboratory work), but not according to end-of-coure grade. Alluvial diagrams of consecutive weeks'' communities show that while student movement between groups are erratic in the beginnning of their studies, they stabilize somewhat towards the end of the course. Taken together, the analyses imply that student interaction networks stabilize quickly and that students establish collaborations based on who is immediately available to them and on observable personal characteristics.     

Unraveling spurious properties of interaction networks with tailored random networks

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erd?s-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.     

Network quantitative trait loci mapping of circadian clock outputs identifies metabolic pathway-to-clock linkages in Arabidopsis

Modern systems biology permits the study of complex networks, such as circadian clocks, and the use of complex methodologies, such as quantitative genetics. However, it is difficult to combine these approaches due to factorial expansion in experiments when networks are examined using complex methods. We developed a genomic quantitative genetic approach to overcome this problem, allowing us to examine the function(s) of the plant circadian clock in different populations derived from natural accessions. Using existing microarray data, we defined 24 circadian time phase groups (i.e., groups of genes with peak phases of expression at particular times of day). These groups were used to examine natural variation in circadian clock function using existing single time point microarray experiments from a recombinant inbred line population. We identified naturally variable loci that altered circadian clock outputs and linked these circadian quantitative trait loci to preexisting metabolomics quantitative trait loci, thereby identifying possible links between clock function and metabolism. Using single-gene isogenic lines, we found that circadian clock output was altered by natural variation in Arabidopsis thaliana secondary metabolism. Specifically, genetic manipulation of a secondary metabolic enzyme led to altered free-running rhythms. This represents a unique and valuable approach to the study of complex networks using quantitative genetics.     

Group unity of chimpanzees elucidated by comparison of sex differences in short-range interactions in Mahale Mountains National Park,Tanzania

Chimpanzees (Pan troglodytes) form multi-male and multi-female unit groups with fission–fusion grouping patterns. Short-range interaction (SRI) plays an important role in the unity of these groups and in maintaining social bonds among members. This study evaluated three models of chimpanzee social structure that differed according to the emphasis each placed on social bonds between the sexes, i.e., the male-only, the bisexual, and the male-bonded unit-group model. I investigated differences in SRI between the sexes among group members in well-habituated wild chimpanzees in Mahale Mountains National Park, Tanzania. I followed six focal adult males and six females, and quantified their respective SRI with other chimpanzees. Except between subordinate males and adult females, adults in general engaged in SRI with about 60–90% of the individuals with whom they made visual contact each day, whether in large or small parties. Although the number of social grooming (SGR) partners was limited, male–male SGR networks were wider than were either male–female or female–female SGR networks among adults. The number of contact-seeking behavior (CSB) partners was also limited, but dominant males had more CSB partners. Adult females mainly interacted by pant-grunt greeting (PGG) with adult males, but tended to do so mainly with the highest-ranking male(s) within visual contact. These results indicated that the social bonds among adult males were essential to group unity. Because of clear male dominance, adult females established peaceful coexistence with all group members despite less frequent SRI with subordinate males by maintaining affiliative social bonds with dominant males, thereby supporting the male-bonded unit-group model. Adult females had many female SRI partners, but these interactions did not involve performing conspicuous behaviors, suggesting that females maintain social bonds with other females in ways that differ from how such bonds are maintained with and between adult males.     

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation

Background

Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models’ expressiveness.

Results

Continuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights.

Conclusions

Continuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.     

Response to novel housing in two groups of captive tufted capuchin monkeys (Cebus apella)

The influence of age, maternal status, and the presence of a group male on use of space was assessed in two groups of captive tufted capuchin monkeys that underwent a move from indoor housing to a larger outdoor facility. Both groups originally contained two adult males, but only one group retained a male after the move. Following the move, mothers spent less time on the ground when carrying their infants than they did when not carrying their infants. In the group with no male (1) individuals decreased time spent on the ground relative to pre-move levels, whereas no such difference was noted in the group with the male; (2) females spent more time carrying their infants than did females in the group with a male. In the group with the adult male, juveniles spent less time on the ground than did non-mother adult females, whereas no difference had existed prior to the move. Grooming rates dropped from pre-move to post-move, but the mean number of partners with which each animal was in contact increased. Measures of social behavior varied across post-move observation periods inversely to time spent on the ground. These results are consistent with the view that an individual's relative vulnerability influences behavioral conservatism in novel environments, and suggests a relatively profound role for males in promoting exploration of new space in this species.     

Phylogenetic,ontogenetic and adult adaptive plasticity of rhythmic neural networks: a common neuromodulatory mechanism?

Neuromodulatory inputs are known to play a major role in the adaptive plasticity of rhythmic neural networks in adult animals. Using the crustacean stomatogastric nervous system, we have investigated the role of modulatory inputs in the development of rhythmic neural networks. We found that the same neuronal population is organised into a single network in the embryo, as opposed to the two networks present in the adult. However, these adult networks pre-exist in the embryo and can be unmasked by specific alterations of the neuromodulatory environment. Similarly, adult networks may switch back to the embryonic phenotype by manipulating neuromodulatory inputs. During development, we found that the early established neuromodulatory population display alteration in expressed neurotransmitter phenotypes, and that although the population of modulatory neurones is established early, with morphology and projection pattern similar to adult ones, their neurotransmitter phenotype may appear gradually. Therefore the abrupt switch from embryonic to adult network expression occurring at metamorphosis may be due to network reconfiguration in response to changes in modulatory input, as found in adult adaptive plasticity. Strikingly, related crustacean species express different motor outputs using the same basic network circuitry, due to species-specific alteration in neuromodulatory substances within homologous projecting neurones. Therefore we propose that alterations within neuromodulatory systems to a given rhythmic neural network displaying the same basic circuitry may account for the generation of different motor outputs throughout development (ontogenetic plasticity), adulthood (adaptive plasticity) and evolution (phylogenetic plasticity).Abbreviations CoG Commissural ganglion - OG Oesophageal ganglion - STG Stomatogastric ganglion - STNS Stomatogastric nervous system     

Social features of online networks: the strength of intermediary ties in online social media

An increasing fraction of today's social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people's coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties); events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties).     

Stoichiometric design of metabolic networks: multifunctionality,clusters, optimization,weak and strong robustness

Starting from a limited set of reactions describing changes in the carbon skeleton of biochemical compounds complete sets of metabolic networks are constructed. The networks are characterized by the number and types of participating reactions. Elementary networks are defined by the condition that a specific chemical conversion can be performed by a set of given reactions and that this ability will be lost by elimination of any of these reactions. Groups of networks are identified with respect to their ability to perform a certain number of metabolic conversions in an elementary way which are called the network’s functions. The number of the network functions defines the degree of multifunctionality. Transitions between networks and mutations of networks are defined by exchanges of single reactions. Different mutations exist such as gain or loss of function mutations and neutral mutations. Based on these mutations neighbourhood relations between networks are established which are described in a graph theoretical way. Basic properties of these graphs are determined such as diameter, connectedness, distance distribution of pairs of vertices. A concept is developed to quantify the robustness of networks against changes in their stoichiometry where we distinguish between strong and weak robustness. Evolutionary algorithms are applied to study the development of network populations under constant and time dependent environmental conditions. It is shown that the populations evolve toward clusters of networks performing a common function and which are closely neighboured. Under changing environmental conditions multifunctional networks prove to be optimal and will be selected.     

Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data

Reconstructing biological networks, such as metabolic and signaling networks, is at the heart of systems biology. Although many approaches exist for reconstructing network structure, few approaches recover the full dynamic behavior of a network. We survey such approaches that originate from computational scientific discovery, a subfield of machine learning. These take as input measured time course data, as well as existing domain knowledge, such as partial knowledge of the network structure. We demonstrate the use of these approaches on illustrative tasks of finding the complete dynamics of biological networks, which include examples of rediscovering known networks and their dynamics, as well as examples of proposing models for unknown networks.