Respiratory circuits: development, function and models

Breathing is a rhythmic motor behavior generated and controlled by hindbrain neuronal networks. Respiratory motor output arises from two distinct, but functionally interacting, rhythmogenic networks: the pre-B?tzinger complex (preB?tC) and the retrotrapezo?d nucleus/parafacial respiratory group (RTN/pFRG). This review outlines recent advances in delineating the genetic specification of the neuronal constituents of these two rhythmogenic networks, their respective roles in respiratory function and how they interact to constitute a functional respiratory circuit ensemble. The often lethal consequences of disruption to these networks found in naturally occurring developmental disorders, transgenic animals, and highly specific lesion studies are described. In addition, we discuss how recent computational models enhance our understanding of how respiratory networks generate and regulate respiratory behavior.     

Kernel-Kohonen networks

We investigate the combination of the Kohonen networks with the kernel methods in the context of classification. We use the idea of kernel functions to handle products of vectors of arbitrary dimension. We indicate how to build Kohonen networks with robust classification performance by transformation of the original data vectors into a possibly infinite dimensional space. The resulting Kohonen networks preserve a non-Euclidean neighborhood structure of the input space that fits the properties of the data. We show how to optimize the transformation of the data vectors in order to obtain higher classification performance. We compare the kernel-Kohonen networks with the regular Kohonen networks in the context of a classification task.     

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.     

Molecular switches in animal cells

Molecular switches are the fundamental building blocks in the field of synthetic biology. The majority of these switches is based on protein-protein, protein-DNA or protein-RNA interactions that are responsive towards endogenous metabolites or external stimuli like small molecules or light. By the rational and predictive reassembling of multiple compatible molecular switches, complex synthetic signaling networks can be engineered. Here we review how these switches were used for the regulation of important cellular processes at every level of the signaling cascade. In the second part we review how these switches can be assembled to open- and closed-loop control signaling networks and how these networks can be applied to facilitate cattle reproduction, to treat diabetes or to autonomously detect and cure disease states like gouty arthritis or cancer.     

Interactome networks and human disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.     

Grazing and the vanishing complexity of plant association networks in grasslands

Interactions are key drivers of the functioning and fate of plant communities. A traditional way to measure them is to use pairwise experiments, but such experiments do not scale up to species-rich communities. For those, using association networks based on spatial patterns may provide a more realistic approach. While this method has been successful in abiotically-stressed environments (alpine and arid ecosystems), it is unclear how well it generalizes to other types of environments. We help fill this knowledge gap by documenting how the structure of plant communities changes in a Mediterranean dry grassland grazed by sheep using plant spatial association networks. We investigated how the structure of these networks changed with grazing intensity to show the effect of biotic disturbance on community structure. We found that these grazed grassland communities were mostly dominated by negative associations, suggesting a dominance of interference over facilitation regardless of the disturbance level. The topology of the networks revealed that the number of associations were not evenly-distributed across species, but rather that a small subset of species established most negative associations under low grazing conditions. All these aspects of spatial organization vanished under high level of grazing as association networks became more similar to null expectations. Our study shows that grazed herbaceous plant communities display a highly non-random organization that responds strongly to disturbance and can be measured through association networks. This approach thus appears insightful to test general hypotheses about plant communities, and in particular understand how anthropogenic perturbations affect the organization of ecological communities.     

Mapping regulatory networks in microbial cells.

Genome sequences are the blueprints of diverse life forms but they reveal little information about how cells make coherent responses to environmental changes. The combined use of gene fusions, gene chips, 2-D polyacrylamide gel electrophoresis, mass spectrometry and 'old-fashioned' microbial physiology will provide the means to reveal a cell's regulatory networks and how those networks are integrated.     

Functional alignment of regulatory networks: a study of temperate phages

The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage λ and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.     

Dynamics of Posttranslational Modification Systems: Recent Progress and Future Directions

Posttranslational modification of proteins is important for signal transduction, and hence significant effort has gone toward understanding how posttranslational modification networks process information. This involves, on the theory side, analyzing the dynamical systems arising from such networks. Which networks are, for instance, bistable? Which networks admit sustained oscillations? Which parameter values enable such behaviors? In this Biophysical Perspective, we highlight recent progress in this area and point out some important future directions. Along the way, we summarize several techniques for analyzing general networks, such as eliminating variables to obtain steady-state parameterizations, and harnessing results on how incorporating intermediates affects dynamics.     

Application of phylogenetic networks in evolutionary studies

The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.     

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.     

The nervous system might 'orthogonalize' to discriminate

It is still unclear how information is actually stored in biological neural networks. We propose here that information could be first orthogonalized and then stored. This could happen in a manner similar to how a set of vectors is transformed into a set of orthogonalized (i.e. mutually perpendicular) vectors. Orthogonalization may overcome the limits of conventional artificial networks, particularly the catastrophic interference caused by interference between stored inputs. The features needed to allow orthogonalization are common to biological networks, suggesting that it may be a common network mechanism. To illustrate this hypothesis, we characterize the underlying features that an archetypal biological network must have in order to perform orthogonalization, and point out that a number of actual networks show this archetypal network organization.     

Learning Universal Computations with Spikes

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.     

New moves in motor control

Motor behaviour results from information processing across multiple neural networks acting at all levels from initial selection of the behaviour to its final generation. Understanding how motor behaviour is produced requires identifying the constituent neurons of these networks, their cellular properties, and their pattern of synaptic connectivity. Neural networks have been traditionally studied with neurophysiological and neuroanatomical approaches. These approaches have been highly successful in particularly suitable 'model' preparations, typically ones in which the numbers of neurons in the networks were relatively small, neural network composition was unvarying across individual animals, and the preparations continued to produce fictive motor patterns in?vitro. However, analysing networks without these characteristics, and analysing the complete ensemble of networks that cooperatively generate behaviours, is difficult with these approaches. Recently developed molecular and neurogenetic tools provide additional avenues for analysing motor networks by allowing individual or groups of neurons within networks to be manipulated in novel ways and allowing experiments to be performed not only in?vitro but also in?vivo. We review here some of the new insights into motor network function that these advances have provided and indicate how these advances might bridge gaps in our understanding of motor control. To these ends, we first review motor neural network organisation highlighting cross-phylum principles. We then use prominent examples from the field to show how neurogenetic approaches can complement classical physiological studies, and identify additional areas where these approaches could be advantageously applied.     

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural 'knock-outs' in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.     

Balanced networks under spike-time dependent plasticity

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.     

Influence of network topology on mechanical properties of network polymers

We studied theoretically how the network topology influences the mechanical properties of polymers. We used conclusions of thermofluctuation theory of fracture and graph theory. The long-term strengths of monofunctional and polyfunctional networks were compared. The cross-link functionality distribution of the polyfunctional networks is a power function. All other conditions being equal, the long-term strengths of the polyfunctional polymer networks are some three to four times the long-term strengths of the monofunctional networks.