Cell communication networks in cancer invasion

The invasion of cancer is a major clinical problem. It is now apparent that invasion is not a simply a cancer cell autonomous process but relies on a complex network of paracrine interactions. Furthermore, this network can change as cancer cells disseminate. Here we summarise the key components of the network and their mechanisms of communication. Finally, we discuss the difficulties and opportunities that this complex network of interactions presents during cancer therapy.     

Electrical communication in branching arterial networks

Electrical communication and its role in blood flow regulation are built on an examination of charge movement in single, isolated vessels. How this process behaves in broader arterial networks remains unclear. This study examined the nature of electrical communication in arterial structures where vessel length and branching were varied. Analysis began with the deployment of an existing computational model expanded to form a variable range of vessel structures. Initial simulations revealed that focal endothelial stimulation generated electrical responses that conducted robustly along short unbranched vessels and to a lesser degree lengthened arteries or branching structures retaining a single branch point. These predictions matched functional observations from hamster mesenteric arteries and support the idea that an increased number of vascular cells attenuate conduction by augmenting electrical load. Expanding the virtual network to 31 branches revealed that electrical responses increasingly ascended from fifth- to first-order arteries when the number of stimulated distal vessels rose. This property enabled the vascular network to grade vasodilation and network perfusion as revealed through blood flow modeling. An elevation in endothelial-endothelial coupling resistance, akin to those in sepsis models, compromised this ascension of vasomotor/perfusion responses. A comparable change was not observed when the endothelium was focally disrupted to mimic disease states including atherosclerosis. In closing, this study highlights that vessel length and branching play a role in setting the conduction of electrical phenomenon along resistance arteries and within networks. It also emphasizes that modest changes in endothelial function can, under certain scenarios, impinge on network responsiveness and blood flow control.     

Correlated dynamics in egocentric communication networks

We investigate the communication sequences of millions of people through two different channels and analyse the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbours, thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constraints (for short messages) and partly to the human behavioural features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics.     

Performance analysis of virtual clusters in personal communication networks

The objective of the paper is to analyze the performance of virtual cluster architectures in wireless networks. The key issues in wireless domain are flooding, connectivity, and power management. These issues arise during the path finding and maintenance between source and destination nodes. To overcome these issues three approaches are introduced in this paper namely; fusion virtual structure, link quality connected dominating set and cluster backbone approach. These approaches follow the distributed localized computations for virtual cluster constructions and focus on fundamental connectivity problems and are partially involved in power saving process of individual nodes. The proposed methods are analyzed in terms of backbone size, packet delivery ratio and normalized routing overhead and the results are witnessed by simulation.     

Directedness of information flow in mobile phone communication networks

Without having direct access to the information that is being exchanged, traces of information flow can be obtained by looking at temporal sequences of user interactions. These sequences can be represented as causality trees whose statistics result from a complex interplay between the topology of the underlying (social) network and the time correlations among the communications. Here, we study causality trees in mobile-phone data, which can be represented as a dynamical directed network. This representation of the data reveals the existence of super-spreaders and super-receivers. We show that the tree statistics, respectively the information spreading process, are extremely sensitive to the in-out degree correlation exhibited by the users. We also learn that a given information, e.g., a rumor, would require users to retransmit it for more than 30 hours in order to cover a macroscopic fraction of the system. Our analysis indicates that topological node-node correlations of the underlying social network, while allowing the existence of information loops, they also promote information spreading. Temporal correlations, and therefore causality effects, are only visible as local phenomena and during short time scales. Consequently, the very idea that there is (intentional) information spreading beyond a small vecinity is called into question. These results are obtained through a combination of theory and data analysis techniques.     

Evolutionarily conserved networks of residues mediate allosteric communication in proteins

A fundamental goal in cellular signaling is to understand allosteric communication, the process by which signals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Here, we describe a sequence-based statistical method for quantitatively mapping the global network of amino acid interactions in a protein. Application of this method for three structurally and functionally distinct protein families (G protein-coupled receptors, the chymotrypsin class of serine proteases and hemoglobins) reveals a surprisingly simple architecture for amino acid interactions in each protein family: a small subset of residues forms physically connected networks that link distant functional sites in the tertiary structure. Although small in number, residues comprising the network show excellent correlation with the large body of mechanistic data available for each family. The data suggest that evolutionarily conserved sparse networks of amino acid interactions represent structural motifs for allosteric communication in proteins.     

Neurons set the tone of gap junctional communication in astrocytic networks

A number of studies have contributed to demonstrate that neurons and astrocytes tightly and actively interact. Indeed, the presence of astrocytes in neuronal cultures increases the number of synapses and their efficiency, and thanks to enzymatic and uptake processes, astrocytes play a role in neuroprotection. A typical feature of astrocytes is that they establish cell-cell communication in vitro, as well as in situ, through intercellular channels forming specialized membrane areas defined as gap junctions. These channels are composed of junctional proteins termed connexins (Cxs): in astrocytes connexin 43 (Cx43) and 30 (Cx30) have been shown to prevail. Several recent works indicate that gap junctional communication (GJC) and/or connexin expression in astrocytes are controlled by neurons. Altogether, these observations lead to the concept that neuronal and astrocytic networks interact through mutual setting of their respective mode of communication and that astrocyte gap junctions represent a target in neuroglial interaction.     

Effects of territorial intrusions on eavesdropping neighbors: communication networks in nightingales

Animal communication often occurs in communication networksin which multiple signalers and receivers are within signalingrange of each other. In such networks, individuals can obtaininformation on the quality and motivation of territorial neighborsby eavesdropping on their signaling interactions. In songbirds,extracting information from interactions involving neighborsis thought to be an important factor in the evolution of strategiesof territory defense. In a playback experiment with radio-taggednightingales Luscinia megarhynchos we here demonstrate thatterritorial males use their familiar neighbors' performancein a vocal interaction with an unfamiliar intruder as a standardfor their own response. Males were attracted by a vocal interactionbetween their neighbor and a simulated stranger and intrudedinto the neighbor's territory. The more intensely the neighborhad interacted with playback, the earlier the intrusions weremade, indicating that males eavesdropped on the vocal contestinvolving a neighbor. However, males never intruded when wehad simulated by a second playback that the intruder had retreatedand sang outside the neighbor's territory. These results suggestthat territorial males use their neighbors' singing behavioras an early warning system when territorial integrity is threatened.Simultaneous responses by neighboring males towards unfamiliarrivals are likely to be beneficial to the individuals in maintainingterritorial integrity.     

Social networks improve leaderless group navigation by facilitating long-distance communication

Group navigation is of great importance for many animals,such as migrating flocks of birds or shoals of fish.One theory states that group membership can improve navigational accuracy compared to limited or less accurate individual navigational ability in groups without leaders (Many-wrongs principle).Here,we simulate leaderless group navigation that includes social connectious as preferential interactions between individuals.Our results suggest that underlying social networks can reduce navigational errors of groups and increase group cohesion.We use network summary statistics,in particular network motifs,to study which characteristics of networks lead to these improvements.It is networks in which preferences between individuals are not clustered,but spread evenly across the group that are advantageous in group navigation by effectively enhancing long-distance information exchange within groups.We suggest that our work predicts a base-line for the type of social structure we might expect to find in group-living animals that navigate without leaders [Current Zoology 58 (2):329-341,2012].     

Interplant communication of tomato plants through underground common mycorrhizal networks

Plants can defend themselves to pathogen and herbivore attack by responding to chemical signals that are emitted by attacked plants. It is well established that such signals can be transferred through the air. In theory, plants can also communicate with each other through underground common mycorrhizal networks (CMNs) that interconnect roots of multiple plants. However, until now research focused on plant-to-plant carbon nutrient movement and there is no evidence that defense signals can be exchanged through such mycorrhizal hyphal networks. Here, we show that CMNs mediate plant-plant communication between healthy plants and pathogen-infected tomato plants (Lycopersicon esculentum Mill.). After establishment of CMNs with the arbuscular mycorrhizal fungus Glomus mosseae between tomato plants, inoculation of 'donor' plants with the pathogen Alternaria solani led to increases in disease resistance and activities of the putative defensive enzymes, peroxidase, polyphenol oxidase, chitinase, β-1,3-glucanase, phenylalanine ammonia-lyase and lipoxygenase in healthy neighbouring 'receiver' plants. The uninfected 'receiver' plants also activated six defence-related genes when CMNs connected 'donor' plants challenged with A. solani. This finding indicates that CMNs may function as a plant-plant underground communication conduit whereby disease resistance and induced defence signals can be transferred between the healthy and pathogen-infected neighbouring plants, suggesting that plants can 'eavesdrop' on defence signals from the pathogen-challenged neighbours through CMNs to activate defences before being attacked themselves.     

Toward an understanding of microbial communities through analysis of communication networks

Bacteria receive signals from diverse members of their biotic environment. They sense their own species through the process of quorum sensing, which detects the density of bacterial cells and regulates functions such as bioluminescence, virulence, and competence. Bacteria also respond to the presence of other microorganisms and eukaryotic hosts. Most studies of microbial communication focus on signaling between the microbe and one other organism for empirical simplicity and because few experimental systems offer the opportunity to study communication among various types of organisms. But in the real biological world, microorganisms must carry on multiple molecular conversations simultaneously between diverse organisms, thereby constructing communication networks. We propose that biocontrol of plant disease, the process of suppressing disease through application of a microorganism, offers a model for the study of communication among multiple organisms. Successful biocontrol requires the sending and receiving of signals between the biocontrol agent and the pathogen, plant host, and microbial community surrounding the host. We are using Bacillus cereus, a biocontrol agent, and the organisms it must interact with, to dissect a communication network. This system offers an excellent starting point for study because its members are defined and well studied. An understanding of signaling in the B. cereus biocontrol system may provide a model for network communication among organisms that share a habitat and provide a new angle of analysis for understanding the interconnections that define communities. This revised version was published online in August 2006 with corrections to the Cover Date.     

Harmonic analysis of Boolean networks: determinative power and perturbations

Consider a large Boolean network with a feed forward structure. Given a probability distribution on the inputs, can one find, possibly small, collections of input nodes that determine the states of most other nodes in the network? To answer this question, a notion that quantifies the determinative power of an input over the states of the nodes in the network is needed. We argue that the mutual information (MI) between a given subset of the inputs X={X₁,...,X_n} of some node i and its associated function f_i(X) quantifies the determinative power of this set of inputs over node i. We compare the determinative power of a set of inputs to the sensitivity to perturbations to these inputs, and find that, maybe surprisingly, an input that has large sensitivity to perturbations does not necessarily have large determinative power. However, for unate functions, which play an important role in genetic regulatory networks, we find a direct relation between MI and sensitivity to perturbations. As an application of our results, we analyze the large-scale regulatory network of Escherichia coli. We identify the most determinative nodes and show that a small subset of those reduces the overall uncertainty of the network state significantly. Furthermore, the network is found to be tolerant to perturbations of its inputs.     

Visualization, documentation, analysis, and communication of large-scale gene regulatory networks

Genetic regulatory networks (GRNs) are complex, large-scale, and spatially and temporally distributed. These characteristics impose challenging demands on software tools for building GRN models, and so there is a need for custom tools. In this paper, we report on our ongoing development of BioTapestry, an open source, freely available computational tool designed specifically for building GRN models. We also outline our future development plans, and give some examples of current applications of BioTapestry.     

Encoding certainty in bump attractors

Persistent activity in neuronal populations has been shown to represent the spatial position of remembered stimuli. Networks that support bump attractors are often used to model such persistent activity. Such models usually exhibit translational symmetry. Thus activity bumps are neutrally stable, and perturbations in position do not decay away. We extend previous work on bump attractors by constructing model networks capable of encoding the certainty or salience of a stimulus stored in memory. Such networks support bumps that are not only neutrally stable to perturbations in position, but also perturbations in amplitude. Possible bump solutions then lie on a two-dimensional attractor, determined by a continuum of positions and amplitudes. Such an attractor requires precisely balancing the strength of recurrent synaptic connections. The amplitude of activity bumps represents certainty, and is determined by the initial input to the system. Moreover, bumps with larger amplitudes are more robust to noise, and over time provide a more faithful representation of the stored stimulus. In networks with separate excitatory and inhibitory populations, generating bumps with a continuum of possible amplitudes, requires tuning the strength of inhibition to precisely cancel background excitation.