Assortative and modular networks are shaped by adaptive synchronization processes

Modular organization and degree-degree correlations are ubiquitous in the connectivity structure of biological, technological, and social interacting systems. So far most studies have concentrated on unveiling both features in real world networks, but a model that succeeds in generating them simultaneously is needed. We consider a network of interacting phase oscillators, and an adaptation mechanism for the coupling that promotes the connection strengths between those elements that are dynamically correlated. We show that, under these circumstances, the dynamical organization of the oscillators shapes the topology of the graph in such a way that modularity and assortativity features emerge spontaneously and simultaneously. In turn, we prove that such an emergent structure is associated with an asymptotic arrangement of the collective dynamical state of the network into cluster synchronization.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

AALUMO: a user model ontology for ambient assisted living services supported in next-generation networks

Ambient Assisted Living (AAL) services are emerging as context-awareness solutions to support elderly people?s autonomy. The context-aware paradigm makes applications more user-adaptive. In this way, context and user models expressed in ontologies are employed by applications to describe user and environment characteristics. The rapid advance of technology allows creating context server to relieve applications of context reasoning techniques. Specifically, the Next Generation Networks (NGN) provides by means of the presence service a framework to manage the current user's state as well as the user's profile information extracted from Internet and mobile context. This paper propose a user modeling ontology for AAL services which can be deployed in a NGN environment with the aim at adapting their functionalities to the elderly's context information and state.

Rule Representation in Distributed Environments with Accepting Networks of Splicing Processors.

This paper presents the model named Accepting Networks of Evolutionary Processors as NP-problem solver inspired in the biological DNA operations. A processor has a rules set, splicing rules in this model,an object multiset and a filters set. Rules can be applied in parallel since there exists a large number of copies of objects in the multiset. Processors can form a graph in order to solve a given problem. This paper shows the network configuration in order to solve the SAT problem using linear resources and time. A rule representation arquitecture in distributed environments can be easily implemented using these networks of processors, such as decision support systems, as shown in the paper.

Simulating Metabolic Processes Using an Architecture Based on Networks of Bio-inspired Processors.

In this work, we propose the Networks of Evolutionary Processors (NEP) [2] as a computational model to solve problems related with biological phenomena. In our first approximation, we simulate biological processes related with cellular signaling and their implications in the metabolism, by using an architecture based on NEP (NEP architecture) and their specializations: Networks of Polarized Evolutionary Processors (NPEP) [1] and NEP Transducers (NEPT) [3]. In particular, we use this architecture to simulate the interplay between cellular processes related with the metabolism as the Krebs cycle and the malate-aspartate shuttle pathway (MAS) both being altered by signaling by calcium.

Peptide design by artificial neural networks and computer-based evolutionary search

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a “seed peptide” with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-β1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human β1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.

Self-assembly of fibronectin into fibrillar networks underneath dipalmitoyl phosphatidylcholine monolayers: Role of lipid matrix and tensile forces

The cell-mediated assembly of fibronectin (Fn) into fibrillar matrices is a complex multistep process that is incompletely understood because of the chemical complexity of the extracellular matrix and a lack of experimental control over molecular interactions and dynamic events. We have identified conditions under which Fn assembles into extended fibrillar networks after adsorption to a dipalmitoyl phosphatidylcholine (DPPC) monolayer in contact with physiological buffer. We propose a sequential model for the Fn assembly pathway, which involves the orientation of Fn underneath the lipid monolayer by insertion into the liquid expanded (LE) phase of DPPC. Attractive interactions between these surface-anchored proteins and the liquid condensed (LC) domains leads to Fn enrichment at domain edges. Spontaneous self-assembly into fibrillar networks, however, occurs only after expansion of the DPPC monolayer from the LC phase though the LC/LE phase coexistence. Upon monolayer expansion, the domain boundaries move apart while attractive interactions among Fn molecules and between Fn and domain edges produce a tensile force on the proteins that initiates fibril assembly. The resulting fibrils have been characterized in situ by using fluorescence and light-scattering microscopy. We have found striking similarities between fibrils produced under DPPC monolayers and those found on cellular surfaces, including their assembly pathways.

Continuous and lurching traveling pulses in neuronal networks with delay and spatially decaying connectivity

Propagation of discharges in cortical and thalamic systems, which is used as a probe for examining network circuitry, is studied by constructing a one-dimensional model of integrate-and-fire neurons that are coupled by excitatory synapses with delay. Each neuron fires only one spike. The velocity and stability of propagating continuous pulses are calculated analytically. Above a certain critical value of the constant delay, these pulses lose stability. Instead, lurching pulses propagate with discontinuous and periodic spatio-temporal characteristics. The parameter regime for which lurching occurs is strongly affected by the footprint (connectivity) shape; bistability may occur with a square footprint shape but not with an exponential footprint shape. For strong synaptic coupling, the velocity of both continuous and lurching pulses increases logarithmically with the synaptic coupling strength gsyn for an exponential footprint shape, and it is bounded for a step footprint shape. We conclude that the differences in velocity and shape between the front of thalamic spindle waves in vitro and cortical paroxysmal discharges stem from their different effective delay; in thalamic networks, large effective delay between inhibitory neurons arises from their effective interaction via the excitatory cells which display postinhibitory rebound.

Functional reorganization in thalamocortical networks: Transition between spindling and delta sleep rhythms

Thalamic reticularis, thalamocortical, and cortical cells participate in the 7–14-hz spindling rhythm of early sleep and the slower delta rhythms of deeper sleep, with different firing patterns. In this case study, showing the interactions of intrinsic and synaptic properties, a change in the conductance of one kind of cell effectively rewires the thalamocortical circuit, leading to the transition from the spindling to the delta rhythm. The two rhythms make different uses of the fast (GABAA) and slow (GABAB) inhibition generated by the thalamic reticularis cells.

The interaction of Arp2/3 complex with actin: Nucleation, high affinity pointed end capping, and formation of branching networks of filaments

The Arp2/3 complex is a stable assembly of seven protein subunits including two actin-related proteins (Arp2 and Arp3) and five novel proteins. Previous work showed that this complex binds to the sides of actin filaments and is concentrated at the leading edges of motile cells. Here, we show that Arp2/3 complex purified from Acanthamoeba caps the pointed ends of actin filaments with high affinity. Arp2/3 complex inhibits both monomer addition and dissociation at the pointed ends of actin filaments with apparent nanomolar affinity and increases the critical concentration for polymerization at the pointed end from 0.6 to 1.0 μM. The high affinity of Arp2/3 complex for pointed ends and its abundance in amoebae suggest that in vivo all actin filament pointed ends are capped by Arp2/3 complex. Arp2/3 complex also nucleates formation of actin filaments that elongate only from their barbed ends. From kinetic analysis, the nucleation mechanism appears to involve stabilization of polymerization intermediates (probably actin dimers). In electron micrographs of quick-frozen, deep-etched samples, we see Arp2/3 bound to sides and pointed ends of actin filaments and examples of Arp2/3 complex attaching pointed ends of filaments to sides of other filaments. In these cases, the angle of attachment is a remarkably constant 70 ± 7°. From these in vitro biochemical properties, we propose a model for how Arp2/3 complex controls the assembly of a branching network of actin filaments at the leading edge of motile cells.

Simple neural networks for the amplification and utilization of small changes in neuron firing rates

I describe physiologically plausible “voter-coincidence” neural networks such that secondary “coincidence” neurons fire on the simultaneous receipt of sufficiently large sets of input pulses from primary sets of neurons. The networks operate such that the firing rate of the secondary, output neurons increases (or decreases) sharply when the mean firing rate of primary neurons increases (or decreases) to a much smaller degree. In certain sensory systems, signals that are generally smaller than the noise levels of individual primary detectors, are manifest in very small increases in the firing rates of sets of afferent neurons. For such systems, this kind of network can act to generate relatively large changes in the firing rate of secondary “coincidence” neurons. These differential amplification systems can be cascaded to generate sharp, “yes–no” spike signals that can direct behavioral responses.

Visualization of biochemical networks in living cells

Functional annotation of novel genes can be achieved by detection of interactions of their encoded proteins with known proteins followed by assays to validate that the gene participates in a specific cellular function. We report an experimental strategy that allows for detection of protein interactions and functional assays with a single reporter system. Interactions among biochemical network component proteins are detected and probed with stimulators and inhibitors of the network. In addition, the cellular location of the interacting proteins is determined. We used this strategy to map a signal transduction network that controls initiation of translation in eukaryotes. We analyzed 35 different pairs of full-length proteins and identified 14 interactions, of which five have not been observed previously, suggesting that the organization of the pathway is more ramified and integrated than previously shown. Our results demonstrate the feasibility of using this strategy in efforts of genomewide functional annotation.

The generation of oscillations in networks of electrically coupled cells

In several biological systems, the electrical coupling of nonoscillating cells generates synchronized membrane potential oscillations. Because the isolated cell is nonoscillating and electrical coupling tends to equalize the membrane potentials of the coupled cells, the mechanism underlying these oscillations is unclear. Here we present a dynamic mechanism by which the electrical coupling of identical nonoscillating cells can generate synchronous membrane potential oscillations. We demonstrate this mechanism by constructing a biologically feasible model of electrically coupled cells, characterized by an excitable membrane and calcium dynamics. We show that strong electrical coupling in this network generates multiple oscillatory states with different spatio-temporal patterns and discuss their possible role in the cooperative computations performed by the system.

Intrinsic noise in gene regulatory networks

Cells are intrinsically noisy biochemical reactors: low reactant numbers can lead to significant statistical fluctuations in molecule numbers and reaction rates. Here we use an analytic model to investigate the emergent noise properties of genetic systems. We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. The noise strength immediately following single gene induction is almost twice the final steady-state value. We find that fluctuations in the concentrations of a regulatory protein can propagate through a genetic cascade; translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. For an autoregulatory protein, we demonstrate that negative feedback efficiently decreases system noise. The model can be used to predict the noise characteristics of networks of arbitrary connectivity. The general procedure is further illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin.

Coverage and invariance for the biological control of pests in mediterranean greenhouses

A major problem related to the treatment of ecosystems is that they have no available mathematical formalization. This implies that many of their properties are not presented as short, rigorous modalities, but rather as long expressions which, from a biological standpoint, totally capture the significance of the property, but which have the disadvantage of not being sufficiently manageable, from a mathematical standpoint. The interpretation of ecosystems through networks allows us to employ the concepts of coverage and invariance alongside other related concepts. The latter will allow us to present the two most important relations in an ecosystem – predator–prey and competition – in a different way. Biological control, defined as “the use of living organisms, their resources or their products to prevent or reduce loss or damage caused by pests”, is now considered the environmentally safest and most economically advantageous method of pest control (van Lenteren, 2011). A guild includes all those organisms that share a common food resource (Polis et al., 1989), which in the context of biological control means all the natural enemies of a given pest. There are several types of intraguild interactions, but the one that has received most research attention is intraguild predation, which occurs when two organisms share the same prey while at the same time participating in some kind of trophic interaction. However, this is not the only intraguild relationship possible, and studies are now being conducted on others, such as oviposition deterrence. In this article, we apply the developed concepts of structural functions, coverage, invariant sets, etc. (Lloret et al., 1998, Esteve and Lloret, 2006a, Esteve and Lloret, 2006b and Esteve and Lloret, 2007) to a tritrophic system that includes aphids, one of the most damaging pests and a current bottleneck for the success of biological control in Mediterranean greenhouses.