Gene Duplication and the Properties of Biological Networks

Patterns of network connection of members of multigene families were examined for two biological networks: a genetic network from the yeast Saccharomyces cerevisiae and a protein–protein interaction network from Caenorhabditis elegans. In both networks, genes belonging to gene families represented by a single member in the genome (“singletons”) were disproportionately represented among the nodes having large numbers of connections. Of 68 single-member yeast families with 25 or more network connections, 28 (44.4%) were located in duplicated genomic segments believed to have originated from an ancient polyploidization event; thus, each of these 28 loci was thus presumably duplicated along with the genomic segment to which it belongs, but one of the two duplicates has subsequently been deleted. Nodes connected to major “hubs” with a large number of connections, tended to be relatively sparsely interconnected among themselves. Furthermore, duplicated genes, even those arising from recent duplication, rarely shared many network connections, suggesting that network connections are remarkably labile over evolutionary time. These factors serve to explain well-known general properties of biological networks, including their scale-free and modular nature. [Reviewing Editor : Dr. Manyuan Long]     

Synchrony and Asynchrony for Neuronal Dynamics Defined on Complex Networks

We describe and analyze a model for a stochastic pulse-coupled neuronal network with many sources of randomness: random external input, potential synaptic failure, and random connectivity topologies. We show that different classes of network topologies give rise to qualitatively different types of synchrony: uniform (Erdős–Rényi) and “small-world” networks give rise to synchronization phenomena similar to that in “all-to-all” networks (in which there is a sharp onset of synchrony as coupling is increased); in contrast, in “scale-free” networks the dependence of synchrony on coupling strength is smoother. Moreover, we show that in the uniform and small-world cases, the fine details of the network are not important in determining the synchronization properties; this depends only on the mean connectivity. In contrast, for scale-free networks, the dynamics are significantly affected by the fine details of the network; in particular, they are significantly affected by the local neighborhoods of the “hubs” in the network.     

Unreasonable implications of reasonable idiotypic network assumptions

We first analyse a simple symmetric model of the idiotypic network. In the model idiotypic interactions regulate B cell proliferation. Three non-idiotypic processes are incorporated: (1) influx of newborn cells; (2) turnover of cells: (3) antigen. Antigen also regulates proliferation. A model of 2 B cell populations has 3 stable equilibria: one virgin, two immune. The twodimensional system thus remembers antigens, i.e. accounts for immunity. By contrast, if an idiotypic clone proliferates (in response to antigen), its anti-idiotypic partner is unable to control this. Symmetric idiotypic networks thus fail to account for proliferation regulation. In high-D networks we run into two problems. Firstly, if the network accounts for memory, idiotypic activation always propagates very deeply into the network. This is very unrealistic, but is an implication of the “realistic” assumption that it should be easier to activate all cells of a small virgin clone than to maintain the activation of all cells of a large (immune) clone. Secondly, graph theory teaches us that if the (random) network connectance exceeds a threshold level of one interaction per clone, most clones are interconnected. We show that this theory is also applicable to immune networks based on complementary matching idiotypes. The combination of the first “percolation” result with the “interconnectancr” result means that the first stimulation of the network with antigen should eventually affect most of the clones. We think this is unreasonable. Another threshold property of the network connectivity is the existence of a virgin state. A gradual increase in network connectance eliminates the virgin state and thus causes an abrupt change in network behaviour. In contrast to weakly connected systems, highly connected networks display autonomous activity and are unresponsive to external antigens. Similar differences between neonatal and adult networks have been described by experimentalists. The robustness of these results is tested with a network in which idiotypic inactivation of a clone occurs more generally than activation. Such “long-range inhibition” is known to promote pattern formation. However, in our model it fails to reduce the percolation, and additionally, generates semi-chaotic behaviour. In our network, the inhibition of a clone that is inhibiting can alter this clone into a clone that is activating. Hence “long-range inhibition” implies “long-range activation”, and idiotypic activation fails to remain localized. We next complicate this model by incorporating antibody production. Although this “antibody” model statically accounts for the same set of equilibrium points, it dynamically fails to account for state switching (i.e. memory). The switching behaviour is disturbed by the autonomous slow decay of the (long-lived) antibodies. After antigenic triggering the system now performs complex cyclic behaviour. Finally, it is suggested that (idiotypic) formation of antibody complexes can play only a secondary role in the network. In conclusion, our results cast doubt on the functional role of a profound idiotypic network. The network fails to account for proliferation regulation, and if it accounts for memory phenomena, it “explodes” upon the first encounter with antigen due to extensive percolation.     

Neural field model of binocular rivalry waves

We present a neural field model of binocular rivalry waves in visual cortex. For each eye we consider a one-dimensional network of neurons that respond maximally to a particular feature of the corresponding image such as the orientation of a grating stimulus. Recurrent connections within each one-dimensional network are assumed to be excitatory, whereas connections between the two networks are inhibitory (cross-inhibition). Slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We derive an analytical expression for the speed of a binocular rivalry wave as a function of various neurophysiological parameters, and show how properties of the wave are consistent with the wave-like propagation of perceptual dominance observed in recent psychophysical experiments. In addition to providing an analytical framework for studying binocular rivalry waves, we show how neural field methods provide insights into the mechanisms underlying the generation of the waves. In particular, we highlight the important role of slow adaptation in providing a “symmetry breaking mechanism” that allows waves to propagate.     

Gene sharing and genome evolution: networks in trees and trees in networks

Frequent lateral genetic transfer undermines the existence of a unique “tree of life” that relates all organisms. Vertical inheritance is nonetheless of vital interest in the study of microbial evolution, and knowing the “tree of cells” can yield insights into ecological continuity, the rates of change of different cellular characters, and the evolutionary plasticity of genomes. Notwithstanding within-species recombination, the relationships most frequently recovered from genomic data at shallow to moderate taxonomic depths are likely to reflect cellular inheritance. At the same time, it is clear that several types of ‘average signals’ from whole genomes can be highly misleading, and the existence of a central tendency must not be taken as prima facie evidence of vertical descent. Phylogenetic networks offer an attractive solution, since they can be formulated in ways that mitigate the misleading aspects of hybrid evolutionary signals in genomes. But the connections in a network typically show genetic relatedness without distinguishing between vertical and lateral inheritance of genetic material. The solution may lie in a compromise between strict tree-thinking and network paradigms: build a phylogenetic network, but identify the set of connections in the network that are potentially due to vertical descent. Even if a single tree cannot be unambiguously identified, choosing a subnetwork of putative vertical connections can still lead to drastic reductions in the set of candidate vertical hypotheses.     

Dynamic analysis,with feedback characterization,of hybrid human musculoskeletal frameworks by the linegraph-flowgraph procedure

The dynamic response of human musculo-skeletal framework is treated by (i) idealization of the musculo-skeletal framework as hybrid structural networks possessing feedback characteristics and then (ii) employing linegraph-flowgraph procedures for the feedback characterization of the hybrid structural networks. Topological procedures are used in which a “tree” of a network furnishes the skeleton upon which the “linkage” (muscle representing) members provide interaction. Feedback characterization (representing the sensitivity of the skeletal members to the tensile forces) is defined, between the internal “linkage” and “tree” members, by means of the flowgraph. Mikusinski operational calculus is used to facilitate representation of inertia effects by dynamic feedback characterization, with inclusion of initial conditions.     

Rare and emerging agents of hyalohyphomycosis

The list of opportunistic agents of hyalohyphomycosis continues to grow, as does the number of immunocompromised patients; however, these mycoses also are manifested in patients who appear immunocompetent by current methods of detection. Although fungi causing hyalohyphomycosis span the Kingdom Fungi, the majority are found within the asexual or mitosporic fungi, with notable exceptions. The following is a discussion of recent, selected literature citations regarding hyaline fungi that are both “new” agents of disease, as well as “new” species within well-known taxonomic groups.     

Self-organized dynamics in plastic neural networks: bistability and coherence

 In this paper, we study the combined dynamics of the neural activity and the synaptic efficiency changes in a fully connected network of biologically realistic neurons with simple synaptic plasticity dynamics including both potentiation and depression. Using a mean-field of technique, we analyzed the equilibrium states of neural networks with dynamic synaptic connections and found a class of bistable networks. For this class of networks, one of the stable equilibrium states shows strong connectivity and coherent responses to external input. In the other stable equilibrium, the network is loosely connected and responds non coherently to external input. Transitions between the two states can be achieved by positively or negatively correlated external inputs. Such networks can therefore switch between their phases according to the statistical properties of the external input. Non-coherent input can only “rcad” the state of the network, while a correlated one can change its state. We speculate that this property, specific for plastic neural networks, can give a clue to understand fully unsupervised learning models. Received: 8 August 1999 / Accepted in revised form: 16 March 2000     

A dynamic neural network with temporal coding and functional connectivity

A neural network model capable of altering its pattern classifying properties by program input is proposed. Here the “program input” is another source of input besides the pattern input. Unlike most neural network models, this model runs as a deterministic point process of spikes in continuous time; connections among neurons have finite delays, which are set randomly according to a normal distribution. Furthermore, this model utilizes functional connectivity which is dynamic connectivity among neurons peculiar to temporal-coding neural networks with short neuronal decay time constants. Computer simulation of the proposed network has been performed, and the results are considered in light of experimental results shown recently for correlated firings of neurons. Received: 6 December 1996 / Accepted in revised form: 15 September 1997     

Two types of using of space in the resident common shrews <Emphasis Type="Italic">Sorex araneus</Emphasis> L.

The home range of resident animals is considered as “familiar area” including a “foraging area.” It has been revealed that the activity of an average animal unit in the “foraging area” could be approximated by normal distribution. Estimation of activity distribution in the “familiar area” (beyond the “foraging area”) was impeded by means of marking since it might be difficult to record distant movements, and the method does not provide an essential body of data. In the case of the common shrew Sorex araneus, the “familiar area” was estimated using pitfall as animals evade them in the known areal. The “foraging area” radius of the average shrew was taken to be 30 m (95% of the animal unit activity), the radius of “familiar area” was within the range of 180–240 m. The “foraging area” was expected to provide the animal with vital resources, and the “familiar area” reflects its need for exploratory activity.     

Biosemiosis and the Cellular Basis of Mind

The human brain is a complex organ made up of neurons and several other cell types, and whose role is processing information for use in elicitation of behaviors. To accomplish this, the brain requires large amounts of energy, and this energy is obtained by the oxidation of glucose (Glc). However, the question of how the oxidation of Glc by individual neurons in brain results in their collective ability to rapidly generate feats of cognition that allow them to recognize the nature of the universe in which they live and to communicate this information remains unclear. In this article, insights into this process are provided by first considering the brain’ s homeostatic “operating system” for supply of energy to stimulated neurons, and how this system defines the basic unit of brain “structure”. This is followed by consideration of the brain’s “two-cell” neuronal communication mechanism which defines the basic unit of brain “function”. Finally, an analysis of the nature of frequency-encoded “neuronal languages” that enable ensembles of neurons to translate energy derived from the oxidation of Glc into a collective “mind”, the aggregate of all brain processes including those involving perception, thought, insight, foresight, imagination and behavior.     

Dynamics and bifurcations of two coupled neural oscillators with different connection types

In this paper we present an oscillatory neural network composed of two coupled neural oscillators of the Wilson-Cowan type. Each of the oscillators describes the dynamics of average activities of excitatory and inhibitory populations of neurons. The network serves as a model for several possible network architectures. We study how the type and the strength of the connections between the oscillators affect the dynamics of the neural network. We investigate, separately from each other, four possible connection types (excitatory→excitatory, excitatory→inhibitory, inhibitory→excitatory, and inhibitory→inhibitory) and compute the corresponding bifurcation diagrams. In case of weak connections (small strength), the connection of populations of different types lead to periodicin-phase oscillations, while the connection of populations of the same type lead to periodicanti-phase oscillations. For intermediate connection strengths, the networks can enter quasiperiodic or chaotic regimes, and can also exhibit multistability. More generally, our analysis highlights the great diversity of the response of neural networks to a change of the connection strength, for different connection architectures. In the discussion, we address in particular the problem of information coding in the brain using quasiperiodic and chaotic oscillations. In modeling low levels of information processing, we propose that feature binding should be sought as a temporally coherent phase-locking of neural activity. This phase-locking is provided by one or more interacting convergent zones and does not require a central “top level” subcortical circuit (e.g. the septo-hippocampal system). We build a two layer model to show that although the application of a complex stimulus usually leads to different convergent zones with high frequency oscillations, it is nevertheless possible to synchronize these oscillations at a lower frequency level using envelope oscillations. This is interpreted as a feature binding of a complex stimulus.     

Generic properties of combinatory maps: Neutral networks of RNA secondary structures

Random graph theory is used to model and analyse the relationship between sequences and secondary structures of RNA molecules, which are understood as mappings from sequence space into shape space. These maps are non-invertible since there are always many orders of magnitude more sequences than structures. Sequences folding into identical structures formneutral networks. A neutral network is embedded in the set of sequences that arecompatible with the given structure. Networks are modeled as graphs and constructed by random choice of vertices from the space of compatible sequences. The theory characterizes neutral networks by the mean fraction of neutral neighbors (λ). The networks are connected and percolate sequence space if the fraction of neutral nearest neighbors exceeds a threshold value (λ>λ*). Below threshold (λ<λ*), the networks are partitioned into a largest “giant” component and several smaller components. Structure are classified as “common” or “rare” according to the sizes of their pre-images, i.e. according to the fractions of sequences folding into them. The neutral networks of any pair of two different common structures almost touch each other, and, as expressed by the conjecture ofshape space covering sequences folding into almost all common structures, can be found in a small ball of an arbitrary location in sequence space. The results from random graph theory are compared to data obtained by folding large samples of RNA sequences. Differences are explained in terms of specific features of RNA molecular structures. Deicated to professor Manfred Eigen     

Catalysis in Reaction Networks

We define catalytic networks as chemical reaction networks with an essentially catalytic reaction pathway: one which is “on” in the presence of certain catalysts and “off” in their absence. We show that examples of catalytic networks include synthetic DNA molecular circuits that have been shown to perform signal amplification and molecular logic. Recall that a critical siphon is a subset of the species in a chemical reaction network whose absence is forward invariant and stoichiometrically compatible with a positive point. Our main theorem is that all weakly-reversible networks with critical siphons are catalytic. Consequently, we obtain new proofs for the persistence of atomic event-systems of Adleman et al., and normal networks of Gnacadja. We define autocatalytic networks, and conjecture that a weakly-reversible reaction network has critical siphons if and only if it is autocatalytic.     

The cancer cell’s “power plants” as promising therapeutic targets: An overview

This introductory article to the review series entitled “The Cancer Cell’s Power Plants as Promising Therapeutic Targets” is written while more than 20 million people suffer from cancer. It summarizes strategies to destroy or prevent cancers by targeting their energy production factories, i.e., “power plants.” All nucleated animal/human cells have two types of power plants, i.e., systems that make the “high energy” compound ATP from ADP and P _i . One type is “glycolysis,” the other the “mitochondria.” In contrast to most normal cells where the mitochondria are the major ATP producers (>90%) in fueling growth, human cancers detected via Positron Emission Tomography (PET) rely on both types of power plants. In such cancers, glycolysis may contribute nearly half the ATP even in the presence of oxygen (“Warburg effect”). Based solely on cell energetics, this presents a challenge to identify curative agents that destroy only cancer cells as they must destroy both of their power plants causing “necrotic cell death” and leave normal cells alone. One such agent, 3-bromopyruvate (3-BrPA), a lactic acid analog, has been shown to inhibit both glycolytic and mitochondrial ATP production in rapidly growing cancers (Ko et al., Cancer Letts., 173, 83–91, 2001), leave normal cells alone, and eradicate advanced cancers (19 of 19) in a rodent model (Ko et al., Biochem. Biophys. Res. Commun., 324, 269–275, 2004). A second approach is to induce only cancer cells to undergo “apoptotic cell death.” Here, mitochondria release cell death inducing factors (e.g., cytochrome c). In a third approach, cancer cells are induced to die by both apoptotic and necrotic events. In summary, much effort is being focused on identifying agents that induce “necrotic,” “apoptotic” or apoptotic plus necrotic cell death only in cancer cells. Regardless how death is inflicted, every cancer cell must die, be it fast or slow.     

Reconstruction of Some Hybrid Phylogenetic Networks with Homoplasies from Distances

Suppose G is a phylogenetic network given as a rooted acyclic directed graph. Let X be a subset of the vertex set containing the root, all leaves, and all vertices of outdegree 1. A vertex is “regular” if it has a unique parent, and “hybrid” if it has two parents. Consider the case where each gene is binary. Assume an idealized system of inheritance in which no homoplasies occur at regular vertices, but homoplasies can occur at hybrid vertices. Under our model, the distances between taxa are shown to be described using a system of numbers called “originating weights” and “homoplasy weights.” Assume that the distances are known between all members of X. Sufficient conditions are given such that the graph G and all the originating and homoplasy weights can be reconstructed from the given distances.     

I/We Narratives Among African American Families Raising Children with Special Needs

This paper examines a statistics debate among African American caregivers raising children with disabilities for insights into the work of “African American mothering.” Using ethnographic, narrative and discourse analyses, we delineate the work that African American mothers do—in and beyond this conversation—to cross ideological and epistemological boundaries around race and disability. Their work entails choosing to be an “I” and, in some cases, actively resisting being seen as a “they” and/or part of a collective “we” in order to chart alternative futures for themselves and their children.     

Physico-chemical model of a voltage-gated sodium channel

A permeant ion is known to create in the channel pore a local electrical field, the intensity of which exceeds the intensity of an electrical field produced by the membrane potential. In our study, we consider a sodium channel model, in which the effects of a permeant ion, an inactivating particle, and pharmacological agents on mobile charged groups of the channel are semi-phenomenologically taken into account by using motion equations for a generalized structural variable. Stationary solutions for the equation correspond to “open,” “closed,” and “inactivated” channel states. Because of this, the channel free energy profile, as a function of the structural variable, has three local minima. The three energy values of these states depend both on the electrical field applied externally and on the near-membrane concentrations of permeant ions and acting pharmacological agents. Sodium channel activation and inactivation kinetics are considered resulting from relative changes of the free energy typical of the above three states of the channel. The results we obtained in the course of channel activation and inactivation modeling and their voltage dependence are qualitatively consistent with the commonly known experimental data. The proposed model allows one to qualitatively predict the dependence of the sodium channel kinetic characteristics on the concentrations of permeant ions and pharmacological agents.     

Memory but no suppression in low-dimensional symmetric idiotypic networks

We present a new symmetric model of the idiotypic immune network. The model specifies clones of B-lymphocytes and incorporates: (1) influx and decay of cells; (2) symmetric stimulatory and inhibitory idiotypic interactions; (3) an explicit affinity parameter (matrix); (4) external (i.e. non-idiotypic) antigens. Suppression is the dominant interaction, i.e. strong idiotypic interactions are always suppressive. This precludes reciprocal stimulation of large clones and thus infinite proliferation. Idiotypic interactions first evoke proliferation, this enlarges the clones, and may in turn evoke suppression. We investigate the effect of idiotypic interactions on normal proliferative immune responses to antigens (e.g. viruses). A 2-D, i.e. two clone, network has a maximum of three stable equilibria: the virgin state and two asymmetric immune states. The immune states only exist if the affinity of the idiotypic interaction is high enough. Stimulation with antigen leads to a switch from the virgin state to the corresponding immune state. The network therefore remembers antigens, i.e. it accounts for immunity/memory by switching beteen multiple stable states. 3-D systems have, depending on the affinities, 9 qualitatively different states. Most of these also account for memory by state switching. Our idiotypic network however fails to account for the control of proliferation, e.g. suppression of excessive proliferation. In symmetric networks, the proliferating clones suppress their anti-idiotypic suppressors long before the latter can suppress the former. The absence of proliferation control violates the general assumption that idiotypic interactions play an important role in immune regulation. We therefore test the robustness of these results by abandoning our assumption that proliferation occurs before suppression. We thus define an “escape from suppression” model, i.e. in the “virgin” state idiotypic interactions are now suppressive. This system erratically accounts for memory and never for suppression. We conclude that our “absence of suppression from idiotypic interactions” does not hinge upon our “proliferation before suppression” assumption.