Uncovering network systems within protein structures

Traditionally, proteins have been viewed as a construct based on elements of secondary structure and their arrangement in three-dimensional space. In a departure from this perspective we show that protein structures can be modelled as network systems that exhibit small-world, single-scale, and to some degree, scale-free properties. The phenomenological network concept of degrees of separation is applied to three-dimensional protein structure networks and reveals how amino acid residues can be connected to each other within six degrees of separation. This work also illuminates the unique features of protein networks in comparison to other networks currently studied. Recognising that proteins are networks provides a means of rationalising the robustness in the overall three-dimensional fold of a protein against random mutations and suggests an alternative avenue to investigate the determinants of protein structure, function and folding.     

Phase diagram of nucleosome core particles

We present a phase diagram of the nucleosome core particle (NCP) as a function of the monovalent salt concentration and applied osmotic pressure. Above a critical pressure, NCPs stack on top of each other to form columns that further organize into multiple columnar phases. An isotropic (and in some cases a nematic) phase of columns is observed in the moderate pressure range. Under higher pressure conditions, a lamello-columnar phase and an inverse hexagonal phase form under low salt conditions, whereas a 2D hexagonal phase or a 3D orthorhombic phase is found at higher salt concentration. For intermediate salt concentrations, microphase separation occurs. The richness of the phase diagram originates from the heterogeneous distribution of charges at the surface of the NCP, which makes the particles extremely sensitive to small ionic variations of their environment, with consequences on their interactions and supramolecular organization. We discuss how the polymorphism of NCP supramolecular organization may be involved in chromatin changes in the cellular context.     

The evolutionary potential of the Drosophila sex determination gene network

The evolution of sex determination mechanisms is known to be relatively rapid, though recent evidence indicates that certain parts of the mechanism may be more highly conserved. These characteristics establish the sex determination mechanism as a good candidate for the theoretical study of gene network evolution, particularly of networks involved in development. We investigate the short-term evolutionary potential of the sex determination mechanism in Drosophila melanogaster with the aid of a synchronous logical model. We introduce general theoretical concepts such as a network-specific form of mutation, and a notion of functional equivalence between networks. We apply this theoretical framework to the sex determination mechanism and compare it to a population of random networks, enabling us to find features both general to sex determination networks, and particular to the Drosophila network. In general, sex determination networks exist within large sets of functionally equivalent networks all of which satisfy the sex determination task. These large sets are in turn composed of subsets which are mutationally related, suggesting a high degree of flexibility is available without compromising the core functionality. Two particular characteristics of the Drosophila network are found: (a) a parsimonious use of gene interactions, and (b) the network structure can produce a relatively large number of dynamical pattern variations through single network mutations.     

On Parametric Stability of Gene Networks Controlling Ontogenetic Processes

The problem of evaluation of parametric stability of three models of pro- and eukaryotic gene networks controlling ontogenetic processes has been defined and solved. Experimental schemes of testing gene networks for parametric stability based on the method of generalized threshold models were developed and realized as a software application. We studied the sensitivity of the functioning modes to random variations of the parameters in three model systems: phage development control system, Arabidopsis thaliana flower morphogenesis control subsystem, and gene subnetwork controlling early ontogeny of Drosophila melanogaster. The parametric stability was quantitatively assessed for these models.     

Fine oral filaments in Paramecium: a biochemical and immunological analysis

In Paramecium, several kinds of the oral networks of fine filaments are defined at the ultrastructural level. Using the sodium chloride-treated oral apparatus of Paramecium as an antigen to produce monoclonal antibodies, we have begun to identify the proteins constituting these networks. Immunoblotting showed that all positive antibodies were directed against three bands (70-, 75-and 83-kD), which corresponded to quantitatively minor components of the antigen; there was no antibody specific for the quantitatively major components (58- and 62-kD). Immunolocalization with four of these antibodies directed against one or several of these three bands showed that these proteins are components of the fine filaments supporting the oral area; a decoration of the basal bodies and the outer lattice was also observed on the cortex. Immunofluorescence on interphase cells suggested that the three proteins colocalized on the left side of the oral apparatus, whereas only the 70-kD band was detected on the right side. During division, the antigens of the antibodies were detected at different stages after oral basal body assembly. The antibodies cross-reacted with the tetrins, which are oral filament-forming proteins in Tetrahymena, demonstrating that tetrin-related proteins are quantitatively minor components of the oral and the somatic cytoskeleton of Paramecium.     

A comparison of methods for predicting vegetation type

Predictive modeling of vegetation patterns has wide application in vegetation science. In this paper I discuss three methods of predictive modeling using data from the alpine treeline ecotone as a case study. The study area is a portion of Glacier National Park, Montana. Parametric general linear models (GLM), artificial neural networks (ANN) and classification tree (CT) methods of predicting vegetation type are compared to determine the relative strength of each predictive approach and how they may be used in concert to increase understanding of important vegetation – environment relations. For each predictive method, vegetation type within the alpine treeline ecotone is predicted using a suite of environmental indicator variables including elevation, moisture potential, solar radiation potential, snow potential index, and disturbance history. Results from each of the predictive methods are compared against the real vegetation types to determine the relative accuracy of the methods.When the entire data field is examined (i.e., not evaluated by smaller spatial aggregates of data) the ANN procedure produces the most accurate predictions (=0.571); the CT predictions are the least accurate (=0.351). The predicted patterns of vegetation on the landscape are considerably different using the three methods. The GLM and CT methods produce large contiguous swaths of vegetation types throughout the study area, whereas the ANN method produces patterns with much more heterogeneity and smaller patches.When predictions are compared to reality at catchment scale, it becomes evident that the accuracy of each method varies depending upon the specific situation. The ANN procedure remains the most accurate method in the majority of the catchments, but both the GLM and PCT produce the most accurate classifications in at least one basin each.The variability in predictive ability of the three methods tested here indicates that there may not be a single best predictive method. Rather it may be important to use a suite of predictive models to help understand the environment – vegetation relationships. The ability to use multiple predictive methods to determine which spatial subunits of a landscape are outliers is important when identifying locations useful for climate change monitoring studies.     

Automated identification of Fos expression

The concentration of Fos, a protein encoded by the immediate-early gene c-fos, provides a measure of synaptic activity that may not parallel the electrical activity of neurons. Such a measure is important for the difficult problem of identifying dynamic properties of neuronal circuitries activated by a variety of stimuli and behaviours. We employ two-stage statistical pattern recognition to identify cellular nuclei that express Fos in two-dimensional sections of rat forebrain after administration of antipsychotic drugs. In stage one, we distinguish dark-stained candidate nuclei from image background by a thresholding algorithm and record size and shape measurements of these objects. In stage two, we compare performance of linear and quadratic discriminants, nearest-neighbour and artificial neural network classifiers that employ functions of these measurements to label candidate objects as either Fos nuclei, two touching Fos nuclei or irrelevant background material. New images of neighbouring brain tissue serve as test sets to assess generalizability of the best derived classification rule, as determined by lowest cross-validation misclassification rate. Three experts, two internal and one external, compare manual and automated results for accuracy assessment. Analyses of a subset of images on two separate occasions provide quantitative measures of inter- and intra-expert consistency. We conclude that our automated procedure yields results that compare favourably with those of the experts and thus has potential to remove much of the tedium, subjectivity and irreproducibility of current Fos identification methods in digital microscopy.     

Surface adhesion of fusion proteins containing the hydrophobins HFBI and HFBII from Trichoderma reesei

Hydrophobins are surface-active proteins produced by filamentous fungi, where they seem to be ubiquitous. They have a variety of roles in fungal physiology related to surface phenomena, such as adhesion, formation of surface layers, and lowering of surface tension. Hydrophobins can be divided into two classes based on the hydropathy profile of their primary sequence. We have studied the adhesion behavior of two Trichoderma reesei class II hydrophobins, HFBI and HFBII, as isolated proteins and as fusion proteins. Both hydrophobins were produced as C-terminal fusions to the core of the hydrolytic enzyme endoglucanase I from the same organism. It was shown that as a fusion partner, HFBI causes the fusion protein to efficiently immobilize to hydrophobic surfaces, such as silanized glass and Teflon. The properties of the surface-bound protein were analyzed by the enzymatic activity of the endoglucanase domain, by surface plasmon resonance (Biacore), and by a quartz crystal microbalance. We found that the HFBI fusion forms a tightly bound, rigid surface layer on a hydrophobic support. The HFBI domain also causes the fusion protein to polymerize in solution, possibly to a decamer. Although isolated HFBII binds efficiently to surfaces, it does not cause immobilization as a fusion partner, nor does it cause polymerization of the fusion protein in solution. The findings give new information on how hydrophobins function and how they can be used to immobilize fusion proteins.     

WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

In this paper, we propose an on-demand distributed clustering algorithm for multi-hop packet radio networks. These types of networks, also known as ad hoc networks, are dynamic in nature due to the mobility of nodes. The association and dissociation of nodes to and from clusters perturb the stability of the network topology, and hence a reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The clusterheads, form a dominant set in the network, determine the topology and its stability. The proposed weight-based distributed clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of mobile nodes. The time required to identify the clusterheads depends on the diameter of the underlying graph. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol. The non-periodic procedure for clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. The clusterheads, operating in dual power mode, connects the clusters which help in routing messages from a node to any other node. We observe a trade-off between the uniformity of the load handled by the clusterheads and the connectivity of the network. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of the number of clusterheads, reaffiliation frequency, and dominant set updates. Results show that our algorithm performs better than existing ones and is also tunable to different kinds of network conditions.     

Network genomics – A novel approach for the analysis of biological systems in the post-genomic era

Network Genomics studies genomics and proteomics foundations of cellular networks in biological systems. It complements systems biology in providing information on elements, their interaction and their functional interplay in cellular networks. The relationship between genomic and proteomic high-throughput technologies and computational methods are described, as well as several examples of specific network genomic application are presented.