Sharron Bransburg-Zabary

Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002

Background The 2007–2009 financial crisis, and its fallout, has strongly emphasized the need to define new ways and measures to study and assess the stock market dynamics. Methodology/Principal Findings The S&P500 dynamics during 4/1999–4/2010 is investigated in terms of the index cohesive force (ICF - the balance between the stock correlations and the partial correlations after subtraction of the index contribution), and the Eigenvalue entropy of the stock correlation matrices. We found a rapid market transition at the end of 2001 from a flexible state of low ICF into a stiff (nonflexible) state of high ICF that is prone to market systemic collapses. The stiff state is also marked by strong effect of the market index on the stock-stock correlations as well as bursts of high stock correlations reminiscence of epileptic brain activity. Conclusions/Significance The market dynamical states, stability and transition between economic states was studies using new quantitative measures. Doing so shed new light on the origin and nature of the current crisis. The new approach is likely to be applicable to other classes of complex systems from gene networks to the human brain.

Genome sequence of the pattern forming Paenibacillus vortex bacterium reveals potential for thriving in complex environments

BackgroundThe pattern-forming bacterium Paenibacillus vortex is notable for its advanced social behavior, which is reflected in development of colonies with highly intricate architectures. Prior to this study, only two other Paenibacillus species (Paenibacillus sp. JDR-2 and Paenibacillus larvae) have been sequenced. However, no genomic data is available on the Paenibacillus species with pattern-forming and complex social motility. Here we report the de novo genome sequence of this Gram-positive, soil-dwelling, sporulating bacterium.ResultsThe complete P. vortex genome was sequenced by a hybrid approach using 454 Life Sciences and Illumina, achieving a total of 289× coverage, with 99.8% sequence identity between the two methods. The sequencing results were validated using a custom designed Agilent microarray expression chip which represented the coding and the non-coding regions. Analysis of the P. vortex genome revealed 6,437 open reading frames (ORFs) and 73 non-coding RNA genes. Comparative genomic analysis with 500 complete bacterial genomes revealed exceptionally high number of two-component system (TCS) genes, transcription factors (TFs), transport and defense related genes. Additionally, we have identified genes involved in the production of antimicrobial compounds and extracellular degrading enzymes.ConclusionsThese findings suggest that P. vortex has advanced faculties to perceive and react to a wide range of signaling molecules and environmental conditions, which could be associated with its ability to reconfigure and replicate complex colony architectures. Additionally, P. vortex is likely to serve as a rich source of genes important for agricultural, medical and industrial applications and it has the potential to advance the study of social microbiology within Gram-positive bacteria.

Analyses of antigen dependency networks unveil immune system reorganization between birth and adulthood

Much effort has been devoted to assess the importance of nodes in complex biological networks (such as gene transcriptional regulatory networks, protein interaction networks, and neural networks). Examples of commonly used measures of node importance include node degree, node centrality, and node vulnerability score (the effect of the node deletion on the network efficiency). Here, we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node-node correlations. To this end, we first define the dependency of node i on node j (or the influence of node j on node i), D(i, j) as the average over all nodes k of the difference between the i - k correlation and the partial correlations between these nodes with respect to node j. Note that the dependencies, D(i, j) define a directed weighted matrix, since, in general, D(i, j) differs from D( j, i). For this reason, many of the commonly used measures of node importance, such as node centrality, cannot be used. Hence, to assess the node importance of the dependency networks, we define the system level influence (SLI) of antigen j, SLI( j) as the sum of the influence of j on all other antigens i. Next, we define the system level influence or the influence score of antigen j, SLI( j) as the sum of D(i, j) over all nodes i. We introduce the new approach and demonstrate that it can unveil important biological information in the context of the immune system. More specifically, we investigated antigen dependency networks computed from antigen microarray data of autoantibody reactivity of IgM and IgG isotypes present in the sera of ten mothers and their newborns. We found that the analysis was able to unveil that there is only a subset of antigens that have high influence scores (SLI) common both to the mothers and newborns. Networks comparison in terms of modularity (using the Newmans algorithm) and of topology (measured by the divergence rate) revealed that, at birth, the IgG networks exhibit a more profound global reorganization while the IgM networks exhibit a more profound local reorganization. During immune system development, the modularity of the IgG network increases and becomes comparable to that of the IgM networks at adulthood. We also found the existence of several conserved IgG and IgM network motifs between the maternal and newborns networks, which might retain network information as our immune system develops. If correct, these findings provide a convincing demonstration of the effectiveness of the new approach to unveil most significant biological information. Whereas we have introduced the new approach within the context of the immune system, it is expected to be effective in the studies of other complex biological social, financial, and manmade networks.

Network Theory Analysis of Antibody-Antigen Reactivity Data: The Immune Trees at Birth and Adulthood

Motivation New antigen microarray technology enables parallel recording of antibody reactivities with hundreds of antigens. Such data affords system level analysis of the immune systems organization using methods and approaches from network theory. Here we measured the reactivity of 290 antigens (for both the IgG and IgM isotypes) of 10 healthy mothers and their term newborns. We constructed antigen correlation networks (or immune networks) whose nodes are the antigens and the edges are the antigen-antigen reactivity correlations, and we also computed their corresponding minimum spanning trees (MST) – maximal information reduced sub-graphs. We quantify the network organization (topology) in terms of the network theory divergence rate measure and rank the antigen importance in the full antigen correlation networks by the eigen-value centrality measure. This analysis makes possible the characterization and comparison of the IgG and IgM immune networks at birth (newborns) and adulthood (mothers) in terms of topology and node importance. Results Comparison of the immune network topology at birth and adulthood revealed partial conservation of the IgG immune network topology, and significant reorganization of the IgM immune networks. Inspection of the antigen importance revealed some dominant (in terms of high centrality) antigens in the IgG and IgM networks at birth, which retain their importance at adulthood.

Genome Holography: Deciphering Function-Form Motifs from Gene Expression Data

Background DNA chips allow simultaneous measurements of genome-wide response of thousands of genes, i.e. system level monitoring of the gene-network activity. Advanced analysis methods have been developed to extract meaningful information from the vast amount of raw gene-expression data obtained from the microarray measurements. These methods usually aimed to distinguish between groups of subjects (e.g., cancer patients vs. healthy subjects) or identifying marker genes that help to distinguish between those groups. We assumed that motifs related to the internal structure of operons and gene-networks regulation are also embedded in microarray and can be deciphered by using proper analysis. Methodology/Principal Findings The analysis presented here is based on investigating the gene-gene correlations. We analyze a database of gene expression of Bacillus subtilis exposed to sub-lethal levels of 37 different antibiotics. Using unsupervised analysis (dendrogram) of the matrix of normalized gene-gene correlations, we identified the operons as they form distinct clusters of genes in the sorted correlation matrix. Applying dimension-reduction algorithm (Principal Component Analysis, PCA) to the matrices of normalized correlations reveals functional motifs. The genes are placed in a reduced 3-dimensional space of the three leading PCA eigen-vectors according to their corresponding eigen-values. We found that the organization of the genes in the reduced PCA space recovers motifs of the operon internal structure, such as the order of the genes along the genome, gene separation by non-coding segments, and translational start and end regions. In addition to the intra-operon structure, it is also possible to predict inter-operon relationships, operons sharing functional regulation factors, and more. In particular, we demonstrate the above in the context of the competence and sporulation pathways. Conclusions/Significance We demonstrated that by analyzing gene-gene correlation from gene-expression data it is possible to identify operons and to predict unknown internal structure of operons and gene-networks regulation.

Theoretical calculations of the permeability of monensin-cation complexes in model bio-membranes.

Monensin is one of the best-characterized ionophores; it functions in the electroneutral exchange of cations between the extracellular and cytoplasmic sides of cell membranes. The X-ray crystal structures of monensin in free acid form and in complex with Na(+), K(+) and Ag(+) are known and we have recently measured the diffusion rates of monensin in free acid form (Mo-H) and in complex with Na(+) (Mo-Na) and with K(+) (Mo-K) using laser pulse techniques. The results have shown that Mo-H diffuses across the membrane one order of magnitude faster than Mo-Na and two orders of magnitude faster than Mo-K. Here, we report calculations of the translocation free energy of these complexes across the membrane along the most favorable path, i.e. the lowest free energy path. The calculations show that the most favorable orientation of monensin is with its hydrophobic furanyl and pyranyl moieties in the hydrocarbon region of the membrane and the carboxyl group and the cation at the water-membrane interface. Further, the calculations show that Mo-H is likely to be inserted deeper than Mo-Na into the bilayer, and that the free energy barrier for transfer of Mo-H across the membrane is approximately 1 kcal/mol lower than for Mo-Na, in good agreement with our measurements. Our results show that the Mo-K complex is unlikely to diffuse across lipid bilayers in its X-ray crystal structure, in contrast to the Mo-H and Mo-Na complexes. Apparently, when diffusing across the membrane, the Mo-K complex assumes a different conformation and/or thinning defects in the bilayer lower significantly the free energy barrier for the process. The suitability of the model for treating the membrane association of small molecules is discussed in view of the successes and failures observed for the monensin system.

Utilization of monensin for detection of microdomains in cholesterol containing membrane.

The effect of cholesterol on the monensin mediated proton-cation exchange reaction was measured in the time-resolved domain. The experimental system consisted of a black lipid membrane equilibrated with monensin (Nachliel, E., Finkelstein, Y. and Gutman, M. (1996) Biochim. Biophys. Acta 1285, 131-145). The membrane separated two compartments containing electrolyte solutions and pyranine (8-hydroxypyrene 1,3,6-trisulfonate) was added on to one side of the membrane. A short laser pulse was used to cause a brief transient acidification of the pyranine-containing solution and the resulting electric signal, derived from proton-cation exchange, was measured in the microsecond time domain. Incorporation of cholesterol had a clear effect on the electric transients as measured with Na+ or K+ as transportable cations. The measured transients were subjected to rigorous analysis based on numeric integration of coupled, non-linear, differential rate equations which correspond with the perturbed multi-equilibria state between all reactants present in the system. The various kinetic parameters of the reaction and their dependence on the cholesterol content had been determined. On the basis of these observations we can draw the following conclusions: (1) Cholesterol perturbed the homogeneity of the membrane and microdomains were formed, having a composition that differed from the average value. The ionophore was found in domains which were practically depleted of phosphatidylserine. (2) The diffusivity of the protonated monensin (MoH) was not affected by the presence of cholesterol, indicating that the viscosity of the central layer of the membrane was unaltered. (3) The diffusivity of the monensin metal complexes (MoNa and MoK) was significantly increased upon addition of cholesterol. As the viscosity along the cross membranal diffusion route is unchanged, the enhanced motion of the MoNa and MoK is attributed to variations of the electrostatic potential within the domains.

Tumor-Associated and Disease-Associated Autoantibody Repertoires in Healthy Colostrum and Maternal and Newborn Cord Sera

In this work, we studied autoantibody repertoires and Ig isotypes in 71 mothers and their 104 healthy newborns (including twins and triplets delivered term or premature). Newborns receive maternal IgG Abs via the placenta before birth, but developing infants must produce their own IgM and IgA Abs. We used an Ag microarray analysis to detect binding to a selection of 295 self-Ags, compared with 27 standard foreign Ags. The magnitude of binding to specific self-Ags was found to be not less than that to the foreign Ags. As expected, each newborn shared with its mother a similar IgG repertoire—manifest as early as the 24th week of gestation. IgM and IgA autoantibody repertoires in cord sera were highly correlated among the newborns and differed from their mothers’ repertoires; the latter differed in sera and milk. The autoantibodies bound to self-Ags known to be associated with tumors and to autoimmune diseases. Thus, autoantibody repertoires in healthy humans—the immunological homunculus—arise congenitally, differ in maternal milk and sera, and mark the potential of the immune system to attack tumors, beneficially, or healthy tissues, harmfully; regulation of the tissue site, the dynamics, and the response phenotype of homuncular autoimmunity very likely affects health.

Individual and meta-immune networks

Networks can be found everywhere-in technology, in nature and in our bodies. In this paper we present how antigen networks can be used as a model to study network interaction and architecture. Utilizing antigen microarray data of the reactivity of hundreds of antibodies of sera of ten mothers and their newborns, we reconstruct networks, either isotype specific (IgM or IgG) or person specific-mothers or newborns-and investigate the network properties. Such an approach makes it possible to decipher fundamental information regarding the personal immune network state and its unique characteristics. In the current paper we demonstrate how we are successful in studying the interaction between two dependent networks, the maternal IgG repertoire and the one of the offspring, using the concept of meta-network provides essential information regarding the biological phenomenon of cross placental transfer. Such an approach is useful in the study of coupled networks in variety of scientific fields.

A Fast in Silico Simulation of Ion Flux through the Large-Pore Channel Proteins

The PSST program (see accompanying article) utilizes the detailed structure of a large-pore channel protein as the sole input for selection of trajectories along which negative and positive ions propagate. In the present study we applied this program to reconstruct the ion flux through five large-pore channel proteins (PhoE, OmpF, the WT R. blastica general diffusion porin and two of its mutants). The conducting trajectories, one for positive and one for negative particles, are contorted pathways that run close to arrays of charged residues on the inner surface of the channel. In silico propagation of the charged particles yielded passage time values that are compatible with the measured average passage time of ions. The calculated ionic mobilities are close to those of the electrolyte solution of comparable concentrations. Inspection of the transition probabilities along the channel revealed no region that could impose a rate-limiting step. It is concluded that the ion flux is a function of the whole array of local barriers. Thus, the conductance of the large-pore channel protein is determined by the channels shape and charge distribution, while the selectivity also reflects the features of the channels vestibule.