ABSTRACT Complex cellular processes are modular and are accomplished by the concerted action of functional modules (Ravasz et al., Science 2002;297:1551–1555; Hartwell et al., Nature 1999;402: C47–52). These modules encompass groups of genes or proteins involved in common elementary biological functions. One important and largely unsolved goal of functional genomics is the identification of functional modules from genomewide information, such as transcription profiles or protein interactions. To cope with the ever-increasing volume and complexity of protein interaction data (Bader et al., Nucleic Acids Res 2001;29:242–245; Xenarios et al., Nucleic Acids Res 2002;30:303–305), new automated approaches for pattern discovery in these densely connected interaction networks are required

ORA is a network analysis tool that detects risks or vulnerabilities of an organization’s design structure. The design structure of an organization is the relationship among its personnel, knowledge, resources, and tasks entities. These entities and relationships are represented by the Meta-Matrix. Measures that take as input a Meta-Matrix are used to analyze the structural properties of an organization for potential risk. ORA contains over 50 measures which are categorized by which type of risk they detect. Measures are also organized by input requirements and by output. ORA generates formatted reports viewable on screen or in log files, and reads and writes networks in multiple data formats to be interoperable with existing network analysis packages. In addition, it has tools for graphically visualizing Meta-Matrix data and for optimizing a network’s design structure. ORA uses a Java interface for ease of use, and a C++ computational backend. The current version ORA 1.2 software is available on the CASOS

Background: DNA microarrays are used to produce large sets of expression measurements from which specific biological information is sought. Their analysis requires efficient and reliable algorithms for dimensional reduction, classification and annotation.

Abstract not found

We investigate the properties of an autoassociative network of thresholdlinear units whose synaptic connectivity is spatially structured and asymmetric. Since the methods of equilibrium statistical mechanics cannot be applied to such a network due to the lack of a Hamiltonian, we approach the problem through a signal-to-noise analysis, that we adapt to spatially organized networks. The conditions are analyzed for the appearance of stable, spatially non-uniform profiles of activity with large overlaps with one of the stored patterns. It is also shown, with simulations and analytic results, that the storage capacity does not decrease much when the connectivity of the network becomes short range. In addition, the method used here enables us to calculate exactly the storage capacity of a randomly connected network with arbitrary degree of dilution. 1

Targeted social distancing to mitigate pandemic influenza can be designed through simulation of influenza's spread within local community social contact networks. We demonstrate this design for a stylized community representative of a small town in the United States. The critical importance of children and teenagers in transmission of influenza is first identified and targeted. For influenza as infectious as 1957-58 Asian flu (≈50 % infected), closing schools and keeping children and teenagers at home reduced the attack rate by>90%. For more infectious strains, or transmission that is less focused on the young, adults and the work environment must also be targeted. Tailored to specific communities across the world, such design would yield local defenses against a highly virulent strain in the absence of vaccine and antiviral drugs. At the start of an influenza pandemic, effective vaccine and antiviral drugs may not be available to the general population (1,2). If the accompanying illness and death rates of the virus strain are high, how might a community respond to protect itself? Closing roads, restricting travel, and community-level quarantine will enter discussions (3,4). However, within a community, influenza spreads from person to person through the social contact network. Therefore, understanding and strategically controlling this network during a period of pandemic is critical. We describe how social contact network–focused mitigation can be designed. At the foundation of the design process is a network-based simulation model for the spread of influenza. We apply this model to a community of 10,000 persons connected within an overlapping, stylized, social network representative of a small US town. After study of the unmitigated transmission of influenza within the community, we change the frequency of contact within targeted groups and build combinations of strategies that

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A novel approach to phylogenetic tree construction using stochastic optimization and clustering

Despite improved rational drug design and a remarkable progress in genomic, proteomic and highthroughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade. Multi-target drugs multiply the number of pharmacologically relevant target molecules by introducing a set of indirect, network-dependent effects. Parallel with this the low-affinity binding of multi-target drugs eases the constraints of druggability, and significantly increases the size of the druggable proteome. These effects tremendously expand the number of potential drug targets, and will introduce novel classes of multi-target drugs with smaller side effects and toxicity. Here we review the recent progress in this field, compare possible network attack strategies, and propose several methods to find target-sets for multi-target drugs.

Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals ’ risk of infection and time to infection in small-world and randomly mixing networks. Infection transmitted more rapidly but ultimately resulted in fewer infected individuals in the small-world, compared with the random, network. The ability of measures of network centrality to identify high-risk individuals was also assessed. ‘‘Centrality’ ’ describes an individual’s position in a population; numerous parameters are available to assess this attribute. Here, the authors use the centrality measures degree (number of contacts), random-walk betweenness (a measure of the proportion of times an individual lies on the path between other individuals), shortest-path betweenness (the proportion of times an individual lies on the shortest path between other individuals), and farness (the sum of the number of steps between an individual and all other individuals). Each was associated with time to infection and risk of infection in the simulated outbreaks. In the networks examined, degree (which is the most readily measured) was at least as good as other network parameters in predicting risk of infection. Identification of more central individuals in populations may be used to inform surveillance and infection control strategies. disease outbreaks; disease transmission; infection; population surveillance Abbreviations: HIV, human immunodeficiency virus; R0, basic reproductive number. Recent high-profile outbreaks of infectious diseases have