Functional models for large-scale gene regulation networks: realism and fiction

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as realistic and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.Comment: to appear on Mol. BioSyst. 200

Universal Features in the Genome-level Evolution of Protein Domains

Protein domains are found on genomes with notable statistical distributions, which bear a high degree of similarity. Previous work has shown how these distributions can be accounted for by simple models, where the main ingredients are probabilities of duplication, innovation, and loss of domains. However, no one so far has addressed the issue that these distributions follow definite trends depending on protein-coding genome size only. We present a stochastic duplication/innovation model, falling in the class of so-called Chinese Restaurant Processes, able to explain this feature of the data. Using only two universal parameters, related to a minimal number of domains and to the relative weight of innovation to duplication, the model reproduces two important aspects: (a) the populations of domain classes (the sets, related to homology classes, containing realizations of the same domain in different proteins) follow common power-laws whose cutoff is dictated by genome size, and (b) the number of domain families is universal and markedly sublinear in genome size. An important ingredient of the model is that the innovation probability decreases with genome size. We propose the possibility to interpret this as a global constraint given by the cost of expanding an increasingly complex interactome. Finally, we introduce a variant of the model where the choice of a new domain relates to its occurrence in genomic data, and thus accounts for fold specificity. Both models have general quantitative agreement with data from hundreds of genomes, which indicates the coexistence of the well-known specificity of proteomes with robust self-organizing phenomena related to the basic evolutionary ``moves'' of duplication and innovation

Employee Reactions to Technology Implementations: A Before and Within Implementation Comparison

The implementation of new technology is common in organizations and has long been of interest to researchers. Despite this interest, much of this research has focused on the success of the system being implemented. Equally important to firms is an understanding of how these implementations affect an employee’s attitudes toward their work environment. Therefore, this research seeks to understand how these implementations affect employee attitudes and commitment to the organization. Using a survey methodology, data will be collected in a mid-size manufacturing firm currently implementing barcode scanning throughout their organization. Specifically this study will compare employees who have not yet implemented the technology with employees who are in the middle of the implementation on several factors including barcode scanner self-efficacy, job self-efficacy, employee attitudes, job stress, satisfaction, and organizational commitment

Influence of interface potential on the effective mass in Ge nanostructures

The role of the interface potential on the effective mass of charge carriers is elucidated in this work. We develop a new theoretical formalism using a spatially dependent effective mass that is related to the magnitude of the interface potential. Using this formalism we studied Ge quantum dots (QDs) formed by plasma enhanced chemical vapour deposition (PECVD) and co-sputtering (sputter). These samples allowed us to isolate important consequences arising from differences in the interface potential. We found that for a higher interface potential, as in the case of PECVD QDs, there is a larger reduction in the effective mass, which increases the confinement energy with respect to the sputter sample. We further understood the action of O interface states by comparing our results with Ge QDs grown by molecular beam epitaxy. It is found that the O states can suppress the influence of the interface potential. From our theoretical formalism we determine the length scale over which the interface potential influences the effective mass

Fixed points of dynamic processes of set-valued F-contractions and application to functional equations

The article is a continuation of the investigations concerning F-contractions which have been recently introduced in [Wardowski in Fixed Point Theory Appl. 2012:94,2012]. The authors extend the concept of F-contractive mappings to the case of nonlinear F-contractions and prove a fixed point theorem via the dynamic processes. The paper includes a non-trivial example which shows the motivation for such investigations. The work is summarized by the application of the introduced nonlinear F-contractions to functional equations