Retrieval, alignment, and clustering of computational models based on semantic annotations

As the number of computational systems biology models increases, new methods are needed to explore their content and build connections with experimental data. In this Perspective article, the authors propose a flexible semantic framework that can help achieve these aims

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p

A Cognitive Information Theory of Music: A Computational Memetics Approach

This thesis offers an account of music cognition based on information theory and memetics. My research strategy is to split the memetic modelling into four layers: Data, Information, Psychology and Application. Multiple cognitive models are proposed for the Information and Psychology layers, and the MDL best-fit models with published human data are selected. Then, for the Psychology layer only, new experiments are conducted to validate the best-fit models. In the information chapter, an information-theoretic model of musical memory is proposed, along with two competing models. The proposed model exhibited a better fit with human data than the competing models. Higher-level psychological theories are then built on top of this information layer. In the similarity chapter, I proposed three competing models of musical similarity, and conducted a new experiment to validate the best-fit model. In the fitness chapter, I again proposed three competing models of musical fitness, and conducted a new experiment to validate the best-fit model. In both cases, the correlations with human data are statistically significant. All in all, my research has shown that the memetic strategy is sound, and the modelling results are encouraging. Implications of this research are discussed

Solutions to decision-making problems in management engineering using molecular computational algorithms and experimentations

制度:新 ; 報告番号:甲3368号 ; 学位の種類:博士(工学) ; 授与年月日:2011/5/23 ; 早大学位記番号:新568

Doctor of Philosophy

dissertationAppropriate regulation of gene expression is important for the development and homeostasis of multicellular organisms. DNA sequencespecific transcription factors play a central role in regulating the first step of gene expression, transcription. The aberrant expression of transcription factors is a common mechanism for the initiation and progression of many human cancers. The ETS family of transcription factors consists of twenty-eight human proteins that contain a conserved DNA-binding domain, termed the ETS domain. ETS factors have varied roles in organismal development and disease etiology. For example, ETS proteins from the ERG and ETV1/4/5 subfamilies are overexpressed in the majority of prostate cancers and contribute to cancer initiation and progression. In stark contrast, EHF and SPDEF are two ETS factors present in normal prostate tissue that have been characterized as tumor suppressors whose genes are often deleted during cancer progression. The phenotypic dichotomy displayed between these subclasses of ETS factors suggests that the understanding of the molecular mechanisms that underlie transcription factors' roles in normal and disease settings may provide additional opportunities for therapeutic intervention. Here we describe the DNA-binding autoinhibition of ETS factors ETV1, iv ETV4, and ETV5. An intrinsically disordered region and an α-helix cooperate to inhibit DNA-binding by altering the positioning of the DNA-recognition α-helix of the ETS domain. These inhibitory elements are distinct from those that have been previously described for other ETS factors. We also characterize the interaction of Mediator subunit 25 (MED25) with the transcriptional activation and DNA-binding domains of ETV4. The inhibitory α-helix of ETV4 provides a unique interaction surface for MED25, as compared to other ETS domains, and interaction with MED25 activates the DNA-binding of ETV4. We also demonstrate the differential ability of ETS factors to bind to DNA with JUN-FOS at composite DNA binding sites. These distinct intra- and intermolecular interactions distinguish ETS oncoproteins and tumor suppressors in prostate cancer and may, in part, underlie their phenotypic differences. Finally, we present an assay for ETS-DNA interactions that is amenable to high-throughput screening for small molecule inhibitors. This assay could be further modified to incorporate any of the previously described partnerships