Data integration, pathway analysis and mining for systems biology

Post-genomic molecular biology embodies high-throughput experimental techniques and hence is a data-rich field. The goal of this thesis is to develop bioinformatics methods to utilise publicly available data in order to produce knowledge and to aid mining of newly generated data. As an example of knowledge or hypothesis generation, consider function prediction of biological molecules. Assignment of protein function is a non-trivial task owing to the fact that the same protein may be involved in different biological processes, depending on the state of the biological system and protein localisation. The function of a gene or a gene product may be provided as a textual description in a gene or protein annotation database. Such textual descriptions lack in providing the contextual meaning of the gene function. Therefore, we need ways to represent the meaning in a formal way. Here we apply data integration approach to provide rich representation that enables context-sensitive mining of biological data in terms of integrated networks and conceptual spaces. Context-sensitive gene function annotation follows naturally from this framework, as a particular application. Next, knowledge that is already publicly available can be used to aid mining of new experimental data. We developed an integrative bioinformatics method that utilises publicly available knowledge of protein-protein interactions, metabolic networks and transcriptional regulatory networks to analyse transcriptomics data and predict altered biological processes. We applied this method to a study of dynamic response of Saccharomyces cerevisiae to oxidative stress. The application of our method revealed dynamically altered biological functions in response to oxidative stress, which were validated by comprehensive in vivo metabolomics experiments. The results provided in this thesis indicate that integration of heterogeneous biological data facilitates advanced mining of the data. The methods can be applied for gaining insight into functions of genes, gene products and other molecules, as well as for offering functional interpretation to transcriptomics and metabolomics experiments

Customizable views on semantically integrated networks for systems biology

Motivation: The rise of high-throughput technologies in the post-genomic era has led to the production of large amounts of biological data. Many of these datasets are freely available on the Internet. Making optimal use of these data is a significant challenge for bioinformaticians. Various strategies for integrating data have been proposed to address this challenge. One of the most promising approaches is the development of semantically rich integrated datasets. Although well suited to computational manipulation, such integrated datasets are typically too large and complex for easy visualization and interactive exploration

Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together

Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at:

A Molecular Biology Database Digest

Computational Biology or Bioinformatics has been defined as the application of mathematical and Computer Science methods to solving problems in Molecular Biology that require large scale data, computation, and analysis [18]. As expected, Molecular Biology databases play an essential role in Computational Biology research and development. This paper introduces into current Molecular Biology databases, stressing data modeling, data acquisition, data retrieval, and the integration of Molecular Biology data from different sources. This paper is primarily intended for an audience of computer scientists with a limited background in Biology

Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput “omics” Data

High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge

Trends in modeling Biomedical Complex Systems

In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented

Trends in modeling Biomedical Complex Systems.

In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are