A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas

Department of Biomedical Informatics (DBMI), Columbia University, New York, NY 10032, USA.
Molecular Systems Biology 02/2008; 4(1). DOI: 10.1038/msb.2008.2
Source: PubMed

ABSTRACT The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non-Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B-cell interactome (BCI), a genome-wide compendium of human B-cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases.

Download full-text


Available from: Kai Wang, Jun 27, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology since it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.
    Journal of Molecular Endocrinology 10/2013; DOI:10.1530/JME-13-0112 · 3.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Purpose Complex networks seem to be ubiquitous objects in contemporary research, both in the natural and social sciences. An important area of research regarding the applicability and modeling of graph- theoretical-oriented approaches to complex systems, is the probabilistic inference of such networks. There exist different methods and algorithms designed for this purpose, most of them are inspired in statistical mechanics and rely on information theoretical grounds. An important shortcoming for most of these methods, when it comes to disentangle the actual structure of complex networks, is that they fail to distinguish between direct and indirect interactions. Here, we suggest a method to discover and assess for such indirect interactions within the framework of information theory. Methods Information-theoretical measures (in particular, Mutual Information) are applied for the probabilistic inference of complex networks. Data Processing Inequality is used to find and assess for direct and indirect interactions impact in complex networks. Results We outline the mathematical basis of information-theoretical assessment of complex network structure and discuss some examples of application in the fields of biological systems and social networks. Conclusions Information theory provides to the field of complex networks analysis with effective means for structural assessment with a computational burden low enough to be useful in both, Biological and Social network analysis.
    04/2013; 1(1):8. DOI:10.1186/2194-3206-1-8
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Interactions across biological networks are often quantified under a single set of conditions; however, cellular behaviors are dynamic and interactions can be expected to change in response to molecular context and environment. To determine the consistency of network interactions, we examined the enzyme network responsible for the reduction of nicotinamide adenine dinucleotide phosphate (NADP) to NADPH across three different conditions: oxidative stress, starvation, and desiccation. Synthetic, activity-variant alleles were used in Drosophila melanogaster for glucose-6-phosphate dehydrogenase (G6pd), cytosolic isocitrate dehydrogenase (Idh), and cytosolic malic enzyme (Men) along with seven different genetic backgrounds to lend biological relevance to the data. The responses of the NADP-reducing enzymes and two downstream phenotypes (lipid and glycogen concentration) were compared between the control and stress conditions. In general, responses in NADP-reducing enzymes were greater under conditions of oxidative stress, likely due to an increased demand for NADPH. Interactions between the enzymes were altered by environmental stress in directions and magnitudes that are consistent with differential contributions of the different enzymes to the NADPH pool: the contributions of G6PD and IDH seem to be accentuated by oxidative stress, and MEN by starvation. Overall, we find that biological network interactions are strongly influenced by environmental conditions, underscoring the importance of examining networks as dynamic entities.
    G3-Genes Genomes Genetics 12/2012; 2(12):1613-23. DOI:10.1534/g3.112.003715 · 2.51 Impact Factor