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.

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Available from: Kai Wang, Aug 23, 2015
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    • "Several examples of disease-focused networks are now emerging, especially in the area of cancer research where numerous cancer cluster gene networks have been described in different types of tumours, including colorectal , ovarian and breast cancers (Bravata et al. 2013, Kumar et al. 2013b, Yamaga et al. 2013, Zhang et al. 2013). Network analysis is also leading to promising advances in other fields, including neurological diseases (Parkinson's and Alzheimer's diseases (Balthazar et al. 2013, Lones et al. 2013, Strafella 2013)) and neuropsychiatric disorders (Kasparek et al. 2013); diseases of the immune system (Ivanov & Anderson 2013) including autoimmune or haematologic diseases (Mani et al. 2008, Kumar et al. 2013a); the pathogenesis of coronary heart diseases (Huan et al. 2013), fatty liver diseases (Sookoian & Pirola 2013) and endocrine/metabolic conditions, which will be covered in more detail in the following sections. "
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    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; 52. DOI:10.1530/JME-13-0112 · 3.62 Impact Factor
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    • "In addition to these good performance general methods (references (Liang and Wang 2008; Zhang et al. 2012) were developed for gene regulatory networks although with minimal adjustments can ba applied to any other probabilistically inferred networks), there are also more specific approaches based on somehow Ad Hoc considerations. We can mention, for instance the MARINa algorithm (Lefebvre et al. 2010; Lefebvre et al. 2007; Mani et al. 2008) developed specifically for the assessment and reconstruction of gene regulatory networks based on statistical enrichment of certain signatures (Subramanian et al. 2005), an approach close in philosophy of that of conditioning variables that, however requires for additional information (i.e. the signatures themselves) to be useful, hence is more restricted to its scope and applications as are approaches relying on additional phenotypic information (Wu et al. 2009; Yu et al. 2006). "
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    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
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    • "In addition, the way in which network members interact, or whether they interact at all, may be expected to change across molecular contexts (i.e., cellular conditions, genetic backgrounds) or environmental stressors (Dworkin et al. 2009). To account for, or quantify, this variability and cross-interaction, several studies have taken large-scale approaches to quantify how interactions change across conditions (Luscombe et al. 2004; Mani et al. 2008; Wang et al. 2009; Bandyopadhyay et al. 2010), but these studies may be limited by the sheer size and complexity of the networks they investigate , making it difficult to obtain direct measures of genetic interactions, to detect smaller-scale interactions, or to examine interactions across a number of different contexts, such as multiple environmental conditions or genetic backgrounds. Research on relatively small, well-characterized metabolic networks can complement large-scale studies and offer additional insight into consistency of biological networks by allowing for a fine-scale examination across multiple conditions. "
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    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
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