Complex networks theory for analyzing metabolic networks

One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism, while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks.Comment: 13 pages, 2 figure

Analyzing network models to make discoveries about biological mechanisms

Systems biology provides alternatives to the strategies to developing mechanistic explanations traditionally pursued in cell and molecular biology and much discussed in accounts of mechanistic explanation. Rather than starting by identifying a mechanism for a given phenomenon and decomposing it, systems biologists often start by developing cell-wide networks of detected connections between proteins or genes and construe clusters of highly interactive components as potential mechanisms. Using inference strategies such as ‘guilt-by-association’, researchers advance hypotheses about functions performed of these mechanisms. I examine several examples of research on budding yeast, first on what are taken to be enduring networks and subsequently on networks that change as cells perform different activities or respond to different external condition

Analyzing network models to make discoveries about biological mechanisms

Systems biology provides alternatives to the strategies to developing mechanistic explanations traditionally pursued in cell and molecular biology and much discussed in accounts of mechanistic explanation. Rather than starting by identifying a mechanism for a given phenomenon and decomposing it, systems biologists often start by developing cell-wide networks of detected connections between proteins or genes and construe clusters of highly interactive components as potential mechanisms. Using inference strategies such as ‘guilt-by-association’, researchers advance hypotheses about functions performed of these mechanisms. I examine several examples of research on budding yeast, first on what are taken to be enduring networks and subsequently on networks that change as cells perform different activities or respond to different external condition

Probabilistic methods in the analysis of protein interaction networks

Imperial Users onl

Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality

BACKGROUND: Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex) may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. RESULTS: We have developed Genetic Interaction Motif Finding (GIMF), an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast). Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. CONCLUSION: GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic lethality occurs between-pathway rather than within-pathway

Disease re-classi cation via integration of biological networks

Currently, human diseases are classi ed as they were in the late 19th century, by considering only symptoms of the a ected organ. With a growing body of transcriptomic, proteomic, metabolomic and genomics data sets describing diseases, we ask whether the old classi cation still holds in the light of modern biological data. These large-scale and complex biological data can be viewed as networks of inter-connected elements. We propose to rede ne human disease classi cation by considering diseases as systemslevel disorders of the entire cellular system. To do this, we will integrate di erent types of biological data mentioned above. A network-based mathematical model will be designed to represent these integrated data, and computational algorithms and tools will be developed and implemented for its analysis. In this report, a review of the research progress so far will be presented, including 1) a detailed statement of the research problem, 2) a literature survey on relative research topics, 3) reports of on-going work, and 4) future research plans.