Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select truepositive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.Fisher's method ͉ mixture distribution models S ystems biology (1, 2) aims to understand cellular behavior in terms of the spatiotemporal interactions among cellular components, such as genes, proteins, metabolites, and organelles. In systems biology, one typically perturbs a system and, with highthroughput measurements to identify all pertinent elements and their interactions, integrates them into a biological network to understand the system's behavior. As such, systems biology is predicated on the integration of experimental data from an ever increasing number of technologies, such as gene expression arrays, proteomics, and chromatin immunoprecipitation on chip assays (3). Integration achieves one of the most important imperatives of systems biology, namely it reduces the dimensionality of global data to deliver useful information about the system of interest.A major challenge in systems biology is that technologies that globally interrogate biological systems have inherently high falsepositive and false-negative rates (4); thus, each data type alone has a limited utility. The integration of data from different sources provides an effective means to deal with this issue by reinforcing bona fide observations and reducing false negatives. Moreover, because different experimental technologies provide different insights into a system, the integration of multiple data types offers the greatest information about a particular cellular process. For example, gene perturbation experiments (e.g., knockouts or RNA interference) reveal relationships between genes that may imply direct physical interactions or indirect logical interactions. In contrast, chromatin immunoprecipitation chip data can reveal direct protein-DNA interactions or cofacto...

Metabolic control analysis (MCA) provides a quantitative description of substrate flux in response to changes in system parameters of complex enzyme systems. Medical applications of the approach include the following: understanding the threshold effect in the manifestation of metabolic diseases; investigating the gene dose effect of aneuploidy in inducing phenotypic transformation in cancer; correlating the contributions of individual genes and phenotypic characteristics in metabolic disease (e.g., diabetes); identifying candidate enzymes in pathways suitable as targets for cancer therapy; and elucidating the function of "silent" genes by identifying metabolic features shared with genes of known pathways. MCA complements current studies of genomics and proteomics, providing a link between biochemistry and functional genomics that relates the expression of genes and gene products to cellular biochemical and physiological events. Thus, it is an important tool for the study of genotype-phenotype correlations. It allows genes to be ranked according to their importance in controlling and regulating cellular metabolic networks. We can expect that MCA will have an increasing impact on the choice of targets for intervention in drug discovery.

IntroductionMetabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.ObjectivesThis review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.MethodsWe review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.ResultsWe offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.ConclusionsSystems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.