Abstract
The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.
Highlights
Systems biology has become an important research field to fully understand the complex metabolism of cells in living organisms in toto (Kitano, 2002a,b; Aderem, 2005; Kirschner, 2005)
We address the basic concepts of advancements in both metabolomics and mathematical modeling
We review the methods for metabolome analyses and nature of metabolome data (Section Nature of Metabolome Data) as well as current mathematical modeling approaches (Section Current Status of Modeling Metabolic Reaction Networks), and describe the procedures to construct a kinetic model from those data
Summary
Systems biology has become an important research field to fully understand the complex metabolism of cells in living organisms in toto (Kitano, 2002a,b; Aderem, 2005; Kirschner, 2005). A complete understanding of metabolism through use of metabolome data is greatly anticipated This involves the development of high-throughput analytical instruments to provide a wide range of metabolic profiles along with the improvement of data processing techniques to accurately identify or annotate metabolites from mass spectra and precisely measure their quantities in a living cell (Fiehn, 2002; Weckwerth, 2003; Bain et al, 2009). This information can be implemented to advance intuitive and functional concepts for designing and engineering ideal metabolic systems. We pinpoint the potential of combining mathematical techniques to construct a large-scale dynamic model for further understanding of biological systems
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