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At present, we are attempting to develop basic analytical methods, a database, and search tools for lipidomics, i.e., metabolomics focusing on lipids. Further, in order to develop in new analytical methods and identification tools, we believe that our efforts will be successfuly only through collaboration with numerous cooperating researchers and application personnel of MS manufacturers. Analysis by ESI-MS can be applied to elucidate lipid mediators in the cells as well as for the purposes of detection and quantification, and for localization in the cells of minor lipid metabolites. Bioinformatics studies based on both data of proteome of enzymes and receptors, as well as metabolome of lipids, will make it possible to acquire new knowledge on the metabolic and signaling pathways of lipid metabolism.

The focused analysis for the metabolites obtained by rough separation with the differences in solubility compared to the extraction solvent is very effective, because further separation such as by LC or CE conditions suitable for the chemical properties of these metabolites is more easily selected. This is true for all metabolomics studies, especially in the detection of minor components. By using the differences in solubility, metabolites can be categorized into different groups of chemical and physiological nature. For each specified category of the metabolites, specified fields of metabolomics such as for peptidome, glycome, and lipidome can exist. It is very important that new strategies of analytical methods and databases for each of these categories of metabolites are created.

The ARM database provides a unique function to digitize the atom-to-atom correspondences between metabolites in identified biochemical reactions. Currently, no other metabolic database can trace biochemical pathways at the atomic scale. The technique used can be extended to other pathway searches in which molecular structures are stepwise transformed. Such an application includes the design of bio-processes for useful materials such as amino acids or plastics.

In the train network, the primary criterion to select a route is the distance (i.e., shortest paths are chosen). In the evolutionary process of metabolic networks, however, it seems that this criterion has not been much exercised: not a few metabolic pathways include redundant steps and are not optimized in terms of length or robustness. The systems biology on metabolic networks is still at its earliest stage, and will become more important in future biological research.

Metabolic profiling by FT-ICR-MS and ESI-Q-TOF-MS is an effective method to capture a rough image of metabolism. It should be performed after the accuracy is confirmed by using the authentic sample after an elaborate calibration is done, when an individual metabolite is identified from the accurate mass for FT-ICR-MS. Moreover, when ESI-Q-TOF-MS is used, the CID spectrum should also be measured, and compared with that of the authentic sample.

There are bright prospects for the modeling of metabolic systems. However, in most cells except cells in which no genetic expression at all occurs, such as human erythrocytes, metabolism is controlled by genes. That is, the maximum activity of each enzyme is regulated by the gene expression, and changes dynamically according to the circumstance. Moreover, because one group of enzyme genes is not expressed under normal conditions, some cases in each pathway are expunged. In such cases and only under specific conditions, the enzyme group associated with that pathway is expressed and operated. It is therefore unlikely the metabolic pathway map created with the aid of data mining from genome sequencing using pathway databases and the GEM system could be applied in its present form. That is, it is not likely that detailed cell simulation could be obtained through metabolic modeling only; rather, the cell simulation would likely be a combination of a gene expression model and signal transmission model controlling it.

Although the principles differ from this hybrid method for metabolic systems, we are currently developing a new method to enable modeling of gene expression systems and signal transduction pathways.

By far, the most important lesson to take away from this chapter is the following:

Don’t get caught up in form over substance!

The model that you produce is only a means to an end . . . and the process, notation, and tools that you use to produce the model are but a to the means to this end. If you get too hung up on which notation to use, or which process to use, or which tool to use, you may wind up spinning your wheels in “analysis paralysis.” Don’t lose sight of your ultimate goal: to build .