Traditional modeling methods using rate equations and enzyme kinetics aloneare insufficient for large-scale plant modeling. As the development of methods to address the challenges posed by plant systems biology is a priority, plant systems biology should take advantage of current modeling strategies and advances in system-wide analysis that have been developed in efforts to achieve both unicellular and multi-cellular modeling. There are currently two large-scale modeling projects undertaken by E-Cell:e2coli, which aims to simulate the E. coli bacterium, and e-Rice, which aims at simulating the rice plant. e-Rice is one of the first attempts to simulate a whole plant organism. Our preliminary goal is to create a generic model for the basic metabolism in a plant cell that can then be adapted to cells residing at different sites in a rice plant. The current version of the basic model consists of reactions that take place in the chloroplast, mitochondrion, peroxisome, and cytosol. The reconstruction of even a prokaryotic cell poses many challenges.3, 20, 21With plants, a huge amount of data and many data types are involved, complicating the process even further.22-24 Even in the same genome, these data vary depending on the type of organelles, cells, tissues, and organs involving multiple development phases. However, by obtaining cell-wide data at two different levels-the genome for global properties and the metabolome for expressed physical properties, a basic dataset for cell simulation can be prepared for computer simulations. Our endeavor to simulate a whole plant is still in its initial stages where we are developing the tools and algorithms necessary for integrating vast amounts of available data and information. The tools and algorithms being developed are indispensable for reliable and comprehensive large-scale simulations, and can be adapted to various organisms including plants and mammals. In summary, our integrative approach and methods addresses large-scale modeling that is being developed at the Institute for Advanced Biosciences. Our approach for constructing the cell model consists of using 1) the Genome to E-Cell System (GEM System) for modeling pathways based on genomic information, 2) rice metabolome research using capillary electrophoresis mass spectrometry (CE-MS), 3) Atomic Reconstruction of Metabolism (ARM), and 4) the hybrid static/dynamic algorithm for integrating static models based on pathway stoichiometry and dynamic models using kinetics. These applications provide a systematic method for acquiring and integrating various data types for whole cell modeling using the E-Cell simulation environment.
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