A team of pioneering South Korean scientists has developed a new strategy for accurately predicting cellular metabolic fluxes under various genotypic and environmental conditions. This groundbreaking research is published in the journal Proceedings of the National Academy of Sciences of the USA (PNAS) on August 2, 2010.
To understand cellular metabolism and predict its metabolic capability at systems-level, systems biological analysis by modeling and simulation of metabolic network plays an important role. The team from the Korea Advanced Institute of Science and Technology (KAIST), led by Distinguished Professor Sang Yup Lee, focused their research on the development of a new strategy for more accurate prediction of cellular metabolism.
“For strain improvement, biologists have made every effort to understand the global picture of biological systems and investigate the changes of all metabolic fluxes of the system under changing genotypic and environmental conditions,” said Lee. The accumulation of omics data, including genome, transcriptome, proteome, metabolome, and fluxome, provides an opportunity to understand the cellular physiology and metabolic characteristics at systems-level. With the availability of the fully annotated genome sequence, the genome-scale in silico (means “performed on computer or via computer simulation.”) metabolic models for a number of organisms have been successfully developed to improve our understanding on these biological systems. With these advances, the development of new simulation methods to analyze and integrate systematically large amounts of biological data and predict cellular metabolic capability for systems biological analysis is important.
Information used to reconstruct the genome-scale in silico cell is not yet complete, which can make the simulation results different from the physiological performances of the real cell. Thus, additional information and procedures, such as providing additional constraints (constraint: a term to exclude incorrect metabolic fluxes by restricting the solution space of in silico cell) to the model, are often incorporated to improve the accuracy of the in silico cell.
By employing information generated from the genome sequence and annotation, the KAIST team developed a new set of constraints, called Grouping Reaction (GR) constraints, to accurately predict metabolic fluxes. Based on the genomic information, functionally related reactions were organized into different groups. These groups were considered for the generation of GR constraints, as condition- and objective function- independent constraints. Since the method developed in this study does not require complex information but only the genome sequence and annotation, this strategy can be applied to any organism with a completely annotated genome sequence.
“As we become increasingly concerned with environmental problems and the limits of fossil resources, bio-based production of chemicals from renewable biomass has been receiving great attention. Systems biological analysis by modeling and simulation of biological systems, to understand cellular metabolism and identify the targets for the strain improvement, has provided a new paradigm for developing successful bioprocesses,” concluded Lee. This new strategy for predicting cellular metabolism is expected to contribute to more accurate determination of cellular metabolic characteristics, and consequently to the development of metabolic engineering strategies for the efficient production of important industrial products and identification of new drug targets in pathogens.”