• Proceedings of the Korean Society for Applied Microbiology Conference
• /
• 2004.06a
• /
• pp.60-61
• /
• 2004

Metabolic engineering is now a well established discipline, used extensively to determine and execute rational strategies of strain development to improve the performance of micro-organisms employed in industrial fermentations. The basic principle of this approach is that performance of the microbial catalyst should be adequately characterised metabolically so as to clearlyidentify the metabolic network constraints, thereby identifying the most probable targets for genetic engineering and the extent to which improvements can be realistically achieved. In order to harness correctly this potential, it is clear that the physiological analysis of each strain studied needs to be undertaken under conditions as close as possible to the physico-chemical environment in which the strain evolves within the full-scale process. Furthermore, this analysis needs to be undertaken throughoutthe entire fermentation so as to take into account the changing environment in an essentially dynamic situation in which metabolic stress is accentuated by the microbial activity itself, leading to increasingly important stress response at a metabolic level. All too often these industrial fermentation constraints are overlooked, leading to identification of targets whose validity within the industrial context is at best limited. Thus the conceptual error is linked to experimental design rather than inadequate methodology. New tools are becoming available which open up new possibilities in metabolic engineering and the characterisation of complex metabolic networks. Traditionally metabolic analysis was targeted towards pre-identified genes and their corresponding enzymatic activities within pre-selected metabolic pathways. Those pathways not included at the onset were intrinsically removed from the network giving a fundamentally localised vision of pathway functionality. New tools from genome research extend this reductive approach so as to include the global characteristics of a given biological model which can now be seen as an integrated functional unit rather than a specific sub-group of biochemical reactions, thereby facilitating the resolution of complexnetworks whose exact composition cannot be estimated at the onset. This global overview of whole cell physiology enables new targets to be identified which would classically not have been suspected previously. Of course, as with all powerful analytical tools, post-genomic technology must be used carefully so as to avoid expensive errors. This is not always the case and the data obtained need to be examined carefully to avoid embarking on the study of artefacts due to poor understanding of cell biology. These basic developments and the underlying concepts will be illustrated with examples from the author's laboratory concerning the industrial production of commodity chemicals using a number of industrially important bacteria. The different levels of possibleinvestigation and the extent to which the data can be extrapolated will be highlighted together with the extent to which realistic yield targets can be attained. Genetic engineering strategies and the performance of the resulting strains will be examined within the context of the prevailing experimental conditions encountered in the industrial fermentor. Examples used will include the production of amino acids, vitamins and polysaccharides. In each case metabolic constraints can be identified and the extent to which performance can be enhanced predicted

• Journal of Microbiology and Biotechnology
• /
• v.16 no.5
• /
• pp.748-756
• /
• 2006

We propose a genome-scale clustering approach to identify whole genome relationships using the functional groups given by the Kyoto Encyclopedia of Genes and Genomes Orthology (KO) database. The metabolic capabilities of each organism were defined by the number of genes in each functional category. The archaeal, bacterial, and eukaryotic genomes were compared by simultaneously applying a two-step clustering method, comprised of a self-organizing tree algorithm followed by unsupervised hierarchical clustering. The clustering results were consistent with various phenotypic characteristics of the organisms analyzed and, additionally, showed a different aspect of the relationship between genomes that have previously been established through rRNA-based comparisons. The proposed approach to collect and cluster the metabolic functional capabilities of organisms should make it a useful tool in predicting relationships among organisms.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.8 no.3
• /
• pp.183-186
• /
• 2003

The artificial gene coding for anticoagulant hirudin was placed under the control of the GAL 10 promoter and expressed in the galactokinase-deficient strain (Δgal1) of Saccharomyces cerevisiae, which uses galactose only as a gratuitous inducer in order to avoid its consumption. For efficient production of recombinant hirudin, a carbon source other than galactose should be provided in the medium to support growth of the Δgal1 strain. Here we demonstrate the successful use of glucose in the fed-batch fermentation of the Δgal1 strain to achieve efficient production of recombinant hirudin, with a yield of up to 400 mg hirudin/L.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.10 no.5
• /
• pp.385-399
• /
• 2005

The phenotypic response of a cell results from a well orchestrated web of complex interactions which propagate from the genetic architecture through the metabolic flux network. To rationally design cell factories which carry out specific functional objectives by controlling this hierarchical system is a challenge. Transcriptome analysis, the most mature high-throughput measurement technology, has been readily applied In strain improvement programs in an attempt to Identify genes involved in expressing a given phenotype. Unfortunately, while differentially expressed genes may provide targets for metabolic engineering, phenotypic responses are often not directly linked to transcriptional patterns, This limits the application of genome-wide transcriptional analysis for the design of cell factories. However, improved tools for integrating transcriptional data with other high-throughput measurements and known biological interactions are emerging. These tools hold significant promise for providing the framework to comprehensively dissect the regulatory mechanisms that identify the cellular control mechanisms and lead to more effective strategies to rewire the cellular control elements for metabolic engineering.

• Journal of Microbiology and Biotechnology
• /
• v.8 no.1
• /
• pp.42-48
• /
• 1998

In order to maximize the secretory expression of human serum albumin (HSA) in the yeast Saccharomyces cerevisiae, a series of HSA expression vectors were constructed with a combination of different promoters, 5' untranslated regions (5'UTR), and secretion signal sequences. The expression vector composed of the galactose-inducible promoter GALl0, the natural 5'UTR, and the natural signal sequence of HSA directed the most efficient expression and secretion of HSA among the constructed vectors when introduced into several S. cerevisiae strains. Although the major form of HSA expressed and secreted in the yeast transformants was the mature form of 66 kDa, the truncated form of 45 kDa was also detected both in the cell extract and in the culture supernatant. The level of the intact HSA protein in the culture supernatant reached up to 30 mg/l at 24 h of cultivation in a shake-flask culture but began to decrease afterwards, indicating that the secreted HSA protein was unstable in a prolonged culture of yeast.

• 한국생물공학회:학술대회논문집
• /
• 2005.04a
• /
• pp.426-426
• /
• 2005

• KSBB Journal
• /
• v.24 no.3
• /
• pp.287-290
• /
• 2009

Metabolic network is composed of more than thousands of metabolic reactions. Therefore, understanding of metabolic behavior of microorganisms is required to engineer metabolism of microorganisms. In this paper, we employed ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) to quantify relationships among metabolic subpathways. The results showed that ARACNE analysis can give new insight into the study of bacterial metabolism.

• 한국생물공학회:학술대회논문집
• /
• 2005.04a
• /
• pp.11-11
• /
• 2005

• 한국생물공학회:학술대회논문집
• /
• 2005.04a
• /
• pp.587-588
• /
• 2005

• 한국생물공학회:학술대회논문집
• /
• 2005.10a
• /
• pp.795-796
• /
• 2005