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The Role of High-throughput Transcriptome Analysis in Metabolic Engineering
Jens Nielsen,Ana Paula Oliveira,Kiran Raosaheb Patil,Michael C. Jewett 한국생물공학회 2005 Biotechnology and Bioprocess Engineering Vol.10 No.5
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 comprehen-sively dissect the regulatory mechanisms that identify the cellular control mechanisms and lead to more ef-fective strategies to rewire the cellular control elements for metabolic engineering.
Systems Biology of Yeast Metabolism
Jens NIELSEN 한국생물공학회 2021 한국생물공학회 학술대회 Vol.2021 No.10
Metabolic Engineering relies on a thorough understanding of how the many different metabolic reactions in the cell to be engineered interacts. Genome-scale metabolic models offers a very strong tool for performing quantitative analysis of how the many different reactions in the metabolic network interacts, and through the addition of kinetic and proteome constraints to these models their predictive strength has significantly improved. However, these models can also be used for integrative analysis of quantitative data, e.g. proteomics and metabolomics data. In the lecture there will be presented progress on how kinetic and proteome constraints can improve the predictive strength of genome-scale metabolic models for use in metabolic engineering. Examples will be given of both identification of novel metabolic engineering designs and of using these models for gaining novel insight into the functioning of metabolism.
Further theoretical and practical insight to the do-validated bandwidth selector
Enno Mammen,María Dolores Martínez Miranda,Jens Perch Nielsen,Stefan Sperlich 한국통계학회 2014 Journal of the Korean Statistical Society Vol.43 No.3
Recent contributions to kernel smoothing show that the performance of cross-validatedbandwidth selectors improves significantly from indirectness and that the recent dovalidatedmethod seems to provide the most practical alternative among these methods. Inthis paper we show step by step how classical cross-validation improves in theory, as wellas in practice, from indirectness and that do-validated estimators improve in theory, butnot in practice, from further indirectness. This paper therefore provides a strong support forthe practical and theoretical properties of do-validated bandwidth selection. Do-validationis currently being introduced to survival analysis in a number of contexts and this paperprovides evidence that this might be the immediate step forward.