【博士論文】学術データベース

博士論文 / Development of Genetic Modification Flux with Database for Estimating Metabolic Fluxes of Genetic Mutants 遺伝子変異株の代謝流束を推定するためのデータベース構築と Genetic Modification Fluxソフトウェアの開発

著者

書誌事項

タイトル

Development of Genetic Modification Flux with Database for Estimating Metabolic Fluxes of Genetic Mutants

タイトル別名

遺伝子変異株の代謝流束を推定するためのデータベース構築と Genetic Modification Fluxソフトウェアの開発

著者名

Noorlin binti Mohd Ali

学位授与大学

九州工業大学 (大学ID:0071) (CAT機関ID:KI000844)

取得学位

博士(情報工学)

学位授与番号

甲情工第313号

学位授与年月日

2016-06-30

注記・抄録

In understanding the complexity of a metabolic network structure, flux distribution is the key information to observe as it holds direct representation of cellular phenotype. To examine this, the study on genetically perturbed conditions (e.g. gene deletion/knockout) is one of the useful methods, which significantly contributes to metabolic engineering and biotechnology applications. Currently, metabolic flux analysis (MFA) is proven to be suitable mechanism for specific gene knockout studies, yet the method involves exhaustive computational effort since the calculation are derived by a stoichiometric model of major intracellular reactions and applying mass balances to the intracellular metabolites. Metabolic Flux Analysis (MFA) is widely used to investigate the metabolic fluxes of a variety of cells. MFA is based on the stoichiometric matrix of metabolic reactions and their thermodynamic constraints. The matrix is derived from a metabolic network map, where the rows and columns represent metabolites, chemical/transport reactions, respectively. MFA is very effective in understanding the mechanism of how metabolic networks generate a variety of cellular functions and in rationally planning a gene deletion/amplification strategy for strain improvements. Flux Balance Analysis (FBA) is used to predict the steady-state flux distribution of genetically modified cells under different culture conditions. Minimization of Metabolic Adjustment (MOMA) was developed to predict the flux distributions of gene deletion mutants. FBA and MOMA often lead to incorrect predictions in situations where the constraints associated with regulation of gene expression or activity of the gene products are dominant, because they apply the Boolean logics or its related simple logics to gene regulations and enzyme activities. On the other hand, network-based pathway analyses, elementary modes (EMs) and extreme pathways emerge as alternative ways for constructing a mathematical model of metabolic networks with gene regulations. EM analysis was suggested to be convenient for integrating an enzyme activity profile into the flux distribution. Enzyme Control Fluxes (ECFs) uses the relative enzyme activity profile of a mutant to wild type to predict its flux distribution. In facilitating the analysis of metabolic flux distributions, the support of computational approaches is significantly essential. In addition, the availability of real sample data particularly for further observation, a large number of knockout mutant data provides assistance in enhancing the process. We had presented Genetic Modification Flux (GMF) that predicts the flux distribution of a broad range of genetically modified mutants. The feasibility of GMF to predict the flux distribution of genetic modification mutants is validated on various metabolic network models. The prediction using GMF shows higher prediction accuracy as compared to FBA and MOMA. To enhance the feasibility and usability of GMF, we developed two versions of simulator application with metabolic network database to predict flux distribution of genetically modified mutants. 112 data sets of Escherichia coli (E.coli), Corynebacterium glutamicum (C.glutamicum), Saccharomyces cerevisiae (S.cerevisiae), and Chinese Hamster Ovary (CHO) were registered as standard models.

1: INTRODUCTION AND BACKGROUND|2: MATERIALS AND METHODS|3: RESULT AND DISCUSSION|4: CONCLUSION

平成28年度

九州工業大学博士学位論文(要旨)学位記番号:情工博甲第313号 学位授与年月日:平成28年6月30日

目次

  1. 2017-04-02 再収集 / (index.pdf)
  2. 2017-10-02 再収集 / (index.pdf)

キーワード

Systems Biology, Metabolic Flux Analysis, Metabolic Network Data, Metabolic Flux Estimation, Flux Mutant Prediction, Genetic Mutant Database

各種コード

NII論文ID(NAID)

500000978089

NII著者ID(NRID)
  • 8000001099454
本文言語コード

eng

データ提供元

機関リポジトリ / NDLデジタルコレクション

外部リンク

博士論文 / 九州工業大学 / 情報工学

博士論文 / 九州工業大学

博士論文 / 情報工学

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