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

Noorlin, binti Mohd Ali

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.

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