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Computational design of microbial strains for nongrowth-associated production of antibiotics and oleochemicals : 컴퓨터를 이용한 항생제와 유지화학품의 비성장형 생산을 위한 미생물 균주의 설계

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dc.contributor.advisor김병기-
dc.contributor.author김민석-
dc.date.accessioned2017-07-13T08:46:22Z-
dc.date.available2017-07-13T08:46:22Z-
dc.date.issued2017-02-
dc.identifier.other000000141214-
dc.identifier.urihttps://hdl.handle.net/10371/119828-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2017. 2. 김병기.-
dc.description.abstractOver the past decade, a number of genome-scale models of metabolism (GEMs) and computational strain optimization methods (CSOMs) have been developed to guide metabolic engineering of microbial strains for the production of valuable chemicals. In this thesis, the approach has been extended to be applicable for computational design of microbial strains for nongrowth-associated production. Production of antibiotics in Streptomyces coelicolor and oleochemicals in Yarrowia lipolytica has been investigated using the developed computational tools.
First, antibiotics production in S. coelicolor has been studied by reconstructing a high-quality GEM for S. coelicolor, designated iMK1208. It has been verified that iMK1208 can be used for designing an antibiotic overproducing strain using an existing CSOM named flux scanning based on enforced objective flux. To more precisely design antibiotic overproducers by considering regulatory constraints which governing the production of antibiotics, two new CSOMs, transcriptomics-based strain optimization tool (tSOT) and beneficial regulator targeting (BeReTa), have been devised. tSOT identifies metabolic gene overexpression targets by considering regulatory states through the integration of transcriptomic data into a GEM. On the other hand, BeReTa prioritizes transcriptional regulator manipulations for target chemical production by using transcriptional regulatory network model together with a GEM. Finally, tSOT and BeReTa have been successfully applied for designing antibiotic overproducing strains of S. coelicolor using iMK1208.
Second, to examine oleochemicals production in Y. lipolytica, environmental version of minimization of metabolic adjustment (eMOMA) method has been developed. After confirming that the eMOMA method can be used for predicting lipid accumulation as well as metabolic fluxes of Y. lipolytica, the method has been further applied to find metabolic engineering strategies for the improved lipid production. Using the eMOMA method, several known targets as well as novel non-intuitive targets for lipid overproduction have been successfully identified.
In short, the GEM and CSOMs presented herein would be powerful tools for guiding the development of production hosts for nongrowth-associated products.		-
dc.description.tableofcontentsChapter 1. Introduction 1
1.1 Nongrowth-associated production of chemicals 2
1.1.1 Streptomyces for antibiotic production 4
1.1.2 Yarrowia lipolytica for oleochemical production 7
1.2 Constraint-based reconstruction and analysis (COBRA) 12
1.2.1 Genome-scale model of metabolism (GEM) 14
1.2.2 Prediction of cellular phenotypes 14
1.2.2.1 Flux balance analysis (FBA) 16
1.2.2.2 Minimization of metabolic adjustment (MOMA) 19
1.2.3 Incorporation of regulatory constraints 19
1.2.3.1 Integration of transcriptomic data 20
1.2.3.2 Integrated model of regulation and metabolism 23
1.2.4 Computational strain design 25
1.3 The scope of thesis 30
Chapter 2. Reconstruction of a high-quality metabolic model enables the identification of gene overexpression targets for enhanced antibiotic production in Streptomyces coelicolor A3(2) 35
2.1 Abstract 36
2.2 Introduction 37
2.3 Materials and methods 40
2.3.1 Metabolic model reconstruction 40
2.3.2 FBA 41
2.3.3 Bacterial strains and culture conditions 42
2.3.4 Measurement of ACT production 43
2.4 Results and discussion 44
2.4.1 Updates to the biomass equation 44
2.4.2 Updates to the stoichiometric matrix 48
2.4.3 Updates to energetic parameters 51
2.4.4 Validation of the model 54
2.4.5 Strain design for antibiotic overproduction 57
2.5 Concluding remarks 61
Chapter 3. Transcriptomics-based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor 62
3.1 Abstract 63
3.2 Introduction 64
3.3 Materials and methods 67
3.3.1 GEM and dataset used for the study 67
3.3.2 Evaluation of algorithms for integrating transcriptomic data 67
3.3.3 Transcriptomics-based strain optimization tool (tSOT) 69
3.3.4 Bacterial strains and culture conditions 71
3.3.5 Measurement of ACT production 74
3.4 Results 75
3.4.1 Evaluation of algorithms for integrating transcriptomic data 75
3.4.2 Development of transcriptomics-based strain optimization tool (tSOT) 77
3.4.3 Application of tSOT for ACT overproducer design 77
3.5 Discussion 84
Chapter 4. BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals 88
4.1 Abstract 89
4.2 Introduction 90
4.3 Methods 93
4.3.1 BeReTa algorithm 93
4.3.1.1 Regulatory strength matrix 93
4.3.1.2 Flux slope vector 96
4.3.1.3 Beneficial score 98
4.3.1.4 Permutation test 99
4.3.1.5 Target criteria 100
4.3.2 Network models and datasets 101
4.3.2.1 Genome-scale metabolic network 101
4.3.2.2 Transcriptional regulatory network 101
4.3.2.3 Expression dataset 103
4.4 Results 104
4.4.1 Production of chemicals in E. coli 104
4.4.2 Production of antibiotics in S. coelicolor 107
4.4.3 Comparison of BeReTa with OptORF 108
4.5 Discussion 113
Chapter 5. Predicting metabolic engineering strategies for improved lipid production in Yarrowia lipolytica 116
5.1 Abstract 117
5.2 Introduction 119
5.3 Methods 123
5.3.1 FBA 123
5.3.2 eMOMA 124
5.3.3 Designing mutant strains using eMOMA 125
5.3.4 Model and simulation conditions 126
5.4 Results and discussion 128
5.4.1 Predicting phenotypes of Y. lipolytica in nutrient-limited conditions 128
5.4.2 Predicting metabolic engineering strategies for improved lipid production 135
5.5 Conclusion 143
Chapter 6. Concluding Remarks 144
6.1 Algorithmic developments achieved in this thesis 145
6.2 Further applications of the developed CSOMs 149
6.3 Further algorithmic developments required in the future 152
Appendix 154
A Supplementary Materials for Chapter 2 155
A.1 Supplementary Methods 155
A.2 Supplementary Tables 158
A.3 Supplementary Figures 232
A.4 MATLAB codes used for the study 234
B Supplementary Materials for Chapter 3 237
B.1 Supplementary Results 237
B.2 Supplementary Figures 238
B.3 MATLAB codes used for the study 239
C Supplementary Materials for Chapter 4 247
C.1 Supplementary Results 247
C.2 Supplementary Tables 248
C.3 Supplementary Figures 258
C.4 MATLAB codes used for the study 259
D Supplementary Materials for Chapter 5 268
D.1 Supplementary Tables 268
D.2 Supplementary Figures 280
D.3 MATLAB codes used for the study 281
References 288		-
dc.formatapplication/pdf-
dc.format.extent8541748 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectGenome-scale model-
dc.subjectComputational strain design-
dc.subjectNongrowth-associated product-
dc.subjectAntibiotics-
dc.subjectStreptomyces-
dc.subjectOleochemicals-
dc.subjectYarrowia lipolytica-
dc.subject.ddc660-
dc.titleComputational design of microbial strains for nongrowth-associated production of antibiotics and oleochemicals-
dc.title.alternative컴퓨터를 이용한 항생제와 유지화학품의 비성장형 생산을 위한 미생물 균주의 설계-
dc.typeThesis-
dc.contributor.AlternativeAuthorMinsuk Kim-
dc.description.degreeDoctor-
dc.citation.pages318-
dc.contributor.affiliation공과대학 화학생물공학부-
dc.date.awarded2017-02-
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