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系統識別號 U0026-2807201915593900
論文名稱(中文) 以代謝通量分析評估厭氧混菌發酵系統之表現
論文名稱(英文) Metabolic flux analysis for anaerobic mixed-cultural fermentation systems
校院名稱 成功大學
系所名稱(中) 環境工程學系
系所名稱(英) Department of Environmental Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 程海軒
研究生(英文) Hai-Hsuan (HenryME) Cheng
學號 P58031016
學位類別 博士
語文別 英文
論文頁數 149頁
口試委員 指導教授-黃良銘
口試委員-林志高
口試委員-高志明
口試委員-張嘉修
口試委員-陳文興
口試委員-于昌平
中文關鍵字 混菌系統  微生物代謝  厭氧生物程序  廢水處理  質量平衡 
英文關鍵字 mixed-culture  anaerobic process  wastewater treatment  microbial metabolism  mass balance 
學科別分類
中文摘要 生物方法中,厭氧程序不僅能降低廢水處理過程中所產生的汙泥量,其能源或經濟產物 (包括生質氣體:氫氣、甲烷,生質燃料或化學品:乙醇、乙酸、丁醇,甚至是生物塑膠等等) 皆能被簡易或特定已知方法分離,也因此漸漸被廣泛使用。然而,厭氧程序中,大量副產物 (例如:甲酸、乙酸、丁酸、乳酸等) 的產生常使得目標產物的產量大幅降低,在不同環境下,細胞在代謝過程中電子的流向分布改變,或者不同微生物彼此之間的影響與增減,皆會使各產物的產量有所變化,故因此如何提高特定產物的產量便十分重要。然而,直至今日卻並未發展出能簡易卻有效描述甚至控制其代謝的策略工具,導致反應槽的設計與操控如此發達的今日,厭氧生物程序仍無法做為穩定而常見的廢水處理程序單元。代謝通量分析是基於化學反應式與質量平衡的工具,能有效分析細胞體內代謝的流向分布,然而,過去代謝通量分析僅侷限於純菌研究,且大多僅應用於微生物代謝工程,在實廠規模的操作下,欲維持反應槽微生物的單一性並不現實,不少研究也證實混菌系統較純菌系統穩定,故建立針對混菌系統的代謝分析方法便是本研究的重點。
在此條件下,本研究引用通用細胞的概念,將整個混菌系統考慮成一全能微生物,建立包含大多數厭氧程序的混菌代謝網路,再蒐集整理研究過程中操作厭氧生物反應器以及文獻中記載之實驗數據,成功進行微生物代謝通量分析。藉由純菌操作的產氫反應槽數據,微生物代謝通量分析顯示本研究所建立之代謝網路可用於厭氧程序,並說明了添加蛋白腖 (peptone) 與否對產氫的影響。利用微生物代謝通量分析統整數個產氫發酵槽的數據,結果顯示產氫的通量在系統產生乳酸時與鐵氧還蛋白的還原及乙酸生成有正相關,但在消耗乳酸時則反之。在丁醇發酵的部分,微生物代謝通量分析顯示產生丁醇的通量會隨著產生乙酸與丁酸的通量下降而上升,顯示降低有機酸的通量能有效提高丁醇產量。在針對乙酸化反應槽的微生物代謝通量分析中,反向的五碳糖磷酸化是發酵時消耗二氧化碳的主要途徑,同時其共同發生的糖解作用則能產生足夠能量 (ATP) 以供乙酸化時使用 (Wood-Ljungdahl pathway)。在甲烷化的部分,微生物代謝通量分析能成功表達反應槽中嗜氫甲烷化與嗜乙酸甲烷化的分佈。整體而言,本研究證實了針對混菌系統的微生物代謝通量分析能有效運用於不同發酵程序上,並能提供額外的訊息,若輔以操作條件等資訊,微生物代謝通量分析將能做為未來厭氧發酵的操作上的參考。
英文摘要 As the global warming and energy crisis become serious and unneglectable, bioenergies production for wastewater treatment via anaerobic digestion poses an alternative options. Anaerobic digestion could potentially produce H2, ethanol, butanol, CH4 and other volatile fatty acids from organic wastewaters, however, difficulties remain to produce pure product or even to enhance the yield due to the complex metabolic network in anaerobic digestion. Until now, developing a tool to describe or even to control the complex flux distribution between various metabolites during anaerobic digestion is still an unsolved task, making anaerobic digestion an unstable and hardly applicable process for wastewater treatment. Metabolic flux analysis (MFA) is a method based on stoichiometry and mass balance, which was mostly used for metabolic engineering with pure culture system.
In this study, a mixed-cultural metabolic network for anaerobic digestion was constructed by involving the concept of “universal bacterium”, and the proposed network was used for MFA to evaluate the electron and material flows during different anaerobic processes under various conditions. The mixed-cultural metabolic network, consisting of glycolysis, pentose phosphorylation pathway (PPP), lactate branch, acetyl-CoA branches (including acidogenesis and solventogenesis), TCA cycle, and post-acidogenesis branches (such as acetogenesis and methanogenesis, was successfully applied to MFA using the results from H2 bioreactors, acetone-butanol-ethanol fermentative bioreactors, acetogenesis bioreactors and methanogenesis bioreactors.
Results obtained from the MFAs applying to H2 bioreactors fed with cellulose, lactate and acetate, and three different types of bioethanol fermented residues showed that PPP, lactate branch, and TCA cycle are important to regulate NADH and could potentially affect H2 production. Pearson’s correlation analysis shows that the flux of H2 production had positive correlations with the reduction of ferredoxin with pyruvate oxidation, acetate formation and its emission when lactate was produced in the system, while negative relationships were found between the flux of H2 production and these three fluxes. Results of MFA from ABE bioreactors indicate that the flux toward butanol increased with decreasing fluxes for acetate and butyrate, while batch experiments also show the addition of butyrate could enhance butanol yield. The MFA results of two bioreactors for acetogenesis from H2/CO2 indicate that PPP is an important flux since its negative flux acted as a CO2 sink, leading to ATP production in glycolysis which could be used in Wood-Ljungdahl pathway, the conversion of H2 and CO2 to acetyl-CoA. During the investigation of CH4 bioreactor, MFA result successfully identified and described the dominance shift between hydrogenotrophic methanogenesis and aceticlastic methanogenesis.
論文目次 Abstract I
中文摘要 III
致謝 V
Table of Contents XIII
Table of tables XVI
Table of figures XVIII
List of abbreviations XX
Chapter 1 Introduction 1
Chapter 2 Literature Review 3
2.1 Background and Introduction 3
2.1.1 Renewable energies and bioenergy 3
2.1.2 Brief overview of wastewater treatment 4
2.1.3 Current development of anaerobic wastewater treatment 6
2.1.4 Bioenergy or bioproducts from wastewaters 10
2.2 General pathways and mechanisms of anaerobic process 15
2.2.1 Acidogenesis and solventogenesis 17
2.2.2 Acetogenesis 20
2.2.3 Methanogenesis 22
2.3 Proteins involved in anaerobic digestion 24
2.3.1 Glycolysis (including pentose phosphate) 28
2.3.2 Lactate branch 29
2.3.3 Acetyl-CoA branch (acidogenesis) 30
2.3.4 Acetyl-CoA branch (solventogenesis) 31
2.3.5 Acetogenesis 32
2.3.6 Methanogenesis 33
2.3.7 Tricarboxylic acid cycle (TCA cycle) 34
2.4 Factors that affect anaerobic digestion 36
2.4.1 pH 36
2.4.2 ATP and NAD(P)H 37
2.4.3 Volatile fatty acid and alcohols 38
2.4.4 Metal ions 39
2.4.5 Inorganic compounds 40
2.4.6 Substrates and inoculums 40
2.5 Metabolic flux analysis (MFA) 41
2.5.1 Constraint-based modeling 41
2.5.2 Metabolic flux analysis and flux balance analysis 43
2.5.3 Applications of MFA on anaerobic processes 44
Chapter 3 Materials and Methods 47
3.1 Data collection and calculation 47
3.1.1 Data used in MFA for H2 production 47
3.1.2 Data used in MFA for ABE fermentation 52
3.1.3 Data used in MFA for acetogenesis 54
3.1.4 Data used in MFA for CH4 production 55
3.2 Construction of mixed-culture metabolic network 56
3.3 Assumptions for MFA 61
3.4 CellNetAnalyzer 62
3.5 Post analyses on MFA results 62
Chapter 4 Results and Discussion 63
4.1 Validation of metabolic network 63
4.1.1 MFA of H2 production from glucose/peptone 63
4.1.2 MFA of H2 production from glucose 66
4.1.3 Comparison of GP and GA H2 bioreactors 68
4.2 Applying mixed-cultural MFA to H2 bioreactors 69
4.2.1 MFA of H2 production from cellulose using Clostridium Cellulyticum 69
4.2.2 MFA of mixed-cultural H2 production from lactate/acetate 73
4.2.3 MFA of H2 production from bioethanol fermented residues (BEFRs) 76
4.2.3.1 MFA of H2 production from tapioca starch BEFR 76
4.2.3.2 MFA of H2 production from rice straw BEFR 80
4.2.3.3 MFA of H2 production from bagasse BEFR 82
4.2.4 Relationships of metabolites and H2 production 84
4.3 Applying mixed-cultural MFA to ABE fermentation 88
4.3.1 MFA of ABE production from glucose using Clostridium acetobutylicum 88
4.3.2 MFA of ABE production from lactose using Clostridium acetobutylicum 89
4.3.3 MFA of ABE production from starch using Clostridium saccharobutylicum 92
4.3.4 Relationships of fluxes between metabolites and butanol production 96
4.4 Applying mixed-cultural MFA to acetogenesis 100
4.4.1 MFA of acetogenesis bioreactor using Clostridium ljungdahlii 100
4.4.2 MFA of acetogenesis bioreactor using Acetobacterium woodii 101
4.4.3 Brief discussion on the MFA results from acetogenesis bioreactors 101
4.5 Applying mixed-cultural MFA to CH4 bioreactor 105
4.5.1 MFA of CH4 bioreactor treating butyrate-based synthetic wastewater 105
4.5.2 MFA of CH4 bioreactor treating H2 fermentation effluent 106
4.6 Energy balance analysis 110
4.6.1 NADH balance in H2 bioreactors 110
4.6.2 ATP balance on H2 bioreactors 113
4.6.3 NADH balance on ABE bioreactors 115
4.6.4 ATP balance on ABE bioreactors 117
4.6.5 NADH balance on post-acidogenesis bioreactors 119
4.6.6 ATP balance on post-acidogenesis bioreactors 121
Chapter 5 Conclusion and Suggestions 123
Chapter 6 Reference 125
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