國立虎尾科技大學 |

A synthetic biology application in metabolic engineering.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A synthetic biology application in metabolic engineering./
作者:	Anesiadis, Nikolaos.
面頁冊數:	1 online resource (283 pages)
附註:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
Contained By:	Dissertation Abstracts International75-11B(E).
標題:	Chemical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781321086188

A synthetic biology application in metabolic engineering.
Anesiadis, Nikolaos.

A synthetic biology application in metabolic engineering. - 1 online resource (283 pages)

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Since the 1970s, bioprocess engineering has focussed on the optimization of the production of chemicals via biological transformations. In particular, much emphasis has been placed on estimating the optimal process variables to maximize the production of the desired chemical. Notably, engineers were limited to using macroscopic process variables, such as the feed rate of the bioreactor. Optimization involves the trade-off between productivity and yield. High values of both metrics are required for a viable plant; however, the two metrics are in competition. Recently, the emergence of synthetic biology has enabled bioengineers to extend the optimization of bioprocesses from the macroscopic level to the genetic level. With this in mind, we propose a novel synthetic biology approach for bioprocess optimization. Our case study involves a lactic acid-producing Escherichia coli strain with the adh (alcohol dehydrogenase) and pta (phosphotransacetylase) genes deleted. Deletion of these genes increases the yield of lactic acid; but, at the same time, growth rate and productivity decrease drastically. Initially, we introduce the model-based design of an integrated genetic circuit that links a density sensory mechanism to a dynamic genetic controller, and subsequently to bacterial metabolism. In this way, the genetic circuit dynamically controls genes that contribute to growth and productivity. Then, we conduct a mathematical analysis of the model to help us in the initial design and further optimization of the integrated circuit. The analysis can minimize the time required to design and troubleshoot the genetic circuit. Also, the analysis showed that the induction time is the most important process variable we can optimize. Finally, we carried out experimental results in an attempt to utilize the genetic toggle switch as a controller to manipulate genes adh and pta in an ON-OFF fashion. While we expected to observe some growth restoration and productivity improvement, it is common for synthetic biology constructs to behave differently in different environments or strains. Indeed, the experimental results show that our assumption that the genetic toggle switch will restore wild-type levels of adh-pta expression may not be true. In summary, this work introduces a novel synthetic biology approach for the optimization of bioprocesses and attempts a proof of concept implementation of the strategy. Although, the initial implementation was not successful, we have done some troubleshooting with respect to the problems involved and suggestions are given for future experiments.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321086188Subjects--Topical Terms:

555952
Chemical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

A synthetic biology application in metabolic engineering.
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504 $a Includes bibliographical references
520 $a Since the 1970s, bioprocess engineering has focussed on the optimization of the production of chemicals via biological transformations. In particular, much emphasis has been placed on estimating the optimal process variables to maximize the production of the desired chemical. Notably, engineers were limited to using macroscopic process variables, such as the feed rate of the bioreactor. Optimization involves the trade-off between productivity and yield. High values of both metrics are required for a viable plant; however, the two metrics are in competition. Recently, the emergence of synthetic biology has enabled bioengineers to extend the optimization of bioprocesses from the macroscopic level to the genetic level. With this in mind, we propose a novel synthetic biology approach for bioprocess optimization. Our case study involves a lactic acid-producing Escherichia coli strain with the adh (alcohol dehydrogenase) and pta (phosphotransacetylase) genes deleted. Deletion of these genes increases the yield of lactic acid; but, at the same time, growth rate and productivity decrease drastically. Initially, we introduce the model-based design of an integrated genetic circuit that links a density sensory mechanism to a dynamic genetic controller, and subsequently to bacterial metabolism. In this way, the genetic circuit dynamically controls genes that contribute to growth and productivity. Then, we conduct a mathematical analysis of the model to help us in the initial design and further optimization of the integrated circuit. The analysis can minimize the time required to design and troubleshoot the genetic circuit. Also, the analysis showed that the induction time is the most important process variable we can optimize. Finally, we carried out experimental results in an attempt to utilize the genetic toggle switch as a controller to manipulate genes adh and pta in an ON-OFF fashion. While we expected to observe some growth restoration and productivity improvement, it is common for synthetic biology constructs to behave differently in different environments or strains. Indeed, the experimental results show that our assumption that the genetic toggle switch will restore wild-type levels of adh-pta expression may not be true. In summary, this work introduces a novel synthetic biology approach for the optimization of bioprocesses and attempts a proof of concept implementation of the strategy. Although, the initial implementation was not successful, we have done some troubleshooting with respect to the problems involved and suggestions are given for future experiments.
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