東華大學圖書館 |

Engineering the Emergent Properties of Complex Communities.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Engineering the Emergent Properties of Complex Communities./
作者:	Chang, Chang-Yu.
面頁冊數:	1 online resource (189 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Ecology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30250195click for full text (PQDT)
ISBN:	9798379783914

Engineering the Emergent Properties of Complex Communities.
Chang, Chang-Yu.

Engineering the Emergent Properties of Complex Communities. - 1 online resource (189 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--Yale University, 2023.

Includes bibliographical references

Microorganisms perform critical functions throughout the biosphere and in biotechnology: from the biogeochemical cycle to the production of sourdough. The overwhelming majority of these functions are usually emergent from the collective outcomes of a large number of microorganisms living in a diverse community. Controlling and manipulating the functions of complex microbial communities is a major aspiration in modern biology. However, we lack a quantitative understanding of how often these functions are emergent, and when it occurs, how we can engineer the emergent properties of microbial communities. In this dissertation, I combine computational approaches with experiments using microbial communities to explore the challenges of predicting species coexistence from a bottom-up perspective and propose ideas on extending evolutionary engineering to design microbial consortia with desired functions.In Chapter 1, I present a draft manuscript in which I address a long-standing conundrum in ecology: is species coexistence primarily a reductionist pairwise phenomenon, or is it an emergent property of the whole community? To address this question, this chapter combines microbial community enrichment and pairwise competition experiments, with the aid of computational practices including image processing and machine learning. It shows that the whole community coexists despite competitive exclusion prevailing at the pairwise level. In addition, across these communities, the pairwise exclusion is consistently arranged in a hierarchical instead of a non-transitive structure. These results suggest that species coexistence is an emergent property of microbial communities and highlight the challenge of predicting species coexistence from a bottom-up perspective without taking into account the community context.In Chapter 2, I present a published paper that tackles the empirical challenges of artificial ecosystem selection. Artificial selection is a promising approach to manipulating microbial communities. In this chapter, I experimentally implemented a propagule selection protocol, where the best-performing communities are used as the inocula to generate a new generation of communities. I found that while propagule selection can effectively purge low-performing communities, two limitations arise in this commonly used protocol: (1) response to selection stops soon after the heritable variation is exhausted in a few selection rounds, (2) propagule selection does not increase the function of the best-performing community. Addressing these limitations could inform the design of future studies intending to artificially select microbial communities.In Chapter 3, I present a published paper in which I address the evolutionary engineering of complex microbial communities. Several previous studies (including my own presented in Chapter 2) have attempted to engineer microbial communities but with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection has been limited, particularly for large assemblages of asexually reproducing microorganisms. In this chapter, I developed a flexible modeling framework that allows us to probe any number of arbitrary protocols of artificial selection. Using this model, I found that a simple screen control with no selection outperforms most protocols proposed to date, which do not allow the communities to reach their ecological equilibrium. Inspired by the directed evolution of biochemical macro-molecules, I then identified a range of selection strategies that are well-suited for the top-down engineering of complex, diverse, and stable microbial consortia.The works presented here illustrate how bottom-up and top-down approaches inspired by synthetic biology can be combined to develop a predictive and quantitative understanding of the emergent properties of ecological communities. While focusing on the microbial communities, the concepts developed apply to other biological organizations engaging in interspecific ecological interactions with heritable functional variations. The work I have presented highlights the limitations and strategies that could inform future designs on the engineering of complex microbial communities.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379783914Subjects--Topical Terms:

516476
Ecology.
Subjects--Index Terms:

Artificial selectionIndex Terms--Genre/Form:

542853
Electronic books.

Engineering the Emergent Properties of Complex Communities.
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520 $a Microorganisms perform critical functions throughout the biosphere and in biotechnology: from the biogeochemical cycle to the production of sourdough. The overwhelming majority of these functions are usually emergent from the collective outcomes of a large number of microorganisms living in a diverse community. Controlling and manipulating the functions of complex microbial communities is a major aspiration in modern biology. However, we lack a quantitative understanding of how often these functions are emergent, and when it occurs, how we can engineer the emergent properties of microbial communities. In this dissertation, I combine computational approaches with experiments using microbial communities to explore the challenges of predicting species coexistence from a bottom-up perspective and propose ideas on extending evolutionary engineering to design microbial consortia with desired functions.In Chapter 1, I present a draft manuscript in which I address a long-standing conundrum in ecology: is species coexistence primarily a reductionist pairwise phenomenon, or is it an emergent property of the whole community? To address this question, this chapter combines microbial community enrichment and pairwise competition experiments, with the aid of computational practices including image processing and machine learning. It shows that the whole community coexists despite competitive exclusion prevailing at the pairwise level. In addition, across these communities, the pairwise exclusion is consistently arranged in a hierarchical instead of a non-transitive structure. These results suggest that species coexistence is an emergent property of microbial communities and highlight the challenge of predicting species coexistence from a bottom-up perspective without taking into account the community context.In Chapter 2, I present a published paper that tackles the empirical challenges of artificial ecosystem selection. Artificial selection is a promising approach to manipulating microbial communities. In this chapter, I experimentally implemented a propagule selection protocol, where the best-performing communities are used as the inocula to generate a new generation of communities. I found that while propagule selection can effectively purge low-performing communities, two limitations arise in this commonly used protocol: (1) response to selection stops soon after the heritable variation is exhausted in a few selection rounds, (2) propagule selection does not increase the function of the best-performing community. Addressing these limitations could inform the design of future studies intending to artificially select microbial communities.In Chapter 3, I present a published paper in which I address the evolutionary engineering of complex microbial communities. Several previous studies (including my own presented in Chapter 2) have attempted to engineer microbial communities but with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection has been limited, particularly for large assemblages of asexually reproducing microorganisms. In this chapter, I developed a flexible modeling framework that allows us to probe any number of arbitrary protocols of artificial selection. Using this model, I found that a simple screen control with no selection outperforms most protocols proposed to date, which do not allow the communities to reach their ecological equilibrium. Inspired by the directed evolution of biochemical macro-molecules, I then identified a range of selection strategies that are well-suited for the top-down engineering of complex, diverse, and stable microbial consortia.The works presented here illustrate how bottom-up and top-down approaches inspired by synthetic biology can be combined to develop a predictive and quantitative understanding of the emergent properties of ecological communities. While focusing on the microbial communities, the concepts developed apply to other biological organizations engaging in interspecific ecological interactions with heritable functional variations. The work I have presented highlights the limitations and strategies that could inform future designs on the engineering of complex microbial communities.
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