Quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth
In this thesis, we develop and apply top-down, quantitative approaches to gain novel insights into various complex biological systems. Beginning at the multicellular level, we study human gut microbiome dynamics from an ecological perspective. We develop computational frameworks to enable a global...
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ndltd-columbia.edu-oai-academiccommons.columbia.edu-10.7916-d8-zx2n-9k092019-05-09T15:16:03ZQuantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growthJi, Brian2019ThesesBiologyBiological systemsCancer cells--Growth--RegulationGastrointestinal system--MicrobiologyCell metabolismIn this thesis, we develop and apply top-down, quantitative approaches to gain novel insights into various complex biological systems. Beginning at the multicellular level, we study human gut microbiome dynamics from an ecological perspective. We develop computational frameworks to enable a global understanding of the spatiotemporal variability of gut bacterial abundances. We demonstrate the utility of our frameworks to elucidate the ecological processes governing abundance changes of gut microbiota. We then shift our focus to the intracellular level by investigating the metabolic limitations of cancer cell growth. We use coarse-grained mathematical modeling to identify a major growth limitation of cancer cells associated with electron acceptor deficiency, which we then experimentally validate. Collectively, these set of approaches help to decipher the organizing principles of complex biological systems at both the individual and multicellular levels.Englishhttps://doi.org/10.7916/d8-zx2n-9k09 |
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English |
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Biology Biological systems Cancer cells--Growth--Regulation Gastrointestinal system--Microbiology Cell metabolism |
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Biology Biological systems Cancer cells--Growth--Regulation Gastrointestinal system--Microbiology Cell metabolism Ji, Brian Quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth |
description |
In this thesis, we develop and apply top-down, quantitative approaches to gain novel insights into various complex biological systems. Beginning at the multicellular level, we study human gut microbiome dynamics from an ecological perspective. We develop computational frameworks to enable a global understanding of the spatiotemporal variability of gut bacterial abundances. We demonstrate the utility of our frameworks to elucidate the ecological processes governing abundance changes of gut microbiota. We then shift our focus to the intracellular level by investigating the metabolic limitations of cancer cell growth. We use coarse-grained mathematical modeling to identify a major growth limitation of cancer cells associated with electron acceptor deficiency, which we then experimentally validate. Collectively, these set of approaches help to decipher the organizing principles of complex biological systems at both the individual and multicellular levels. |
author |
Ji, Brian |
author_facet |
Ji, Brian |
author_sort |
Ji, Brian |
title |
Quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth |
title_short |
Quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth |
title_full |
Quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth |
title_fullStr |
Quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth |
title_full_unstemmed |
Quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth |
title_sort |
quantifying spatiotemporal dynamics of human gut microbiota and metabolic limitations of cancer cell growth |
publishDate |
2019 |
url |
https://doi.org/10.7916/d8-zx2n-9k09 |
work_keys_str_mv |
AT jibrian quantifyingspatiotemporaldynamicsofhumangutmicrobiotaandmetaboliclimitationsofcancercellgrowth |
_version_ |
1719047343925362688 |