Modeling human gut microbiota: from steady states to dynamic systems
Doctoral thesis, 2022
Human gut microbes are an essential part of human sub-microscopic systems and involved in many critical biological processes such as Type 2 diabetes (T2D) and osteoporosis. However, the underlying mechanisms are unclear. Several mathematical modeling approaches, such as genome-scale metabolic models (GEMs) and ordinary differential equation (ODE) based models, have been used to simulate the dynamics of human gut microbiota. This thesis aims to explore, simulate, and predict the behavior of gut microbial ecosystems and the relationships between gut microbes and humans by modeling.
The importance of the gut microbiome for bone metabolism and T2D has been demonstrated in mice and human cohorts. We first reconstructed a GEM for Limosilactobacillus reuteri ATCC PTA 6475, which is a probiotic that significantly reduces bone loss in older women with lower bone mineral density. To investigate the associations between T2D and the gut microbiota, GEMs for 827 gut microbial species and 1,779 community-level GEMs for T2D cohorts have also been constructed. With these GEMs, we investigated metabolic potentials such as short-chain fatty acids, amino acids, and vitamins that play vital roles in the host metabolism regulation. Furthermore, the integration of the models with machine learning method provides potential insights into the possible roles of gut microbiota in T2D.
Cybernetic models, which simulate metabolic rates by integrating the control of enzyme synthesis and enzyme activities, have been applied to explore the dynamic behaviors of small-size metabolic networks. However, only a few studies have applied cybernetic theory to the microbial community so far. The remaining part of this thesis focuses on the use of cybernetic models to explore human gut microbiota's interactions and population dynamics. Considering the high computing burden of the current cybernetic modeling approach for processing the full-size GEMs, we have developed a computing-efficient strategy for model reconstruction and simulation to reveal the metabolic dynamics of human gut microbiota.
In this thesis, we explore the human gut microbiota from single L. reuteri species to microbial gut communities, from simple steady state systems by GEMs to complex dynamic systems by cybernetic model.
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The importance of the gut microbiome for bone metabolism and T2D has been demonstrated in mice and human cohorts. We first reconstructed a GEM for Limosilactobacillus reuteri ATCC PTA 6475, which is a probiotic that significantly reduces bone loss in older women with lower bone mineral density. To investigate the associations between T2D and the gut microbiota, GEMs for 827 gut microbial species and 1,779 community-level GEMs for T2D cohorts have also been constructed. With these GEMs, we investigated metabolic potentials such as short-chain fatty acids, amino acids, and vitamins that play vital roles in the host metabolism regulation. Furthermore, the integration of the models with machine learning method provides potential insights into the possible roles of gut microbiota in T2D.
Cybernetic models, which simulate metabolic rates by integrating the control of enzyme synthesis and enzyme activities, have been applied to explore the dynamic behaviors of small-size metabolic networks. However, only a few studies have applied cybernetic theory to the microbial community so far. The remaining part of this thesis focuses on the use of cybernetic models to explore human gut microbiota's interactions and population dynamics. Considering the high computing burden of the current cybernetic modeling approach for processing the full-size GEMs, we have developed a computing-efficient strategy for model reconstruction and simulation to reveal the metabolic dynamics of human gut microbiota.
In this thesis, we explore the human gut microbiota from single L. reuteri species to microbial gut communities, from simple steady state systems by GEMs to complex dynamic systems by cybernetic model.
dynamics.
metabolic modeling
genome-scale metabolic model
gut microbiota
cybernetic model
Author
Hao Luo
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Included papers
Genome-scale insights into the metabolic versatility of Limosilactobacillus reuteri
BMC Biotechnology,;Vol. 21(2021)
Journal article
One-year supplementation with Lactobacillus reuteri ATCC PTA 6475 counteracts a degradation of gut microbiota in older women with low bone mineral density
npj Biofilms and Microbiomes,;Vol. 8(2022)
Journal article
Modeling the metabolic dynamics at the genome-scale by optimized yield analysis
Metabolic Engineering,;Vol. 75(2023)p. 119-130
Journal article
Peishun Li*, Hao Luo*, Boyang Ji, Jens Nielsen. Metagenomic analysis of type 2 diabetes datasets identifies cross-cohort microbial and metabolic signatures
Manuscript
Hao Luo*, Boyang Ji*, Jens Nielsen. The metabolic network inference framework for shotgun metagenomics
Manuscript
Popular science description
English
Do you know how many gut microbes are in your body? Their cell numbers are close to the number of our human cells. The human microbiota is not only huge in number but also diverse. There are probably more than 2,000 different species of bacteria, viruses, fungi, and other microbes in our gut. They have 100 times more genes than those in the human genome. Are you as curious as I and my collaborators are about what they are doing in our bodies and how they affect our health? In this thesis, I have used genome-scale metabolic models (GEM) to create a "map" of gut microbiota metabolism. This "map" provides insight into the behavior of microorganisms and the metabolic potential at a steady state. In addition, I used cybernetic models to illustrate the "traffic conditions" and resource allocation in this "map", simulating the dynamic behavior of human microbiota.
The importance of the gut microbiome for bone metabolism and T2D has been demonstrated in mice and human cohorts. To study this, a GEM for Limosilactobacillus reuteri ATCC PTA 6475 was reconstructed, this probiotic bacterium significantly reducing bone loss in older women with lower bone mineral density. To investigate the associations between T2D and the gut microbiota, GEMs for T2D cohorts have also been constructed. Furthermore, integrating the models with the machine learning method provides potential insight into the possible roles of gut microbiota in T2D. The remaining part of this thesis focuses on using cybernetic models to explore human gut microbiota interactions and population dynamics. Together with my collaborators, I have developed a computing-efficient strategy for model reconstruction and simulation to reveal the metabolic dynamics of human gut microbiota.
In this thesis, I have explored the human gut microbiota from single L. reuteri species to microbial gut communities, from simple steady-state systems by GEMs to complex dynamic systems by cybernetic model.
The importance of the gut microbiome for bone metabolism and T2D has been demonstrated in mice and human cohorts. To study this, a GEM for Limosilactobacillus reuteri ATCC PTA 6475 was reconstructed, this probiotic bacterium significantly reducing bone loss in older women with lower bone mineral density. To investigate the associations between T2D and the gut microbiota, GEMs for T2D cohorts have also been constructed. Furthermore, integrating the models with the machine learning method provides potential insight into the possible roles of gut microbiota in T2D. The remaining part of this thesis focuses on using cybernetic models to explore human gut microbiota interactions and population dynamics. Together with my collaborators, I have developed a computing-efficient strategy for model reconstruction and simulation to reveal the metabolic dynamics of human gut microbiota.
In this thesis, I have explored the human gut microbiota from single L. reuteri species to microbial gut communities, from simple steady-state systems by GEMs to complex dynamic systems by cybernetic model.
Categorizing
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
Subject Categories (SSIF 2011)
Microbiology
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
Identifiers
ISBN
978-91-7905-709-1
Other
Series
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5175
Publisher
Chalmers
Public defence
2022-11-07 13:30
10:an meeting room, Kemihuset, Kemigården 4, Göteborg,
Opponent: Associate professor Vassily Hatzimanikatis, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.