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Atasoy M, Scott WT, Regueira A, Mauricio-Iglesias M, Schaap PJ, Smidt H. Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches. Biotechnol Adv 2024; 73:108363. [PMID: 38657743 DOI: 10.1016/j.biotechadv.2024.108363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/03/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024]
Abstract
In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.
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Affiliation(s)
- Merve Atasoy
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Department of Environmental Technology, Wageningen University & Research, Wageningen, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
| | - William T Scott
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Alberte Regueira
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain; Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, Belgium; Center for Advanced Process Technology for Urban Resource Recovery (CAPTURE), Frieda Saeysstraat 1, Ghent, Belgium.
| | - Miguel Mauricio-Iglesias
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
| | - Peter J Schaap
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Hauke Smidt
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
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2
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Brunner JD, Chia N. Metabolic model-based ecological modeling for probiotic design. eLife 2024; 13:e83690. [PMID: 38380900 PMCID: PMC10942782 DOI: 10.7554/elife.83690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 02/19/2024] [Indexed: 02/22/2024] Open
Abstract
The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.
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Affiliation(s)
- James D Brunner
- Biosciences Division, Los Alamos National LaboratoryLos AlamosUnited States
- Center for Nonlinear Studies, Los Alamos National LaboratoryLos AlamosUnited States
| | - Nicholas Chia
- Data Science and Learning, Argonne National LaboratoryLemontUnited States
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3
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Brunner JD, Gallegos-Graves LA, Kroeger ME. Inferring microbial interactions with their environment from genomic and metagenomic data. PLoS Comput Biol 2023; 19:e1011661. [PMID: 37956203 PMCID: PMC10681327 DOI: 10.1371/journal.pcbi.1011661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/27/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.
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Affiliation(s)
- James D. Brunner
- Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - Marie E. Kroeger
- Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
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4
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Díaz-Seoane S, Sellán E, Villaverde AF. Structural Identifiability and Observability of Microbial Community Models. Bioengineering (Basel) 2023; 10:bioengineering10040483. [PMID: 37106670 PMCID: PMC10135947 DOI: 10.3390/bioengineering10040483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/10/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
Biological communities are populations of various species interacting in a common location. Microbial communities, which are formed by microorganisms, are ubiquitous in nature and are increasingly used in biotechnological and biomedical applications. They are nonlinear systems whose dynamics can be accurately described by models of ordinary differential equations (ODEs). A number of ODE models have been proposed to describe microbial communities. However, the structural identifiability and observability of most of them-that is, the theoretical possibility of inferring their parameters and internal states by observing their output-have not been determined yet. It is important to establish whether a model possesses these properties, because, in their absence, the ability of a model to make reliable predictions may be compromised. Hence, in this paper, we analyse these properties for the main families of microbial community models. We consider several dimensions and measurements; overall, we analyse more than a hundred different configurations. We find that some of them are fully identifiable and observable, but a number of cases are structurally unidentifiable and/or unobservable under typical experimental conditions. Our results help in deciding which modelling frameworks may be used for a given purpose in this emerging area, and which ones should be avoided.
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Affiliation(s)
- Sandra Díaz-Seoane
- Department of Systems Engineering & Control, Universidade de Vigo, 36310 Vigo, Spain
| | - Elena Sellán
- Department of Systems Engineering & Control, Universidade de Vigo, 36310 Vigo, Spain
| | - Alejandro F Villaverde
- Department of Systems Engineering & Control, Universidade de Vigo, 36310 Vigo, Spain
- CITMAga, 15782 Santiago de Compostela, Spain
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5
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Liu YY. Controlling the human microbiome. Cell Syst 2023; 14:135-159. [PMID: 36796332 PMCID: PMC9942095 DOI: 10.1016/j.cels.2022.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/18/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.
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Affiliation(s)
- Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
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6
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van den Berg NI, Machado D, Santos S, Rocha I, Chacón J, Harcombe W, Mitri S, Patil KR. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat Ecol Evol 2022; 6:855-865. [PMID: 35577982 PMCID: PMC7613029 DOI: 10.1038/s41559-022-01746-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/23/2022] [Indexed: 12/20/2022]
Abstract
Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.
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Affiliation(s)
| | - Daniel Machado
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sophia Santos
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Isabel Rocha
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Jeremy Chacón
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - William Harcombe
- Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, MN, USA
| | - Sara Mitri
- Département de Microbiologie Fondamentale, University of Lausanne, Lausanne, Switzerland
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.
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7
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Wagner A. Competition for nutrients increases invasion resistance during assembly of microbial communities. Mol Ecol 2022; 31:4188-4203. [PMID: 35713370 PMCID: PMC9542400 DOI: 10.1111/mec.16565] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/08/2022] [Accepted: 06/10/2022] [Indexed: 12/02/2022]
Abstract
The assembly of microbial communities through sequential invasions of microbial species is challenging to study experimentally. Here, I used genome‐scale metabolic models of multiple species to model community assembly. Each such model represents all known biochemical reactions that a species uses to build biomass from nutrients in the environment. Species interactions in such models emerge from first biochemical principles, either through competition for environmental nutrients, or through cross‐feeding on metabolic by‐products excreted by resident species. I used these models to study 250 community assembly sequences. In each such sequence, a community changes through successive species invasions. During the 250 assembly sequences, communities become more species‐rich and invasion‐resistant. Resistance against both constructive and destructive invasions – those that entail species extinction – is associated with high community productivity, high biomass, and low concentrations of unused carbon. Competition for nutrients outweighs the influence of cross‐feeding on the growth rate of individual species. In a community assembly network of all communities that arise during the 250 assembly sequences, some communities occur more often than expected by chance. These include invasion resistant “attractor” communities with high biomass that arise late in community assembly and persist preferentially because of their invasion resistance. Genome‐scale metabolic models can reveal generic properties of microbial communities that are independent of the resident species and the environment.
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Affiliation(s)
- Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode, Lausanne, Switzerland.,The Santa Fe Institute, Santa Fe, New Mexico, USA.,Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa
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8
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Suzuki K, Abe MS, Kumakura D, Nakaoka S, Fujiwara F, Miyamoto H, Nakaguma T, Okada M, Sakurai K, Shimizu S, Iwata H, Masuya H, Nihei N, Ichihashi Y. Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031228. [PMID: 35162258 PMCID: PMC8834966 DOI: 10.3390/ijerph19031228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
Network-based assessments are important for disentangling complex microbial and microbial–host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka–Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.
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Affiliation(s)
- Kenta Suzuki
- BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan; (H.M.); (Y.I.)
- Correspondence:
| | - Masato S. Abe
- Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo 103-0027, Japan; (M.S.A.); (S.S.)
| | - Daiki Kumakura
- Graduate School of Life Science, Hokkaido University, Sapporo 060-0810, Japan; (D.K.); (S.N.)
| | - Shinji Nakaoka
- Graduate School of Life Science, Hokkaido University, Sapporo 060-0810, Japan; (D.K.); (S.N.)
- Laboratory of Mathematical Biology, Faculty of Advanced Life Science, Hokkaido University, Sapporo 060-0819, Japan
| | - Fuki Fujiwara
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Hirokuni Miyamoto
- Graduate School of Horticulture, Chiba University, Matsudo 271-8501, Japan; (H.M.); (T.N.)
- RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Sermas Co., Ltd., Ichikawa 272-0015, Japan
| | - Teruno Nakaguma
- Graduate School of Horticulture, Chiba University, Matsudo 271-8501, Japan; (H.M.); (T.N.)
- Sermas Co., Ltd., Ichikawa 272-0015, Japan
| | - Mashiro Okada
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Kengo Sakurai
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Shohei Shimizu
- Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo 103-0027, Japan; (M.S.A.); (S.S.)
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan; (F.F.); (M.O.); (K.S.); (H.I.)
| | - Hiroshi Masuya
- BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan; (H.M.); (Y.I.)
| | - Naoto Nihei
- Faculty of Food and Agricultural Sciences, Fukushima University, Fukushima 960-1296, Japan;
| | - Yasunori Ichihashi
- BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan; (H.M.); (Y.I.)
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9
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Kim M, Sung J, Chia N. Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling. Metab Eng 2022; 70:12-22. [PMID: 34990848 DOI: 10.1016/j.ymben.2021.12.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/01/2021] [Accepted: 12/29/2021] [Indexed: 10/19/2022]
Abstract
Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.
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Affiliation(s)
- Minsuk Kim
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jaeyun Sung
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA; Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
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10
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Aguirre de Cárcer D. Experimental and computational approaches to unravel microbial community assembly. Comput Struct Biotechnol J 2020; 18:4071-4081. [PMID: 33363703 PMCID: PMC7736701 DOI: 10.1016/j.csbj.2020.11.031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.
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11
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Brunner JD, Chia N. Minimizing the number of optimizations for efficient community dynamic flux balance analysis. PLoS Comput Biol 2020; 16:e1007786. [PMID: 32991583 PMCID: PMC7546477 DOI: 10.1371/journal.pcbi.1007786] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 10/09/2020] [Accepted: 08/21/2020] [Indexed: 01/03/2023] Open
Abstract
Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism's metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. We can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba.
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Affiliation(s)
- James D. Brunner
- Department of Surgery, Center for Individualized Medicine Microbiome Program, Mayo Clinic, Rochester, MN, USA
| | - Nicholas Chia
- Department of Surgery, Center for Individualized Medicine Microbiome Program, Mayo Clinic, Rochester, MN, USA
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12
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Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence. PLoS Comput Biol 2020; 16:e1008135. [PMID: 32810127 PMCID: PMC7480867 DOI: 10.1371/journal.pcbi.1008135] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 09/09/2020] [Accepted: 07/09/2020] [Indexed: 01/03/2023] Open
Abstract
Social interaction between microbes can be described at many levels of details: from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing an appropriate level to model microbial communities without losing generality remains a challenge. Here we show that modeling cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems is suitable to experimental data. We applied our modeling framework to three published examples of multi-strain Escherichia coli communities with increasing complexity: uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately fit empirical data and quantified metabolic exchange rates that are hard to measure experimentally, even for a complex community of 14 amino acid auxotrophies. By studying the conditions of species coexistence, the ecological outcomes of cross-feeding interactions, and each community’s robustness to perturbations, we extracted new quantitative insights from these three published experimental datasets. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities. The behavior of microbial communities such as the human microbiome is hard to predict by its species composition alone. Our efforts to engineer microbiomes—for example to improve human health—would benefit from mathematical models that accurately describe how microbes exchange metabolites with each other and how their environment shapes these exchanges. But what is an appropriate level of details for those models? We propose an intermediate level to model metabolic exchanges between microbes. We show that these models can accurately describe population dynamics in three laboratory communities and predicts their stability in response to perturbations such as changes in the nutrients available in the medium that they grow on. Our work suggests that a highly detailed metabolic network model is unnecessary for extracting ecological insights from experimental data and improves mathematical models so that one day we may be able to predict the behavior of real-world communities such as the human microbiome.
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