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Afarin M, Naeimpoor F. Effect of microbial interactions on performance of community metabolic modeling algorithms: flux balance analysis (FBA), community FBA (cFBA) and SteadyCom. Bioprocess Biosyst Eng 2024; 47:1833-1848. [PMID: 39180547 DOI: 10.1007/s00449-024-03072-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 07/30/2024] [Indexed: 08/26/2024]
Abstract
To explore the impact of microbial interactions on outcomes from three prevalent algorithms (Flux Balance Analysis (FBA), community FBA (cFBA), and SteadyCom) analyzing microbial community metabolic networks, five toy community models representing common microbial interactions were designed. These include commensalism, mutualism, competition, mutualism-competition, and commensalism-competition. Various scenarios, considering different biomass yields and substrate constraints, were examined for each type. In commensal communities, all algorithms consistently produced similar results. However, changes in biomass yields and substrate constraints led to variable abundances (0.33-0.8) and community growth rates (2-5 1/h) within a broad range. For competitive communities, all algorithms predicted growth of fastest-growing member. To comply with the natural coexistence of members, suboptimal solutions over optimal point are recommended. FBA faced challenges in modeling mutualism, consistently predicting growth of only one member. Although cFBA and SteadyCom resulted in a lower community growth rate, coexistence of both members were satisfied. In toy models with dual interactions, more realistic outcomes were achieved contrary to purely competitive model as the dependency fosters the coexistence which was missing in the competitive only scenarios. These findings emphasize the importance of algorithm choice based on specific microbial interaction types for reliable community behavior predictions..
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Affiliation(s)
- Maryam Afarin
- Biotechnology Research Laboratory, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Fereshteh Naeimpoor
- Biotechnology Research Laboratory, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran.
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2
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Song HS, Lee NR, Kessell AK, McCullough HC, Park SY, Zhou K, Lee DY. Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation. mSystems 2024; 9:e0130523. [PMID: 38682902 PMCID: PMC11097648 DOI: 10.1128/msystems.01305-23] [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: 12/05/2023] [Accepted: 03/28/2024] [Indexed: 05/01/2024] Open
Abstract
Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. The effectiveness of our method was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids) but competitively grow on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions, such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics.IMPORTANCEModeling environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer the complex dynamics of microbial communities. Moreover, the prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among macroorganisms, such as plants and animals, as well as microorganisms.
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Affiliation(s)
- Hyun-Seob Song
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Na-Rae Lee
- Research Institute for Bioactive-Metabolome Network, Konkuk University, Seoul, South Korea
| | - Aimee K. Kessell
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Hugh C. McCullough
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, South Korea
| | - Kang Zhou
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, South Korea
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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4
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Lasa AV, Guevara MÁ, Villadas PJ, Vélez MD, Fernández-González AJ, de María N, López-Hinojosa M, Díaz L, Cervera MT, Fernández-López M. Correlating the above- and belowground genotype of Pinus pinaster trees and rhizosphere bacterial communities under drought conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 832:155007. [PMID: 35381249 DOI: 10.1016/j.scitotenv.2022.155007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 06/14/2023]
Abstract
Increasing temperatures along with severe droughts are factors that may jeopardize the survival of the forests in the Mediterranean basin. In this region, Pinus pinaster is a common conifer species, that has been used as a model species in evolutionary studies due to its adaptive response to changing environments. Although its drought tolerance mechanisms are already known, knowledge about the dynamics of its root microbiota is still scarce. We aimed to decipher the structural (bacterial abundance), compositional, functional and associative changes of the P. pinaster rhizosphere bacterial communities in spring and summer, at DNA and RNA level (environmental DNA, live and dead cells, and those synthesizing proteins). A fundamental aspect of root microbiome-based approaches is to guarantee the correct origin of the samples. Thus, we assessed the genotype of host needles and roots from which rhizosphere samples were obtained. For more than 50% of the selected trees, genotype discrepancies were found and in three cases the plant species could not be determined. Rhizosphere bacterial communities were homogeneous with respect to diversity and structural levels regardless of the host genotype in both seasons. Nonetheless, significant changes were seen in the taxonomic profiles depending on the season. Seasonal changes were also evident in the bacterial co-occurrence patterns, both in DNA and RNA libraries. While spring communities switched to more complex networks, summer populations resulted in more compartmentalized networks, suggesting that these communities were facing a disturbance. These results may mirror the future status of bacterial communities in a context of climate change. A keystone hub was ascribed to the genus Phenylobacterium in the functional network calculated for summer. Overall, it is important to validate the origin and identity of plant samples in any plant-microbiota study so that more reliable ecological analyses are performed.
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Affiliation(s)
- Ana V Lasa
- Department of Soil and Plant Microbiology, Estación Experimental del Zaidín, CSIC, Profesor Albareda 1, 18008 Granada, Spain.
| | - M Ángeles Guevara
- Dept. Forest Ecology and Genetics, Centro de Investigación Forestal, INIA-CSIC, Carretera de La Coruña Km 7,5, 28040 Madrid, Spain; Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Spain.
| | - Pablo J Villadas
- Department of Soil and Plant Microbiology, Estación Experimental del Zaidín, CSIC, Profesor Albareda 1, 18008 Granada, Spain.
| | - María Dolores Vélez
- Dept. Forest Ecology and Genetics, Centro de Investigación Forestal, INIA-CSIC, Carretera de La Coruña Km 7,5, 28040 Madrid, Spain; Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Spain.
| | - Antonio J Fernández-González
- Department of Soil and Plant Microbiology, Estación Experimental del Zaidín, CSIC, Profesor Albareda 1, 18008 Granada, Spain.
| | - Nuria de María
- Dept. Forest Ecology and Genetics, Centro de Investigación Forestal, INIA-CSIC, Carretera de La Coruña Km 7,5, 28040 Madrid, Spain; Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Spain.
| | - Miriam López-Hinojosa
- Dept. Forest Ecology and Genetics, Centro de Investigación Forestal, INIA-CSIC, Carretera de La Coruña Km 7,5, 28040 Madrid, Spain; Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Spain
| | - Luis Díaz
- Dept. Forest Ecology and Genetics, Centro de Investigación Forestal, INIA-CSIC, Carretera de La Coruña Km 7,5, 28040 Madrid, Spain; Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Spain.
| | - María Teresa Cervera
- Dept. Forest Ecology and Genetics, Centro de Investigación Forestal, INIA-CSIC, Carretera de La Coruña Km 7,5, 28040 Madrid, Spain; Mixed Unit of Forest Genomics and Ecophysiology, INIA/UPM, Spain.
| | - Manuel Fernández-López
- Department of Soil and Plant Microbiology, Estación Experimental del Zaidín, CSIC, Profesor Albareda 1, 18008 Granada, Spain.
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Jeong SY, Kim TG. Spatial Variance of Species Distribution Predicts the Interspecies Interactions within a Microbial Metacommunity. MICROBIAL ECOLOGY 2021; 81:549-552. [PMID: 32948906 DOI: 10.1007/s00248-020-01603-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/15/2020] [Indexed: 06/11/2023]
Abstract
Interspecies interactions have a profound influence on spatial distribution of coexisting microbial species. We explored whether spatial variance of species distribution (SVSD) predicts the degree of interspecies interactions within a microbial metacommunity. Simulations were used to determine the relationships from random, lake, soil, and biofilm metacommunity datasets (1,000 times). All of the bacterial datasets showed a negative correlation between the habitat breadth (inverse to SVSD) and the numbers of total, positive, and negative interspecies interactions (P < 0.05); the only exception was the relationship between habitat breadth and negative interactions in the biofilm dataset. The random dataset had no significant relationships (P > 0.05). We repeated the simulations to determine the degree of correlation and reproducibility (100 times). Habitat breadth was negatively correlated with the total and positive interactions in all of the real datasets (P < 0.05), and the negative relationships persisted across repetitions. Despite variability in the slope of total interactions, the slope values of positive interactions were similar for the real datasets (- 19.9, - 19.2, and - 25.8 for lake, soil, and biofilm, respectively). In conclusion, our results demonstrate the patterns of species interaction-distribution and show that interspecies interactions are positively correlated with the SVSD.
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Affiliation(s)
- So-Yeon Jeong
- Department of Microbiology, Pusan National University, Pusan, 46241, South Korea
| | - Tae Gwan Kim
- Department of Microbiology, Pusan National University, Pusan, 46241, South Korea.
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Ross BN, Whiteley M. Ignoring social distancing: advances in understanding multi-species bacterial interactions. Fac Rev 2020; 9:23. [PMID: 33659955 PMCID: PMC7886066 DOI: 10.12703/r/9-23] [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] [Indexed: 02/07/2023] Open
Abstract
Almost every ecosystem on this planet is teeming with microbial communities made of diverse bacterial species. At a reductionist view, many of these bacteria form pairwise interactions, but, as the field of view expands, the neighboring organisms and the abiotic environment can play a crucial role in shaping the interactions between species. Over the years, a strong foundation of knowledge has been built on isolated pairwise interactions between bacteria, but now the field is advancing toward understanding how cohabitating bacteria and natural surroundings affect these interactions. Use of bottom-up approaches, piecing communities together, and top-down approaches that deconstruct communities are providing insight on how different species interact. In this review, we highlight how studies are incorporating more complex communities, mimicking the natural environment, and recurring findings such as the importance of cooperation for stability in harsh environments and the impact of bacteria-induced environmental pH shifts. Additionally, we will discuss how omics are being used as a top-down approach to identify previously unknown interspecies bacterial interactions and the challenges of these types of studies for microbial ecology.
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Affiliation(s)
- Brittany N Ross
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
- Emory-Children's Cystic Fibrosis Center, Atlanta, Georgia, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Marvin Whiteley
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
- Emory-Children's Cystic Fibrosis Center, Atlanta, Georgia, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, Georgia, USA
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7
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Kessell AK, McCullough HC, Auchtung JM, Bernstein HC, Song HS. Predictive interactome modeling for precision microbiome engineering. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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8
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Lee JY, Sadler NC, Egbert RG, Anderton CR, Hofmockel KS, Jansson JK, Song HS. Deep learning predicts microbial interactions from self-organized spatiotemporal patterns. Comput Struct Biotechnol J 2020; 18:1259-1269. [PMID: 32612750 PMCID: PMC7298420 DOI: 10.1016/j.csbj.2020.05.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 05/16/2020] [Accepted: 05/17/2020] [Indexed: 12/27/2022] Open
Abstract
Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 µm × 450 µm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.
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Affiliation(s)
- Joon-Yong Lee
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Natalie C. Sadler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Robert G. Egbert
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Christopher R. Anderton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kirsten S. Hofmockel
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Janet K. Jansson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Hyun-Seob Song
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Nebraska Food for Health Center, Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, USA
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9
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Zaccaria M, Dawson W, Cristiglio V, Reverberi M, Ratcliff LE, Nakajima T, Genovese L, Momeni B. Designing a bioremediator: mechanistic models guide cellular and molecular specialization. Curr Opin Biotechnol 2020; 62:98-105. [DOI: 10.1016/j.copbio.2019.09.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/22/2019] [Accepted: 09/06/2019] [Indexed: 12/26/2022]
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10
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Lee JY, Haruta S, Kato S, Bernstein HC, Lindemann SR, Lee DY, Fredrickson JK, Song HS. Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data. Front Microbiol 2020; 10:3049. [PMID: 32038529 PMCID: PMC6985286 DOI: 10.3389/fmicb.2019.03049] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Accepted: 12/18/2019] [Indexed: 11/13/2022] Open
Abstract
Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients - basic parameters required for implementing the MIIA - are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite.
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Affiliation(s)
- Joon-Yong Lee
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Shin Haruta
- Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Japan
| | - Souichiro Kato
- National Institute of Advanced Industrial Science and Technology, Sapporo, Japan
| | - Hans C Bernstein
- Faculty of Biosciences, Fisheries and Economics, UiT - The Arctic University of Norway, Tromsø, Norway.,The Arctic Centre for Sustainable Energy, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Stephen R Lindemann
- Whistler Center for Carbohydrate Research, Department of Food Science, Purdue University, West Lafayette, IN, United States
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore, Singapore.,School of Chemical Engineering, Sungkyunkwan University, Seoul, South Korea
| | - Jim K Fredrickson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Hyun-Seob Song
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States.,Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.,Nebraska Food for Health Center, Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, United States
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