Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities

doi:10.15302/J-QB-022-0313

Quant. Biol.

2023, Vol. 11

Issue (1) : 15-30 https://doi.org/10.15302/J-QB-022-0313

REVIEW

Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities

Huan Du^1,², Meng Li^1,², Yang Liu^1,²(

)

¹. Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China
². Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China

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Abstract

Background: Synthetic microbial communities, with different strains brought together by balancing their nutrition and promoting their interactions, demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications. The potential of such microbial communities has not been explored, due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.

Results: Genome-scale metabolic models (GEM) have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities, since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbe-habitats and microbe-microbe interactions. In this work, we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities: predicting multi-species interactions, exploring environmental impacts on microbial phenotypes, and optimizing community-level performance.

Conclusions: Although at the infancy stage, GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities. Compared to other methods, especially the use of laboratory cultures, GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality, such as identifying community members, determining media composition, evaluating microbial interaction potential or selecting the best community configuration. Future efforts should be made to overcome the limitations of the approaches, ranging from quality control of GEM reconstructions to community-level modeling algorithms, so that more applications of GEMs in studying phenotypes of microbial communities can be expected.

Keywords genome-scale metabolic modeling microbial community design interspecies interaction environmental impact community-level performance

Corresponding Author(s): Yang Liu

Online First Date: 21 February 2023 Issue Date: 13 March 2023

Cite this article:

Huan Du,Meng Li,Yang Liu. Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities[J]. Quant. Biol., 2023, 11(1): 15-30.

URL:

https://academic.hep.com.cn/qb/EN/10.15302/J-QB-022-0313
https://academic.hep.com.cn/qb/EN/Y2023/V11/I1/15

Fig.1 Basic process of GEM reconstructions.

Fig.2 Different categories of GEM-based modeling tools.

Fig.3 Examples of GEM applications guiding design of synthetic microbial communities.

Classification	Method	Short description
Community-level, static, lumped network-based	Rodríguez et al. 2006 [56]	A model to predict product formation from glucose in anaerobic mixed culture fermentation through maximizing a community-level biomass objective.
Community-level, static, lumped network-based	Pramanik et al. 1999 [57]	A model to explore biological phosphorus removal metabolism.
Community-level, static, compartment-based	OptCom [58]	An FBA-based framework to describe trade-offs between individual and community-level fitness criteria by optimizing multi-level objectives.
	cFBA [59]	A method to analyze community parameters (maximal growth rate, relative biomass abundance, etc.) at balanced growth.
	SteadyCom [21]	A framework reformulated from cFBA without the limitations on the number of linear programming iterations for predicting the variation in species abundance in response to substrate changes.
	DOLMN [60]	A mixed integer linear programming (MILP) optimization approach to explore possible labor division in communities under constraints (e.g., limited number of exchange reactions).
	BioLEGO 2 [61]	A Microsoft Azure Cloud-based framework which supports large-scale simulations of biomass serial fermentation processes by two different organisms with single or multiple gene knockouts.
	SMETANA [62]	A tool to estimate pairwise and community-level microbial interaction potential (through SMETANA score) and identify likely exchanged metabolites.
	Stolyar et al. 2007 [69]	The first multi-species GEM to predict community-level fluxes and the ratio of cells.
	The microbiome modeling toolbox [71]	A COBRA-based toolbox to study various types of pairwise microbe-microbe, microbe-host interactions and, to analyze personalized gut microbial communities under different diets.
	MMinte [72]	A methodology to assess pairwise microbial metabolic interactions ends the effect of these interactions on the relative growth rates of microbes from 16S rRNA data.
	Klitgord and Segrè 2010 [76]	A model to identify media that can induce putative symbiotic interactions.
	ViNE [81]	An FBA-based model for analyzing the integrated metabolism of the holobiont consisting of a host plant and its symbiotic bacterium.
	MICOM [86]	A framework for predicting growth rates of diverse bacterial species in human gut and metabolic fluxes of communities by using a heuristic optimization approach based on L2 regularization.
	CASINO [89]	A toolbox for modeling diet-microbiota interactions.
	Zampieri and Sauer 2016 [94]	A mixed-integer bi-level linear programming to infer an optimal combination of nutrients for sustaining pairwise, synergistic growth of microbes with minimum cost of cross-fed metabolites.
Community-level, dynamic, temporal	DMMM [63]	The first method using dFBA at community level to optimize growth rates of each strain within the community.
Community-level, dynamic, temporal	dOptCom [64]	A method extended from OptCom for the dynamic metabolic modeling of microbial communities with multi-level objectives.
Community-level, dynamic, spatio-temporal	COMETS [65]	A platform implementing a dFBA algorithm on a lattice to track the spatio-temporal biomass distribution and fluxes of a multi-species community at population level.
	BacArena [20]	An R package integrating dFBA with individual-based approach to generate spatial organization and metabolic phenotype in biofilms over time.
	IndiMeSH [66]	A model combined dFBA with individual-based approach in an angular pore network for spatial modeling of soil aggregates in considering the impact of habitat geometry and hydration conditions.
	CODY [67]	A multi-scale framework to identify and quantify spatiotemporal-specific variations of gut microbiome abundance profiles in the colon as impacted by host physiology.
	FLYCOP [100]	A framework combining COMETS with a local search algorithm to automatically select the best consortium configuration among multiple predefined/random ones for a given goal.
Individual level, integration with macromolecular expression	FoldME [78]	A metabolism and protein expression (ME) model incorporating folding and degradation kinetics to predict the effect of temperature on microbial growth.
	OxidizeME [79]	An ME model to describe the response of microbes to reactive oxygen species stress.
	AcidifyME [80]	An ME model integrating folding and unfolding thermodynamics and kinetics to simulate the response of microbes to pH variations.

Tab.1 Summary of GEM-based approaches which can be applied in synthetic community researches

Fig.4 Limitations (B) hindering the application of GEM-based approaches in synthetic community design (A) and the potential improvement strategies (C).

1	T. kopf, O. Soyer, (2014). Synthetic microbial communities. Curr. Opin. Microbiol., 18: 72–77 https://doi.org/10.1016/j.mib.2014.02.002
2	K., Zhou, K., Qiao, S. Edgar, (2015). Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol., 33: 377–383 https://doi.org/10.1038/nbt.3095
3	X., Wang, R., Su, K., Chen, S., Xu, J. Feng, (2019). Engineering a microbial consortium based whole-cell system for efficient production of glutarate from L-lysine. Front. Microbiol., 10: 341 https://doi.org/10.3389/fmicb.2019.00341
4	S. G., Hays, W. G., Patrick, M., Ziesack, N. Oxman, P. Silver, (2015). Better together: engineering and application of microbial symbioses. Curr. Opin. Biotechnol., 36: 40–49 https://doi.org/10.1016/j.copbio.2015.08.008
5	N. S. McCarty, (2019). Synthetic biology tools to engineer microbial communities for biotechnology. Trends Biotechnol., 37: 181–197 https://doi.org/10.1016/j.tibtech.2018.11.002
6	D., Beyter, P. Z., Tang, S., Becker, T., Hoang, D., Bilgin, Y. W., Lim, T. C., Peterson, S., Mayfield, F., Haerizadeh, J. B. Shurin, et al.. (2016). Diversity, productivity, and stability of an industial microbial ecosystem. Appl. Environ. Microbiol., 82: 2494–2505 https://doi.org/10.1128/AEM.03965-15
7	J. B., Shurin, R. L., Abbott, M. S., Deal, G. T., Kwan, E., Litchman, R. C., McBride, S. Mandal, V. Smith, (2013). Industrial-strength ecology: trade-offs and opportunities in algal biofuel production. Ecol. Lett., 16: 1393–1404 https://doi.org/10.1111/ele.12176
8	T., Senthilvelan, J., Kanagaraj, R. C. Panda, A. Mandal, (2014). Biodegradation of phenol by mixed microbial culture: an eco-friendly approach for the pollution reduction. Clean Technol. Environ. Policy, 16: 113–126 https://doi.org/10.1007/s10098-013-0598-2
9	H., Zhang, B., Pereira, Z. Li, (2015). Engineering Escherichia coli coculture systems for the production of biochemical products. Proc. Natl. Acad. Sci. U.S.A., 112: 8266–8271 https://doi.org/10.1073/pnas.1506781112
10	T. R., Zuroff, S. B. Xiques, W. Curtis, (2013). Consortia-mediated bioprocessing of cellulose to ethanol with a symbiotic Clostridium phytofermentans/yeast co-culture. Biotechnol. Biofuels, 6: 59 https://doi.org/10.1186/1754-6834-6-59
11	S. Patle, (2007). Ethanol production from hydrolysed agricultural wastes using mixed culture of Zymomonas mobilis and Candida tropicalis. Biotechnol. Lett., 29: 1839–1843 https://doi.org/10.1007/s10529-007-9493-4
12	J. P., Wang, W., Lin, V., Wray, D. Lai, (2013). Induced production of depsipeptides by co-culturing Fusarium tricinctum and Fusarium begoniae. Tetrahedron Lett., 54: 2492–2496 https://doi.org/10.1016/j.tetlet.2013.03.005
13	S., Caballero, S., Kim, R. A., Carter, I. M., Leiner, B., ac, L., Miller, G. J., Kim, L. Ling, E. Pamer, (2017). Cooperating commensals restore colonization resistance to vancomycin-resistant Enterococcus faecium. Cell Host Microbe, 21: 592–602.e4 https://doi.org/10.1016/j.chom.2017.04.002
14	S., Brugiroux, M., Beutler, C., Pfann, D., Garzetti, H. J., Ruscheweyh, D., Ring, M., Diehl, S., Herp, Y., tscher, S. Hussain, et al.. (2017). Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium. Nat. Microbiol., 2: 16215 https://doi.org/10.1038/nmicrobiol.2016.215
15	S. Che, (2019). Synthetic microbial consortia for biosynthesis and biodegradation: promises and challenges. J. Ind. Microbiol. Biotechnol., 46: 1343–1358 https://doi.org/10.1007/s10295-019-02211-4
16	N. I., Johns, T., Blazejewski, A. L. Gomes, H. Wang, (2016). Principles for designing synthetic microbial communities. Curr. Opin. Microbiol., 31: 146–153 https://doi.org/10.1016/j.mib.2016.03.010
17	M. T., Mee, J. J., Collins, G. M. Church, H. Wang, (2014). Syntrophic exchange in synthetic microbial communities. Proc. Natl. Acad. Sci. U.S.A., 111: E2149–E2156 https://doi.org/10.1073/pnas.1405641111
18	M., Embree, J. K., Liu, M. M. Al-Bassam, (2015). Networks of energetic and metabolic interactions define dynamics in microbial communities. Proc. Natl. Acad. Sci. U.S.A., 112: 15450–15455 https://doi.org/10.1073/pnas.1506034112
19	K. Zengler, L. Zaramela, (2018). The social network of microorganisms—how auxotrophies shape complex communities. Nat. Rev. Microbiol., 16: 383–390 https://doi.org/10.1038/s41579-018-0004-5
20	E., Bauer, J., Zimmermann, F., Baldini, I. Thiele, (2017). BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities. PLOS Comput. Biol., 13: e1005544 https://doi.org/10.1371/journal.pcbi.1005544
21	S. H. J., Chan, M. N. Simons, C. Maranas, (2017). SteadyCom: Predicting microbial abundances while ensuring community stability. PLOS Comput. Biol., 13: e1005539 https://doi.org/10.1371/journal.pcbi.1005539
22	M., Kumar, B., Ji, K. Zengler, (2019). Modelling approaches for studying the microbiome. Nat. Microbiol., 4: 1253–1267 https://doi.org/10.1038/s41564-019-0491-9
23	M., Kanehisa, M., Furumichi, Y., Sato, M. Ishiguro-Watanabe, (2021). KEGG: integrating viruses and cellular organisms. Nucleic Acids Res., 49: D545–D551 https://doi.org/10.1093/nar/gkaa970
24	R., Caspi, R., Billington, I. M., Keseler, A., Kothari, M., Krummenacker, P. E., Midford, W. K., Ong, S., Paley, P. Subhraveti, P. Karp, (2020). The MetaCyc database of metabolic pathways and enzymes — a 2019 update. Nucleic Acids Res., 48: D445–D453 https://doi.org/10.1093/nar/gkz862
25	Z. A., King, J., Lu, A., ger, P., Miller, S., Federowicz, J. A., Lerman, A., Ebrahim, B. O. Palsson, N. Lewis, (2016). BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res., 44: D515–D522 https://doi.org/10.1093/nar/gkv1049
26	I. Thiele, B. Palsson, (2010). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc., 5: 93–121 https://doi.org/10.1038/nprot.2009.203
27	J. Pramanik, J. Keasling, (1997). Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. Biotechnol. Bioeng., 56: 398–421 https://doi.org/10.1002/(SICI)1097-0290(19971120)56:4<398::AID-BIT6>3.0.CO;2-J
28	A. Feist, J. Scholten, B., Palsson, F. Brockman, (2006). Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri. Mol. Syst. Biol. 2, 2006.0004 https://doi.org/10.1038/msb4100046
29	C., Gu, G. B., Kim, W. J., Kim, H. U. Kim, S. Lee, (2019). Current status and applications of genome-scale metabolic models. Genome Biol., 20: 121 https://doi.org/10.1186/s13059-019-1730-3
30	D., Machado, S., Andrejev, M. Tramontano, K. Patil, (2018). Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res., 46: 7542–7553 https://doi.org/10.1093/nar/gky537
31	S. M. D., Seaver, F., Liu, Q., Zhang, J., Jeffryes, J. P., Faria, J. N., Edirisinghe, M., Mundy, N., Chia, E., Noor, M. E. Beber, et al.. (2021). The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res., 49: D575–D588 https://doi.org/10.1093/nar/gkaa746
32	M., Aite, M., Chevallier, C., Frioux, C., Trottier, J., Got, S. N., Mendoza, G., Carrier, O., Dameron, N. Guillaudeux, et al.. (2018). Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models. PLOS Comput. Biol., 14: e1006146 https://doi.org/10.1371/journal.pcbi.1006146
33	C. S., Henry, M. D., Jankowski, L. J. Broadbelt, (2006). Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys. J., 90: 1453–1461 https://doi.org/10.1529/biophysj.105.071720
34	B., Pereira, J., Miguel, S., Soares, I. Rocha, (2018). Reconstruction of a genome-scale metabolic model for Actinobacillus succinogenes 130Z. BMC Syst. Biol., 12: 61 https://doi.org/10.1186/s12918-018-0585-7
35	J. D., Orth, I. Thiele, B. Palsson, (2010). What is flux balance analysis? Nat. Biotechnol., 28: 245–248 https://doi.org/10.1038/nbt.1614
36	J. S. Edwards, B. Palsson, (1999). Systems properties of the Haemophilus influenzae Rd metabolic genotype. J. Biol. Chem., 274: 17410–17416 https://doi.org/10.1074/jbc.274.25.17410
37	J. M., Monk, C. J., Lloyd, E., Brunk, N., Mih, A., Sastry, Z., King, R., Takeuchi, W., Nomura, Z., Zhang, H. Mori, et al.. (2017). iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotechnol., 35: 904–908 https://doi.org/10.1038/nbt.3956
38	T. zdamar, (2017). Analyses of extracellular protein production in Bacillus subtilis – I: Genome-scale metabolic model reconstruction based on updated gene-enzyme-reaction data. Biochem. Eng. J., 127: 229–241 https://doi.org/10.1016/j.bej.2017.07.005
39	H., Nazem-Bokaee, S., Gopalakrishnan, J. G., Ferry, T. K. Wood, C. Maranas, (2016). Assessing methanotrophy and carbon fixation for biofuel production by Methanosarcina acetivorans. Microb. Cell Fact., 15: 10 https://doi.org/10.1186/s12934-015-0404-4
40	J., rster, I., Famili, P., Fu, B. Palsson, (2003). Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res., 13: 244–253 https://doi.org/10.1101/gr.234503
41	S., Mintz-Oron, S., Meir, S., Malitsky, E., Ruppin, A. Aharoni, (2012). Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. Proc. Natl. Acad. Sci. U.S.A., 109: 339–344 https://doi.org/10.1073/pnas.1100358109
42	A., Mardinoglu, R., Agren, C., Kampf, A., Asplund, I., Nookaew, P., Jacobson, A. J., Walley, P., Froguel, L. M., Carlsson, M. Uhlen, et al.. (2013). Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol., 9: 649 https://doi.org/10.1038/msb.2013.5
43	A. P., Arkin, R. W., Cottingham, C. S., Henry, N. L., Harris, R. L., Stevens, S., Maslov, P., Dehal, D., Ware, F., Perez, S. Canon, et al.. (2018). KBase: The United States department of energy systems biology knowledgebase. Nat. Biotechnol., 36: 566–569 https://doi.org/10.1038/nbt.4163
44	L., Heirendt, S., Arreckx, T., Pfau, S. N., Mendoza, A., Richelle, A., Heinken, H. S., ttir, J., Wachowiak, S. M., Keating, V. Vlasov, et al.. (2019). Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0. Nat. Protoc., 14: 639–702 https://doi.org/10.1038/s41596-018-0098-2
45	P. D., Karp, P. E., Midford, R., Billington, A., Kothari, M., Krummenacker, M., Latendresse, W. K., Ong, P., Subhraveti, R., Caspi, C. Fulcher, et al.. (2021). Pathway Tools version 23. 0 update: software for pathway/genome informatics and systems biology. Brief. Bioinform., 22: 109–126 https://doi.org/10.1093/bib/bbz104
46	S., ttir, A., Heinken, L., Kutt, D. A., Ravcheev, E., Bauer, A., Noronha, K., Greenhalgh, C., ger, J., Baginska, P. Wilmes, et al.. (2017). Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol., 35: 81–89 https://doi.org/10.1038/nbt.3703
47	T., Blasco, S., rez-Burillo, F., Balzerani, D., Hinojosa-Nogueira, A., Lerma-Aguilera, S., Pastoriza, X., Cendoya, M. J., Gosalbes, N. ndez, et al.. (2021). An extended reconstruction of human gut microbiota metabolism of dietary compounds. Nat. Commun., 12: 4728 https://doi.org/10.1038/s41467-021-25056-x
48	R., Agren, L., Liu, S., Shoaie, W., Vongsangnak, I. Nookaew, (2013). The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLOS Comput. Biol., 9: e1002980 https://doi.org/10.1371/journal.pcbi.1002980
49	J., Capela, D., Lagoa, R., Rodrigues, E., Cunha, F., Cruz, A., Barbosa, J., Bastos, D., Lima, E. C., Ferreira, M. Rocha, et al.. (2022). merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models. Nucleic Acids Res., 50: 6052–6066 https://doi.org/10.1093/nar/gkac459
50	B. J., nchez, C., Zhang, A., Nilsson, P. J., Lahtvee, E. J. Kerkhoven, (2017). Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol. Syst. Biol., 13: 935 https://doi.org/10.15252/msb.20167411
51	J. L., Steffensen, K. Dufault-Thompson, (2016). PSAMM: A portable system for the analysis of metabolic models. PLOS Comput. Biol., 12: e1004732 https://doi.org/10.1371/journal.pcbi.1004732
52	E., rnson, (2016). Personalized cardiovascular disease prediction and treatment—A review of existing strategies and novel systems medicine tools. Front. Physiol., 7: 2 https://doi.org/10.3389/fphys.2016.00002
53	J. S., Cho, C., Gu, T. H., Han, J. Y. Ryu, S. Lee, (2019). Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. Curr. Opin. Syst. Biol., 15: 1–11 https://doi.org/10.1016/j.coisb.2019.02.009
54	E. Esvap, K. Ulgen, (2021). Advances in genome-scale metabolic modeling toward microbial community analysis of the human microbiome. ACS Synth. Biol., 10: 2121–2137 https://doi.org/10.1021/acssynbio.1c00140
55	L. Lin, (2022). Bottom-up synthetic ecology study of microbial consortia to enhance lignocellulose bioconversion. Biotechnol. Biofuels Bioprod., 15: 14 https://doi.org/10.1186/s13068-022-02113-1
56	J., guez, R., Kleerebezem, J. M. Lema, M. C. van Loosdrecht, (2006). Modeling product formation in anaerobic mixed culture fermentations. Biotechnol. Bioeng., 93: 592–606 https://doi.org/10.1002/bit.20765
57	J., Pramanik, P. L., Trelstad, A. J., Schuler, D. Jenkins, J. Keasling, (1999). Development and validation of a flux-based stoichiometric model for enhanced biological phosphorus removal metabolism. Water Res., 33: 462–476 https://doi.org/10.1016/S0043-1354(98)00225-5
58	A. R. Zomorrodi, C. Maranas, (2012). OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLOS Comput. Biol., 8: e1002363 https://doi.org/10.1371/journal.pcbi.1002363
59	R. A., Khandelwal, B. G., Olivier, W. F. M., ling, B. Teusink, F. Bruggeman, (2013). Community flux balance analysis for microbial consortia at balanced growth. PLoS One, 8: e64567 https://doi.org/10.1371/journal.pone.0064567
60	M., Thommes, T., Wang, Q., Zhao, I. C. Paschalidis, (2019). Designing metabolic division of labor in microbial communities. mSystems, 4: e00263–e18 https://doi.org/10.1128/mSystems.00263-18
61	E., Vitkin, A., Gillis, M., Polikovsky, B., Bender, A. Golberg, (2020). Distributed flux balance analysis simulations of serial biomass fermentation by two organisms. PLoS One, 15: e0227363 https://doi.org/10.1371/journal.pone.0227363
62	A., Zelezniak, S., Andrejev, O., Ponomarova, D. R., Mende, P. Bork, K. Patil, (2015). Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl. Acad. Sci. U.S.A., 112: 6449–6454 https://doi.org/10.1073/pnas.1421834112
63	K., Zhuang, M., Izallalen, P., Mouser, H., Richter, C., Risso, R. Mahadevan, D. Lovley, (2011). Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J., 5: 305–316 https://doi.org/10.1038/ismej.2010.117
64	A. R., Zomorrodi, M. M. Islam, C. Maranas, (2014). d-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities. ACS Synth. Biol., 3: 247–257 https://doi.org/10.1021/sb4001307
65	W. R., Harcombe, W. J., Riehl, I., Dukovski, B. R., Granger, A., Betts, A. H., Lang, G., Bonilla, A., Kar, N., Leiby, P. Mehta, et al.. (2014). Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep., 7: 1104–1115 https://doi.org/10.1016/j.celrep.2014.03.070
66	B., Borer, M., Ataman, V. Hatzimanikatis, (2019). Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH). PLOS Comput. Biol., 15: e1007127 https://doi.org/10.1371/journal.pcbi.1007127
67	J., Geng, B., Ji, G., Li, F. pez-Isunza, (2021). CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention. Proc. Natl. Acad. Sci. U.S.A., 118: e2019336118 https://doi.org/10.1073/pnas.2019336118
68	E. J., Brien, J. M. Monk, B. Palsson, (2015). Using genome-scale models to predict biological capabilities. Cell, 161: 971–987 https://doi.org/10.1016/j.cell.2015.05.019
69	S., Stolyar, S., Van Dien, K. L., Hillesland, N., Pinel, T. J., Lie, J. A. Leigh, D. Stahl, (2007). Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol., 3: 92 https://doi.org/10.1038/msb4100131
70	I. E., El-Semman, F. H., Karlsson, S., Shoaie, I., Nookaew, T. H. Soliman, (2014). Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction. BMC Syst. Biol., 8: 41 https://doi.org/10.1186/1752-0509-8-41
71	F., Baldini, A., Heinken, L., Heirendt, S., Magnusdottir, R. M. T. Fleming, (2019). The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities. Bioinformatics, 35: 2332–2334 https://doi.org/10.1093/bioinformatics/bty941
72	H., Mendes-Soares, M., Mundy, L. M. Soares, (2016). MMinte: an application for predicting metabolic interactions among the microbial species in a community. BMC Bioinformatics, 17: 343 https://doi.org/10.1186/s12859-016-1230-3
73	A. R., Pacheco, M. Moel, (2019). Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun., 10: 103 https://doi.org/10.1038/s41467-018-07946-9
74	E. J., Brien, J. A., Lerman, R. L., Chang, D. R. Hyduke, B. Palsson, (2013). Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol. Syst. Biol., 9: 693 https://doi.org/10.1038/msb.2013.52
75	A. Khodayari, C. Maranas, (2016). A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Nat. Commun., 7: 13806 https://doi.org/10.1038/ncomms13806
76	N. Klitgord, (2010). Environments that induce synthetic microbial ecosystems. PLOS Comput. Biol., 6: e1001002 https://doi.org/10.1371/journal.pcbi.1001002
77	A. Heinken, (2015). Anoxic conditions promote species-specific mutualism between gut microbes In Silico. Appl. Environ. Microbiol., 81: 4049–4061 https://doi.org/10.1128/AEM.00101-15
78	K., Chen, Y., Gao, N., Mih, E. J., Brien, L. Yang, B. Palsson, (2017). Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proc. Natl. Acad. Sci. U.S.A., 114: 11548–11553 https://doi.org/10.1073/pnas.1705524114
79	L., Yang, N., Mih, A., Anand, J. H., Park, J., Tan, J. T., Yurkovich, J. M., Monk, C. J., Lloyd, T. E., Sandberg, S. W. Seo, et al.. (2019). Cellular responses to reactive oxygen species are predicted from molecular mechanisms. Proc. Natl. Acad. Sci. U.S.A., 116: 14368–14373 https://doi.org/10.1073/pnas.1905039116
80	B., Du, L., Yang, C. J., Lloyd, X. Fang, B. Palsson, (2019). Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli. PLOS Comput. Biol., 15: e1007525 https://doi.org/10.1371/journal.pcbi.1007525
81	G. C., diCenzo, M., Tesi, T., Pfau, A. Mengoni, (2020). Genome-scale metabolic reconstruction of the symbiosis between a leguminous plant and a nitrogen-fixing bacterium. Nat. Commun., 11: 2574 https://doi.org/10.1038/s41467-020-16484-2
82	A. B., Shreiner, J. Y. Kao, V. Young, (2015). The gut microbiome in health and in disease. Curr. Opin. Gastroenterol., 31: 69–75 https://doi.org/10.1097/MOG.0000000000000139
83	J., Qin, Y., Li, Z., Cai, S., Li, J., Zhu, F., Zhang, S., Liang, W., Zhang, Y., Guan, D. Shen, et al.. (2012). A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490: 55–60 https://doi.org/10.1038/nature11450
84	P. J., Turnbaugh, M., Hamady, T., Yatsunenko, B. L., Cantarel, A., Duncan, R. E., Ley, M. L., Sogin, W. J., Jones, B. A., Roe, J. P. Affourtit, et al.. (2009). A core gut microbiome in obese and lean twins. Nature, 457: 480–484 https://doi.org/10.1038/nature07540
85	A., Heinken, D. A., Ravcheev, F., Baldini, L., Heirendt, R. M. T. Fleming, (2019). Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease. Microbiome, 7: 75 https://doi.org/10.1186/s40168-019-0689-3
86	C., Diener, S. M. Gibbons, (2020). MICOM: Metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems, 5: e00606–e00619 https://doi.org/10.1128/mSystems.00606-19
87	M., Kumar, B., Ji, P., Babaei, P., Das, D., Lappa, G., Ramakrishnan, T. E., Fox, R., Haque, W. A., Petri, F. ckhed, et al.. (2018). Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: Lessons from genome-scale metabolic modeling. Metab. Eng., 49: 128–142 https://doi.org/10.1016/j.ymben.2018.07.018
88	S. Shoaie, (2014). Elucidating the interactions between the human gut microbiota and its host through metabolic modeling. Front. Genet., 5: 86 https://doi.org/10.3389/fgene.2014.00086
89	S., Shoaie, P., Ghaffari, P., Kovatcheva-Datchary, A., Mardinoglu, P., Sen, E., Pujos-Guillot, T., de Wouters, C., Juste, S., Rizkalla, J. Chilloux, et al.. (2015). Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab., 22: 320–331 https://doi.org/10.1016/j.cmet.2015.07.001
90	N., Swainston, K., Smallbone, H., Hefzi, P. D., Dobson, J., Brewer, M., Hanscho, D. C., Zielinski, K. S., Ang, N. J., Gardiner, J. M. Gutierrez, et al.. (2016). Recon 2. 2: from reconstruction to model of human metabolism. Metabolomics, 12: 109 https://doi.org/10.1007/s11306-016-1051-4
91	E., Brunk, S., Sahoo, D. C., Zielinski, A., Altunkaya, A., ger, N., Mih, F., Gatto, A., Nilsson, G. A., Preciat Gonzalez, M. K. Aurich, et al.. (2018). Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol., 36: 272–281 https://doi.org/10.1038/nbt.4072
92	N. C., Duarte, S. A., Becker, N., Jamshidi, I., Thiele, M. L., Mo, T. D., Vo, R. Srivas, B. Palsson, (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. U.S.A., 104: 1777–1782 https://doi.org/10.1073/pnas.0610772104
93	S. Sahoo, (2013). Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. Hum. Mol. Genet., 22: 2705–2722 https://doi.org/10.1093/hmg/ddt119
94	M. Zampieri, (2016). Model-based media selection to minimize the cost of metabolic cooperation in microbial ecosystems. Bioinformatics, 32: 1733–1739 https://doi.org/10.1093/bioinformatics/btw062
95	H., Du, J., Pan, D., Zou, Y., Huang, Y. Liu, (2022). Microbial active functional modules derived from network analysis and metabolic interactions decipher the complex microbiome assembly in mangrove sediments. Microbiome, 10: 224 https://doi.org/10.1186/s40168-022-01421-w
96	R., Mahadevan, J. S. Edwards, F. J. Doyle, (2002). Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys. J., 83: 1331–1340 https://doi.org/10.1016/S0006-3495(02)73903-9
97	T. J. Hanly, M. Henson, (2013). Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures. Biotechnol. Biofuels, 6: 44 https://doi.org/10.1186/1754-6834-6-44
98	T. J. Hanly, M. Henson, (2011). Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures. Biotechnol. Bioeng., 108: 376–385 https://doi.org/10.1002/bit.22954
99	F., Salimi, K. Zhuang, (2010). Genome-scale metabolic modeling of a clostridial co-culture for consolidated bioprocessing. Biotechnol. J., 5: 726–738 https://doi.org/10.1002/biot.201000159
100	B., nez, (2018). FLYCOP: metabolic modeling-based analysis and engineering microbial communities. Bioinformatics, 34: i954–i963 https://doi.org/10.1093/bioinformatics/bty561
101	A. V., Colarusso, I., Goodchild-Michelman, M. Rayle, A. Zomorrodi, (2021). Computational modeling of metabolism in microbial communities on a genome-scale. Curr. Opin. Syst. Biol., 26: 46–57 https://doi.org/10.1016/j.coisb.2021.04.001
102	B., nez, J. Torres-Bacete, (2020). Metabolic modelling approaches for describing and engineering microbial communities. Comput. Struct. Biotechnol. J., 19: 226–246 https://doi.org/10.1016/j.csbj.2020.12.003
103	A. P., Burgard, P. Pharkya, C. Maranas, (2003). Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng., 84: 647–657 https://doi.org/10.1002/bit.10803
104	D. Vitkup, G. Church, (2002). Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. U.S.A., 99: 15112–15117 https://doi.org/10.1073/pnas.232349399
105	N. Tepper, (2010). Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics, 26: 536–543 https://doi.org/10.1093/bioinformatics/btp704
106	H. S., Choi, S. Y., Lee, T. Y. Kim, H. Woo, (2010). In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol., 76: 3097–3105 https://doi.org/10.1128/AEM.00115-10
107	D. B., Bernstein, S., Sulheim, E. Almaas, (2021). Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol., 22: 64 https://doi.org/10.1186/s13059-021-02289-z
108	J. C., Xavier, K. R. Patil, (2017). Integration of biomass formulations of genome-scale metabolic models with experimental data reveals universally essential cofactors in prokaryotes. Metab. Eng., 39: 200–208 https://doi.org/10.1016/j.ymben.2016.12.002
109	C., Lieven, M. E., Beber, B. G., Olivier, F. T., Bergmann, M., Ataman, P., Babaei, J. A., Bartell, L. M., Blank, S., Chauhan, K. Correia, et al.. (2020). MEMOTE for standardized genome-scale metabolic model testing. Nat. Biotechnol., 38: 272–276 https://doi.org/10.1038/s41587-020-0446-y
110	D. G., Maghini, E. L., Moss, S. E. Vance, A. Bhatt, (2021). Improved high-molecular-weight DNA extraction, nanopore sequencing and metagenomic assembly from the human gut microbiome. Nat. Protoc., 16: 458–471 https://doi.org/10.1038/s41596-020-00424-x
111	W., Zheng, S., Zhao, Y., Yin, H., Zhang, D. M., Needham, E. D., Evans, C. L., Dai, P. J., Lu, E. J. Alm, D. Weitz, (2022). High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome. Science, 376: eabm1483 https://doi.org/10.1126/science.abm1483
112	J. C., Lachance, C. J., Lloyd, J. M., Monk, L., Yang, A. V., Sastry, Y., Seif, B. O., Palsson, S., Rodrigue, A. M., Feist, Z. A. King, et al.. (2019). BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLOS Comput. Biol., 15: e1006971 https://doi.org/10.1371/journal.pcbi.1006971
113	H., LengY., WangW., ZhaoS. M. Sievert. (2022) An expanded deep-branching thermophilic bacterial clade sheds light on the early evolution of bacteria. BioRxiv
114	J. M., Dreyfuss, J. D., Zucker, H. M., Hood, L. R., Ocasio, M. S. Sachs, J. Galagan, (2013). Reconstruction and validation of a genome-scale metabolic model for the filamentous fungus Neurospora crassa using FARM. PLOS Comput. Biol., 9: e1003126 https://doi.org/10.1371/journal.pcbi.1003126
115	J. Y., Ryu, H. U. Kim, S. Lee, (2019). Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proc. Natl. Acad. Sci. U.S.A., 116: 13996–14001 https://doi.org/10.1073/pnas.1821905116
116	G. L. Medlock, J. Papin, (2020). Guiding the refinement of biochemical knowledgebases with ensembles of metabolic networks and machine learning. Cell Syst., 10: 109–119.e3 https://doi.org/10.1016/j.cels.2019.11.006
117	K., Plaimas, J. P., Mallm, M., Oswald, F., Svara, V., Sourjik, R. Eils, (2008). Machine learning based analyses on metabolic networks supports high-throughput knockout screens. BMC Syst. Biol., 2: 67 https://doi.org/10.1186/1752-0509-2-67
118	J., Zhang, S. D., Petersen, T., Radivojevic, A., Ramirez, A., quez, E., Abeliuk, B. J., nchez, Z., Costello, Y., Chen, M. J. Fero, et al.. (2020). Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nat. Commun., 11: 4880 https://doi.org/10.1038/s41467-020-17910-1
119	T., Oyetunde, D., Liu, H. G. Martin, Y. Tang, (2019). Machine learning framework for assessment of microbial factory performance. PLoS One, 14: e0210558 https://doi.org/10.1371/journal.pone.0210558
120	C., Culley, S., Vijayakumar, G. Zampieri, (2020). A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proc. Natl. Acad. Sci. U.S.A., 117: 18869–18879 https://doi.org/10.1073/pnas.2002959117
121	J. H., Yang, S. N., Wright, M., Hamblin, D., McCloskey, M. A., Alcantar, L., bbers, A. J., Lopatkin, S., Satish, A., Nili, B. O. Palsson, et al.. (2019). A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell, 177: 1649–1661.e9 https://doi.org/10.1016/j.cell.2019.04.016
122	L., Li, X., Zhou, W. K. Ching, (2010). Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines. BMC Bioinformatics, 11: 501 https://doi.org/10.1186/1471-2105-11-501
123	D., DiMucci, M. Kon, (2018). Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem network. mSystems, 3: e00181–e18 https://doi.org/10.1128/mSystems.00181-18
124	O., Perez-Garcia, G. Lear, (2016). Metabolic network modeling of microbial interactions in natural and engineered environmental systems. Front. Microbiol., 7: 673 https://doi.org/10.3389/fmicb.2016.00673

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