d-OptCom: Dynamic Multi-level and Multi-objective Metabolic Modeling of Microbial Communities

Zomorrodi, Ali R.; Islam, Mohammad Mazharul; Maranas, Costas D.

doi:10.1021/sb4001307

Cited by 191 publications

(166 citation statements)

References 67 publications

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“…These tissuespecific models will follow community (Zomorrodi and Maranas, 2012;Zomorrodi et al, 2014) and multitissue human model (Duarte et al, 2007;Bordbar et al, 2011;Thiele et al, 2013) reconstruction principles. The tissues can be linked using intertissue transport reactions, with the stalk tissue acting as the central transporter among the various tissues and particularly to the developing ear (Cañas et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

et al. 2014

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Maize (Zea mays) is an important C 4 plant due to its widespread use as a cereal and energy crop. A second-generation genomescale metabolic model for the maize leaf was created to capture C 4 carbon fixation and investigate nitrogen (N) assimilation by modeling the interactions between the bundle sheath and mesophyll cells. The model contains gene-protein-reaction relationships, elemental and charge-balanced reactions, and incorporates experimental evidence pertaining to the biomass composition, compartmentalization, and flux constraints. Condition-specific biomass descriptions were introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lignocellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of information from the MetaCrop and MaizeCyc databases, this updated model spans 5,824 genes, 8,525 reactions, and 9,153 metabolites, an increase of approximately 4 times the size of the earlier iRS1563 model. Transcriptomic and proteomic data have also been used to introduce regulatory constraints in the model to simulate an N-limited condition and mutants deficient in glutamine synthetase, gln1-3 and gln1-4. Model-predicted results achieved 90% accuracy when comparing the wild type grown under an N-complete condition with the wild type grown under an N-deficient condition.

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Section: Resultsmentioning

confidence: 99%

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

et al. 2014

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“…Experimental data for the community (such as composition) and organisms (such as uptake rate), if available, can be incorporated to improve the quality of predictions. Zomorrodi et al [73] recently developed a dynamic version of OptCom (called d-OptCom), which we discuss in Section 6.3.…”

Section: Stoichiometric Modeling Of Multiple Speciesmentioning

confidence: 99%

“…Zomorrodi et al [73] proposed a general framework for the dynamic simulation of microbial community by extending OptCom. In contrast to DyMMM that considers a community-level objective alone, d-OptCom solves a bi-level optimization problem for which both species-and community-level objectives should be specified.…”

Section: Direct Couplingmentioning

confidence: 99%

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Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

et al. 2014

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Microorganisms in nature form diverse communities that dynamically change in structure and function in response to environmental variations. As a complex adaptive system, microbial communities show higher-order properties that are not present in individual microbes, but arise from their interactions. Predictive mathematical models not only help to understand the underlying principles of the dynamics and emergent properties of natural and synthetic microbial communities, but also provide key knowledge required for engineering them. In this article, we provide an overview of mathematical tools that include not only current mainstream approaches, but also less traditional approaches that, in our opinion, can be potentially useful. We discuss a broad range of methods ranging from low-resolution supra-organismal to high-resolution individual-based modeling. Particularly, we highlight the integrative approaches that synergistically combine disparate methods. In conclusion, we provide our outlook for the key aspects that should be further developed to move microbial community modeling towards greater predictive power.

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“…147 Considering the numerous species within the gut microbiome, integration of multiple single species and reconstruction of the gut microbial community model remain too complex and need to be scaled up. Although some modeling frameworks (ie, DMMM, 145 OptCom, 159 d-OptCom, 160 cFBA, 161 COMETS, 162 ) for community metabolic modeling have been developed, several conceptual challenges still need to be addressed. 126 For example, the composition of the gut microbial community is highly flexible and environmentally dependent, and traditional objective functions (eg, maximizing biomass) are not appropriate and need to be replaced by putative energy, biosynthesis principles, or the tradeoff between different species.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

New insight into the gut microbiome through metagenomics

Ji¹,

Nielsen

2015

AGG

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Abstract:The human gut is colonized by different types of microorganisms, which are known to play important roles in the human host by maintaining physiological homeostasis. The human host provides a nutrient-rich environment, and the microbiota provides some necessary functions that humans cannot perform. A comprehensive analysis of the human gut microbiome is thus important for revealing the mechanisms of these host-microbe interactions. The development of high-throughput sequencing technology and related computational frameworks enables exploration of the metabolic interactions and their roles in human health and diseases. Herein, we describe the metagenomic methods used in human gut microbiome studies and review the roles of gut microbiota as well as the integrative analyses of metagenomic data with other omics data. Finally, we discuss the application of constraint-based modeling to elucidate the microbemicrobe interaction and host-microbe interaction in the human gut microbiota.

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d-OptCom: Dynamic Multi-level and Multi-objective Metabolic Modeling of Microbial Communities

Cited by 191 publications

References 67 publications

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

New insight into the gut microbiome through metagenomics

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