ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Mao, Zhitao; Zhao, Xin; Yang, Xue; Zhang, Peiji; Du, Jiawei; Yuan, Qianqian; Ma, Hongwu

doi:10.20944/preprints202112.0048.v1

Cited by 4 publications

(23 citation statements)

References 36 publications

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“…This is ok for developing a kinetic model of a metabolic pathway but far from enough for whole cell model. In recent years, enzyme-constrained models (ECMs), which integrate enzymatic constraints into GEMs, have been shown to be more powerful and reliable in simulating/predicting cellular phenotype [84][85][86]. ECMs require a whole network level coverage of enzyme kinetic parameter values for accurate prediction.…”

Section: Prediction Of Kinetic Parametersmentioning

confidence: 99%

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Shi

Liao

et al. 2022

BioDesign Res

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Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.

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Section: Prediction Of Kinetic Parametersmentioning

confidence: 99%

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Shi

Liao

et al. 2022

BioDesign Res

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“…As discussed in the Introduction section, quantitative subunit information of an enzyme is essential to correctly determine its MW but is missing in the GPR relationships in the models. We have manually collected the subunit number of each protein in our previous approach for constructing the enzyme-constrained model of E. coli eciML1515 [18]. Here we used a new automatic method to acquire the quantitative subunit information by extending GPRuler to resolve the subunit number of a protein based on information in the 'Interaction information' section in UniProt.…”

Section: Acquisition Of Quantitative Subunit Compositionmentioning

confidence: 99%

“…, is the turnover number of enzymes that catalyze reaction i; denotes the molecular weight of enzyme i; is the saturation coefficient for enzyme i, whereby we use an average value of 0.5 for all the enzymes [18]; of 0.56 is the average protein content in most microbial cells [15]; is the total mass fraction of all cellular enzymes in our ecGEM.…”

Section: Construction Of Ecicw773mentioning

confidence: 99%

“…In 2007, Zhang et al constructed the FBAwMC model by introducing both a crowding coefficient and cell volume constraint to limit the space occupied by enzymes [13]. Subsequently, the researchers developed other protein resource integration methods [14], which referred to as enzyme-constrained genome-scale models (ecGEMs), including MOMENT [15], GECKO [16], AutoPACMEN [17] and ECMpy [18]. The GECKO method was reported in 2017 and was applied to construct an enzyme-constrained model of Saccharomyces cerevisiae by adding many pseudo-metabolites to represent enzyme utilization and including kcat values to expand the stoichiometric matrix.…”

Section: Introductionmentioning

confidence: 99%

“…It was used to construct an enzyme-constrained model of Escherichia coli which only introduced one pseudo-reaction and pseudo-metabolite into the stoichiometric matrix. Different from GECKO and AutoPACMEN, ECMpy simply adds a constraint on the total amount of enzymes and does not require modification of the stoichiometric matrix, while providing higher prediction accuracy for the simulation of the E. coli growth rate [18]. Currently, ecGEMs have been constructed for many species, such as E. coli [18,22]., S. cerevisiae [16], Yarrowia lipolytica [19], Aspergillus niger [23], and Bacillus subtilis [24].…”

Section: Introductionmentioning

confidence: 99%

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Construction and Analysis of an Enzyme‐constrained Metabolic Model of <em>Corynebacterium glutamicum</em>

Niu¹,

Mao²,

Mao³

et al. 2022

Preprint

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Genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only consid-ers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experi-mentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineer-ing targets. Here we present the first genome-scale enzyme-constrained model (eciCW773) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of pheno-types and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used eciCW773 to identify several gene modifica-tion targets for L-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

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The necessity of considering enzymes as compartments in constraint-based genome-scale metabolic models

Yang

Mao²,

Huang

et al. 2022

Preprint

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As the most widespread and practical digital representations of living cells, metabolic network models have become increasingly precise and accurate. By integrating cellular resources and abiotic constraints, the prediction functions were significantly expanded in recent years. However, we found that if unreasonable modeling methods were adopted due to the lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of L-serine and L-tryptophan. Specific analysis explains the application method of EcoETM-like model, demonstrating its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Finally, we provide a preliminary comparison of the thermodynamic feasibility of ammonia and glutamine as amino donors, which revealed that the direct utilization of ammonia does not have a decisive impact on the thermodynamic feasibility of the anthranilate pathway. Our work is of great value for the structural improvement of constraints-based models.

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ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Cited by 4 publications

References 36 publications

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Construction and Analysis of an Enzyme‐constrained Metabolic Model of <em>Corynebacterium glutamicum</em>

The necessity of considering enzymes as compartments in constraint-based genome-scale metabolic models

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