Optimizing genome-scale network reconstructions

Monk, Jonathan M.; Nogales, Juan; Palsson, Bernhard Ø.

doi:10.1038/nbt.2870

Cited by 191 publications

(171 citation statements)

References 53 publications

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“…Moreover, despite the finding that flux coupling analysis is sensitive to missing reactions (Marashi and Bockmayr 2011), we observe that the determined driver reactions are not significantly altered when removing up to 15% of the reactions from the network of E. coli central metabolism, or up to 25% of the reactions from the genome-scale metabolic network (P values < 0.05 for the Pearson correlation coefficients of driver frequencies with and without removal of reactions) (see "Sensitivity to missing reactions" in the Supplemental Material). Altogether, these robustness tests demonstrate the power of our method in making predictions from incomplete network reconstructions (Monk et al 2014). …”

Section: Control Of Fluxes In Metabolic Networkmentioning

confidence: 67%

Control of fluxes in metabolic networks

et al. 2016

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Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.[Supplemental material is available for this article.]Understanding how cellular systems are controlled on a genomescale is a central issue in biology and medicine. Metabolic networks are at the center of systems biology approaches to unraveling cellular control, because metabolism carries the life-sustaining cellular functions shaping the molecular phenotype (Sweetlove and Ratcliffe 2011). The steady-state principle and physico-chemical constraints (e.g., mass balance and thermodynamics) have been employed to reduce the number of considered network states, facilitating the prediction of genotype-phenotype relationships and intervention strategies for biotechnological or medical purposes (McCloskey et al. 2013). In particular, flux balance analysis and variations thereof have been successfully applied to the metabolic networks of unicellular organisms to predict their metabolic and cellular phenotypes (Varma and Palsson 1994). Yet, those approaches are biased (Lewis et al. 2012) because they restrict the flux space to an a priori specified reference state by assuming a cellular objective to be optimized by the organism (Schuetz et al. 2007). While optimization of biomass yield has proven useful for unicellular organisms, identification of a suitable objective for multicellular organisms remains a nontrivial endeavor (Sweetlove and Ratcliffe 2011). Other approaches, e.g., elementary flux modes (Schuster and Schuster 1993) and extreme pathways analyses (Schilling et al. 2000), do not assume a cellular objective and hence are unbiased. However, despite extensive studies and recent advances (Terzer and Stelling 2008), these unbiased approaches are limited to rather small networks due to their intrinsic computational complexity. We still lack an unbiased computational approach for systematically studying the control of large-scale metabolic networks.Here, we develop such an approach by employing the flux coupling between reactio...

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Section: Control Of Fluxes In Metabolic Networkmentioning

confidence: 67%

Control of fluxes in metabolic networks

et al. 2016

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“…Genomescale models have proven to be useful tools for the targeted engineering of such organisms (Monk et al, 2014). These genome-scale network reconstructions can help us understand the compartmental organization of metabolism within an organism, discover new metabolic gene functions, guide adaptive evolution approaches, and optimize the production of value-added compounds, such as pigments and lipids (Espinosa-Gonzalez et al, 2014).…”

mentioning

confidence: 99%

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Zuñiga

Huelsman

et al. 2016

Plant Physiol.

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The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

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“…These methods were segmented into three distinct categories, and their functionalities and main applications were analyzed. As the number of genome-scale reconstructions increases each year and the complexity of these models is able to capture more and more information (188), new methods capable of exploring this information to generate new knowledge are expected to arise, holding the promise of finally bridging the gap between the academic-grade efforts and the industry-grade standards required for a full adoption of CSOMs as a standard tool for guiding metabolic engineering efforts. Nevertheless, this adoption is possible only if underlying tools such as modeling and simulation are also further developed and validated.…”

Section: Discussionmentioning

confidence: 99%

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Maia

Rocha

2016

Microbiol Mol Biol Rev

116

107

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SUMMARYShifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together within silicostrain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the mainin silicoconstraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

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Optimizing genome-scale network reconstructions

Cited by 191 publications

References 53 publications

Control of fluxes in metabolic networks

Control of fluxes in metabolic networks

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

In SilicoConstraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

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