Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures

Kurata, Hiroyuki; Sugimoto, Yurie

doi:10.1016/j.jbiosc.2017.09.005

Cited by 30 publications

(39 citation statements)

References 17 publications

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“…This can allow for future more realistic uses of the RAF theory in biological contexts, particularly in biochemistry, with the introduction of kinetic constants in the network representation. Although it remains a challenge to obtain kinetic data at a genome scale for real cells, advances have been achieved with E. coli [30,31] and erythrocyte models [32] and others are expected to emerge as new methodologies will convert thermodynamic and metabolomic data to kinetic constants [33]. The advantage of RAF theory here is that it can be applied to networks of any size, as long as a CRS can be drawn for the network [10].…”

Section: Rafs With Reaction Ratesmentioning

confidence: 99%

Autocatalytic networks in biology: structural theory and algorithms

Steel

Hordijk

Xavier

2019

J. R. Soc. Interface.

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Self-sustaining autocatalytic networks play a central role in living systems, from metabolism at the origin of life, simple RNA networks and the modern cell, to ecology and cognition. A collectively autocatalytic network that can be sustained from an ambient food set is also referred to more formally as a ‘reflexively autocatalytic food-generated’ (RAF) set. In this paper, we first investigate a simplified setting for studying RAFs, which is nevertheless relevant to real biochemistry and which allows an exact mathematical analysis based on graph-theoretic concepts. This, in turn, allows for the development of efficient (polynomial-time) algorithms for questions that are computationally intractable (NP-hard) in the general RAF setting. We then show how this simplified setting for RAF systems leads naturally to a more general notion of RAFs that are ‘generative’ (they can be built up from simpler RAFs) and for which efficient algorithms carry over to this more general setting. Finally, we show how classical RAF theory can be extended to deal with ensembles of catalysts as well as the assignment of rates to reactions according to which catalysts (or combinations of catalysts) are available.

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Section: Rafs With Reaction Ratesmentioning

confidence: 99%

Autocatalytic networks in biology: structural theory and algorithms

Steel

Hordijk

Xavier

2019

J. R. Soc. Interface.

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“…Kurata18 : This model (21) was designed to be used as metabolic phenotype prediction tool and is an update from the model published by (22) . The model was trained on batch fermentation data from (14) .…”

Section: Fig 1 Lineage Of E Coli Dynamic Modelsmentioning

confidence: 99%

Benchmarking kinetic models ofEscherichia colimetabolism

Shepelin

Machado

Nielsen

et al. 2020

Preprint

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Predicting phenotype from genotype is the holy grail of quantitative systems biology.Kinetic models of metabolism are among the most mechanistically detailed tools for phenotype prediction. Kinetic models describe changes in metabolite concentrations as a function of enzyme concentration, reaction rates, and concentrations of metabolic effectors uniquely enabling integration of multiple omics data types in a unifying mechanistic framework. While development of such models for Escherichia coli has been going on for almost twenty years, multiple separate models have been established and systematic independent benchmarking studies have not been performed on the full set of models available. In this study we compared systematically all recently published kinetic models of the central carbon metabolism of Escherichia coli . We assess the ease of use of the models, their ability to include omics data as input, and the accuracy of prediction of central carbon metabolic flux phenotypes. We conclude that there is no clear winner among the models when considering the resulting tradeoffs in performance

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“…Principles from control engineering have gained ground in synthetic biology [19] and optimal control ideas have revealed design principles in natural metabolic systems [65,72], but their broader application to dynamic pathway control remains less explored. A potential area for future development is the use of mathematical optimization for circuit design [53] coupled with detailed kinetic models of metabolism [32,49,41,14,27]. Optimization of control architectures also faces significant computational challenges, as the sheer number of circuit designs and tunable parameters may lead to optimization problems that cannot be solved in feasible time.…”

Section: Assembling Parts To Design Control Circuitsmentioning

confidence: 99%

Dynamic metabolic control: towards precision engineering of metabolism

Liu

Mannan

Han

et al. 2018

Journal of Industrial Microbiology and Biotechnology

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Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the "push-pull-block" strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

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Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures

Cited by 30 publications

References 17 publications

Autocatalytic networks in biology: structural theory and algorithms

Autocatalytic networks in biology: structural theory and algorithms

Benchmarking kinetic models ofEscherichia colimetabolism

Dynamic metabolic control: towards precision engineering of metabolism

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