Computational tools for metabolic engineering

Copeland, Wilbert B.; Bartley, Bryan; Chandran, Deepak; Galdzicki, Michal; Kim, Kyung H.; Sleight, Sean C.; Maranas, Costas D.; Sauro, Herbert M.

doi:10.1016/j.ymben.2012.03.001

Cited by 90 publications

(49 citation statements)

References 115 publications

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“…Several optimization-based computational techniques have been developed to achieve this aim [24] (Table 1b). The scope of these approaches has been expanded through the use of synthetic biology tools.…”

Section: Modeling-driven Pathway Engineeringmentioning

confidence: 99%

Advances in de novo strain design using integrated systems and synthetic biology tools

Khodayari

Chowdhury

et al. 2015

Current Opinion in Chemical Biology

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Recent efforts in expanding the range of biofuel and biorenewable molecules using microbial production hosts have focused on the introduction of non-native pathways in model organisms and the bio-prospecting of non-model organisms with desirable features. Current challenges lie in the assembly and coordinated expression of the (non-)native pathways and the elimination of competing pathways and undesirable regulation. Several systems and synthetic biology approaches providing contrasting top-down and bottom-up strategies, respectively, have been developed. In this review, we discuss recent advances in both in silico and experimental approaches for metabolic pathway design and engineering, with a critical assessment of their merits and remaining challenges.

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Section: Modeling-driven Pathway Engineeringmentioning

confidence: 99%

Advances in de novo strain design using integrated systems and synthetic biology tools

Khodayari

Chowdhury

et al. 2015

Current Opinion in Chemical Biology

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“…Where the intention is to manipulate a given pathway to produce a specific outcome, applying the model can potentially generate a variety of alternative strategies, which can lead to a directed experimental validation (Copeland et al, 2012). However, we should bear in mind that the computational model represents an in silico prediction of what is supposed to occur in reality and it is the biologist who can determine appropriate experiments and the approach to be used.…”

Section: Analytical Investigationmentioning

confidence: 99%

Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

2014

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Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.

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“…For a more detailed introduction to computational tools, a recent review paper summarized major computational models and Review. Mapping microbial metabolism J. K.-H. Tang et al 2775 software [80], covering areas of both metabolic reconstruction tools and 13 C flux analysis platforms. Finally, new mathematical algorithms are proposed to reformulate 13 C-MFA problems and simplify the flux calculation.…”

Section: Metabolism Profiling Using Integrated Approachesmentioning

confidence: 99%

Recent advances in mapping environmental microbial metabolisms through¹³C isotopic fingerprints

Tang

You

Blankenship

et al. 2012

J. R. Soc. Interface.

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After feeding microbes with a defined 13 C substrate, unique isotopic patterns (isotopic fingerprints) can be formed in their metabolic products. Such labelling information not only can provide novel insights into functional pathways but also can determine absolute carbon fluxes through the metabolic network via metabolic modelling approaches. This technique has been used for finding pathways that may have been mis-annotated in the past, elucidating new enzyme functions, and investigating cell metabolisms in microbial communities. In this review paper, we summarize the applications of 13 C approaches to analyse novel cell metabolisms for the past 3 years. The isotopic fingerprints (defined as unique isotopomers useful for pathway identifications) have revealed the operations of the Entner-Doudoroff pathway, the reverse tricarboxylic acid cycle, new enzymes for biosynthesis of central metabolites, diverse respiration routes in phototrophic metabolism, co-metabolism of carbon nutrients and novel CO 2 fixation pathways. This review also discusses new isotopic methods to map carbon fluxes in global metabolisms, as well as potential factors influencing the metabolic flux quantification (e.g. metabolite channelling, the isotopic purity of 13 C substrates and the isotopic effect). Although 13 C labelling is not applicable to all biological systems (e.g. microbial communities), recent studies have shown that this method has a significant value in functional characterization of poorly understood micro-organisms, including species relevant for biotechnology and human health.

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Computational tools for metabolic engineering

Cited by 90 publications

References 115 publications

Advances in de novo strain design using integrated systems and synthetic biology tools

Advances in de novo strain design using integrated systems and synthetic biology tools

Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

Recent advances in mapping environmental microbial metabolisms through¹³C isotopic fingerprints

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Computational tools for metabolic engineering

Cited by 90 publications

References 115 publications

Advances in de novo strain design using integrated systems and synthetic biology tools

Advances in de novo strain design using integrated systems and synthetic biology tools

Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

Recent advances in mapping environmental microbial metabolisms through13C isotopic fingerprints

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Product

Resources

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Recent advances in mapping environmental microbial metabolisms through¹³C isotopic fingerprints