How adaptive evolution reshapes metabolism to improve fitness: recent advances and future outlook

Long, Christopher P.; Antoniewicz, Maciek R.

doi:10.1016/j.coche.2018.11.001

Cited by 34 publications

(20 citation statements)

References 56 publications

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“…Metabolic flux analysis (MFA) [40] is a widely used tool for metabolic engineering, which has also become more common in other fields, such as biomedical applications [41] and basic science [42,43]. MFA follows the same principles as FBA but does not assume any functional objective, e.g., the growth rate or ATP production.…”

Section: Metabolic Flux Analysismentioning

confidence: 99%

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

et al. 2020

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Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

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Section: Metabolic Flux Analysismentioning

confidence: 99%

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

et al. 2020

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“…Genetic diversity is the basis of evolution, and the higher the degree of diversity, the greater the likelihood of getting a complex phenotype. Adaptive laboratory evolution (ALE) is still one of the most popular methods for generation of variants on a genome scale . The database of ALEdb (http://aledb.org) has collected over 82,312 variants from the literature over the years (as of March 9, 2019) .…”

Section: Directed Evolution On the Genome Levelmentioning

confidence: 99%

Biosystems design by directed evolution

Wang

Zhao

2019

AIChE Journal

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Through iterative rounds of genetic diversification and screening or selection, directed evolution has been widely used to engineer relatively simple biosystems such as nucleic acids and proteins with desired functions. In addition, directed evolution has played an important role in engineering more complex biosystems such as pathways and genomes. Since 2013, directed evolution has been further explored for biosystems design with numerous newly developed techniques that have enabled design and engineering of proteins, pathways, and genomes in a much more effective manner. In this review, we will highlight the abiotic biotransformations arisen from directed evolution and novel strategies for continuous evolution in vivo and ultrahigh‐throughput screening. We will also discuss the future challenges and opportunities of applying directed evolution for biosystems design.

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“…For example, the yeast Saccharomyces cerevisiae switches from fermentation to respiration upon glucose depletion [ 5 ], and Escherichia coli exhibits drastically different ribosome content between different nutrient conditions [ 6 , 7 ]. Moreover, in laboratory long-term evolution studies of microbes, adaptive mutations consistently emerge that reshape metabolism [ 8 – 11 ]. Such short-term and long-term adjustments of metabolic strategies presumably confer fitness benefits, and it is important to map metabolic strategies onto these benefits to better understand the regulation and evolution of microbial metabolism.…”

Section: Introductionmentioning

confidence: 99%

Modeling microbial metabolic trade-offs in a chemostat

Liu

et al. 2020

PLoS Comput Biol

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Microbes face intense competition in the natural world, and so need to wisely allocate their resources to multiple functions, in particular to metabolism. Understanding competition among metabolic strategies that are subject to trade-offs is therefore crucial for deeper insight into the competition, cooperation, and community assembly of microorganisms. In this work, we evaluate competing metabolic strategies within an ecological context by considering not only how the environment influences cell growth, but also how microbes shape their chemical environment. Utilizing chemostat-based resource-competition models, we exhibit a set of intuitive and general procedures for assessing metabolic strategies. Using this framework, we are able to relate and unify multiple metabolic models, and to demonstrate how the fitness landscape of strategies becomes intrinsically dynamic due to speciesenvironment feedback. Such dynamic fitness landscapes produce rich behaviors, and prove to be crucial for ecological and evolutionarily stable coexistence in all the models we examined.

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How adaptive evolution reshapes metabolism to improve fitness: recent advances and future outlook

Cited by 34 publications

References 56 publications

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Biosystems design by directed evolution

Modeling microbial metabolic trade-offs in a chemostat

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