Flux Imbalance Analysis and the Sensitivity of Cellular Growth to Changes in Metabolite Pools

Reznik, Ed; Mehta, Pankaj; Segrè, Daniel

doi:10.1371/journal.pcbi.1003195

Cited by 68 publications

(69 citation statements)

References 49 publications

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“…The directional changes of fluxsum levels between the N 2 WT condition and the N + WT condition, as well as the GS mutant conditions and the N + WT condition, are qualitatively compared with the directional change in experimentally measured concentration levels. These analyses reveal similar trends to the recently developed flux imbalance analysis (Reznik et al, 2013), which makes use of dual variable values associated with metabolite balances to infer the effect of concentration changes on the objective function value.…”

supporting

confidence: 64%

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

et al. 2014

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Maize (Zea mays) is an important C 4 plant due to its widespread use as a cereal and energy crop. A second-generation genomescale metabolic model for the maize leaf was created to capture C 4 carbon fixation and investigate nitrogen (N) assimilation by modeling the interactions between the bundle sheath and mesophyll cells. The model contains gene-protein-reaction relationships, elemental and charge-balanced reactions, and incorporates experimental evidence pertaining to the biomass composition, compartmentalization, and flux constraints. Condition-specific biomass descriptions were introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lignocellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of information from the MetaCrop and MaizeCyc databases, this updated model spans 5,824 genes, 8,525 reactions, and 9,153 metabolites, an increase of approximately 4 times the size of the earlier iRS1563 model. Transcriptomic and proteomic data have also been used to introduce regulatory constraints in the model to simulate an N-limited condition and mutants deficient in glutamine synthetase, gln1-3 and gln1-4. Model-predicted results achieved 90% accuracy when comparing the wild type grown under an N-complete condition with the wild type grown under an N-deficient condition.

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confidence: 64%

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

et al. 2014

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“…Determining the shadow price for a steady-state constraint for metabolite X then amounts to determining how the imbalance of that metabolite affects the objective. This has recently been termed flux imbalance analysis (Reznik et al, 2013). Therefore, extensions of FBA allow insights to be made as to the effect of changes in metabolite pools on the performance of the biological system.…”

Section: General Principles Of Inference and The Prediction Of Metabomentioning

confidence: 99%

“…Flux imbalance analysis explores the sensitivity of metabolic optima to violations of the steady-state constraints (Reznik et al, 2013). The method does not directly integrate metabolite levels but can be used to elucidate the processes that control intracellular metabolites in the cell.…”

Section: Linking Fluxes To Metabolitesmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

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“…In chemostat culture, cells are grown at a set dilution rate to a nutrient-limited steady state, at which all inputs equal all outputs. This steady state is highly desirable for modeling applications (e.g., Knijnenburg et al 2009;Reznik et al 2013) and for perturbation experiments (e.g., Ronen 2006;McIsaac et al 2012). The precise control over growth rate also allows matching of growth rates between wild-type cultures and more slowly growing mutants, or among diverse strain backgrounds, by forcing the cells to grow at a steady state that is below the maximum growth rate of both (e.g., Hayes et al 2002;Torres et al 2007;Skelly et al 2013).…”

Section: Continuous Culturementioning

confidence: 99%

Chemostat Culture for Yeast Physiology and Experimental Evolution

Dunham

Kerr

Miller

et al. 2017

Cold Spring Harb Protoc

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Continuous culture provides many benefits over the classical batch style of growing yeast cells. Steadystate cultures allow for precise control of growth rate and environment. Cultures can be propagated for weeks or months in these controlled environments, which is important for the study of experimental evolution. Despite these advantages, chemostats have not become a highly used system, in large part because of their historical impracticalities, including low throughput, large footprint, systematic complexity, commercial unavailability, high cost, and insufficient protocol availability. However, we have developed methods for building a relatively simple, low-cost, small footprint array of chemostats that can be run in multiples of 32. This "ministat array" can be applied to problems in yeast physiology and experimental evolution. BATCH CULTUREThe most common method of growing yeast cell cultures is the standard "batch" culture. In this regime, a small number of cells are inoculated into nutrient-rich medium and allowed to divide at maximal growth rate through nutrient exhaustion and into saturation. Sampling of the culture for the experiment of choice is most typically done from the saturated culture or from cells growing in midlog phase before the diauxic shift. Batch culture provides a number of obvious benefits-it is easy, uses glassware commonly available in any laboratory, and is consistent with a vast literature. However, batch cultures can be problematic for certain applications. When comparing cells of different genotypes, for example, differences in the maximal growth rate are a common phenotype. Many other phenotypes co-vary with growth rate, leading to a nonspecific suite of cell biological, gene expression, and other physiological changes that may not be directly related to the mutation of interest (Regenberg et al. 2006;Castrillo et al. 2007;Brauer et al. 2008).Batch culture has also been a standard for long-term evolution experiments. Depending on the question being asked, batch culture with serial dilution can be a simple and scalable solution (e.g., Zeyl et al. 2003;Lang et al. 2011). However, such cultures are only rarely propagated in a relatively constant environment and growth rate regime because doing so can require a heroic sampling regimen where back dilution is performed multiple times per day (e.g., Torres et al. 2010). More typically, cultures are allowed to exhaust nutrients before transfer to new medium, leading to a variation in growth rate and nutrient access over the evolutionary time course. These discontinuities in selective pressure can lead to complex subpopulation structures in which different genotypes specialize for the various stages of the growth cycle (e.g., dividing a few extra times or failing to die after saturation, shortening lag phase,

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Flux Imbalance Analysis and the Sensitivity of Cellular Growth to Changes in Metabolite Pools

Cited by 68 publications

References 49 publications

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

Inference and prediction of metabolic network fluxes

Chemostat Culture for Yeast Physiology and Experimental Evolution

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