Cybernetic modeling of iron‐limited growth and siderophore production

Alexander, Matthew L.; Ramkrishna, Doraiswami

doi:10.1002/bit.260380609

Cited by 25 publications

(5 citation statements)

References 15 publications

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“…steady-state iron concentration and therefore increased nutrient limitation stress; steady-state siderophore concentrations were a balance between synthesis and loss in the effluent (the relevant theory is presented in the supplemental material). Consistent with previous research, glucose-limited cultures had a higher-thanbackground siderophore concentration for the lower dilution rates (61).…”

Section: Analysis Of Iron-limited Growthsupporting

confidence: 90%

Physiological and Proteomic Analysis of Escherichia coli Iron-Limited Chemostat Growth

2014

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Iron bioavailability is a major limiter of bacterial growth in mammalian host tissue and thus represents an important area of study. Escherichia coli K-12 metabolism was studied at four levels of iron limitation in chemostats using physiological and proteomic analyses. The data documented an E. coli acclimation gradient where progressively more severe iron scarcity resulted in a larger percentage of substrate carbon being directed into an overflow metabolism accompanied by a decrease in biomass yield on glucose. Acetate was the primary secreted organic by-product for moderate levels of iron limitation, but as stress increased, the metabolism shifted to secrete primarily lactate (ϳ70% of catabolized glucose carbon). Proteomic analysis reinforced the physiological data and quantified relative increases in glycolysis enzyme abundance and decreases in tricarboxylic acid (TCA) cycle enzyme abundance with increasing iron limitation stress. The combined data indicated that E. coli responds to limiting iron by investing the scarce resource in essential enzymes, at the cost of catabolic efficiency (i.e., downregulating high-ATP-yielding pathways containing enzymes with large iron requirements, like the TCA cycle). Acclimation to iron-limited growth was contrasted experimentally with acclimation to glucose-limited growth to identify both general and nutrient-specific acclimation strategies. While the iron-limited cultures maximized biomass yields on iron and increased expression of iron acquisition strategies, the glucose-limited cultures maximized biomass yields on glucose and increased expression of carbon acquisition strategies. This study quantified ecologically competitive acclimations to nutrient limitations, yielding knowledge essential for understanding medically relevant bacterial responses to host and to developing intervention strategies.

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Section: Analysis Of Iron-limited Growthsupporting

confidence: 90%

Physiological and Proteomic Analysis of Escherichia coli Iron-Limited Chemostat Growth

2014

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“…As shown in Figure 5, the maximum cell concentrations reached were practically the same in all systems, including the one without any added FeCl 3 . The same finding has been observed with other species (Alexander and Ramkrishna, 1991). Fe was therefore not an effective growth-limiting nutrient.…”

Section: Comparison Among N- Mg- and Fe-limited Systemssupporting

confidence: 75%

Rhamnolipid production byPseudomonas aeruginosa under denitrification: Effects of limiting nutrients and carbon substrates

Chayabutra

2000

Biotechnol. Bioeng.

136

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“…Alexander and Ramkrishna23 developed a cybernetic model for the production of siderophores (iron‐chelating agents) associated with iron‐limiting conditions. Their data showed that, while final biomass levels were governed by exhaustion of the carbon source, iron limitations also contributed to determination of the biomass yield.…”

Section: Cybernetic Variables and Their Evaluationmentioning

confidence: 99%

Dynamic models of metabolism: Review of the cybernetic approach

Ramkrishna¹,

Song²

2012

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com).The cybernetic approach to metabolic modeling tracing its progress from its early beginnings to its current state with regard to its relationship to other modeling approaches, applications to bioprocess modeling, metabolic engineering, and future prospects are described. The framework is shown to handle large metabolic networks in making dynamic predictions from limited data with looming prospects of extending to genome scale networks. V V C 2012 American Institute of Chemical Engineers AIChE J, 58: 986-997, 2012 Keywords: dynamic metabolic models, the cybernetic approach, metabolic engineering, elementary modes, cellular regulation where W j denotes the species whose concentration is w j . Eq. 1 represents n r reactions of metabolism which subsume both transport and chemical reaction with a ij the stoichiometric coefficient which, as per the usual convention, is positive, negative, or zero, in respective accordance with W j being a product, reactant, or nonparticipant in the ith reaction. The rate associated with the ith reaction is denoted by r i . As each reaction is catalyzed by a specific enzyme, the level of this enzyme and its activity would determine the rate of the reaction besides the concentrations (as dictated by the kinetics) of species participating in the reaction.Predictions (a) based on measurements of glucose alone leads to good predictions of biomass, formate, ethanol but not of lactate and succinate. When measurements of glucose and lactate are both used for model identification (b), all predictions (shown in continuous lines) are considerably improved. Figure 13. Cybernetic model simulations of Namjoshi et al. 52 alongside with experimental data of Europa et al., 50 showing multiple steady states in continuous cultures of hybridoma cells.

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Cybernetic modeling of iron‐limited growth and siderophore production

Cited by 25 publications

References 15 publications

Physiological and Proteomic Analysis of Escherichia coli Iron-Limited Chemostat Growth

Physiological and Proteomic Analysis of Escherichia coli Iron-Limited Chemostat Growth

Rhamnolipid production byPseudomonas aeruginosa under denitrification: Effects of limiting nutrients and carbon substrates

Dynamic models of metabolism: Review of the cybernetic approach

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