Limits...
Determining host metabolic limitations on viral replication via integrated modeling and experimental perturbation.

Birch EW, Ruggero NA, Covert MW - PLoS Comput. Biol. (2012)

Bottom Line: The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production.For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict.Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

View Article: PubMed Central - PubMed

Affiliation: Chemical Engineering, Stanford University, Stanford, CA, USA.

ABSTRACT
Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

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Model approaches, scopes, and additions used in the current integration.(A) The computational methods and the organisms represented by previous modeling efforts that are combined in this study. (B) The additional reactions constructed in this study for the purpose of translating T7 ODE reaction rates into host metabolite use. Shown at the top for each category is a schematic of metabolite connections to host metabolism, and under it the full stoichiometric reaction, which may be a formula based on nucleotide or amino acid sequence (the gene designations  taking both decimal and integer values in correspondence with the naming of T7 genes [36], a total of n = 59 included). Assumptions made in formulating the reactions are expanded in Methods and SI, and the metabolite abbreviations used are consistent with the FBA model definition.
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pcbi-1002746-g001: Model approaches, scopes, and additions used in the current integration.(A) The computational methods and the organisms represented by previous modeling efforts that are combined in this study. (B) The additional reactions constructed in this study for the purpose of translating T7 ODE reaction rates into host metabolite use. Shown at the top for each category is a schematic of metabolite connections to host metabolism, and under it the full stoichiometric reaction, which may be a formula based on nucleotide or amino acid sequence (the gene designations taking both decimal and integer values in correspondence with the naming of T7 genes [36], a total of n = 59 included). Assumptions made in formulating the reactions are expanded in Methods and SI, and the metabolite abbreviations used are consistent with the FBA model definition.

Mentions: Critically, E. coli and its phage are sufficiently understood to enable the construction of predictive computational models. Phage T7 replication has been described with structured ordinary differential equations (ODEs), that account for the dynamic production of molecular species that comprise the phage during infection [15] (Figure 1A right). This model was used to computationally predict the infection outcome of phage genome modifications [16], [17]. Separately, host E. coli metabolism has been most comprehensively modeled using Flux Balance Analysis (FBA), which uses linear optimization of an objective function to solve a system of steady-state mass balance ODEs [18]. FBA-based models have expanded to account for essentially all of the known metabolic functionality in E. coli (Figure 1A upper left) [19]–[21]; these models capture growth rates and nutrient exhaustion as well as the impact of genome perturbation and evolutionary outcomes over time [22]–[24].


Determining host metabolic limitations on viral replication via integrated modeling and experimental perturbation.

Birch EW, Ruggero NA, Covert MW - PLoS Comput. Biol. (2012)

Model approaches, scopes, and additions used in the current integration.(A) The computational methods and the organisms represented by previous modeling efforts that are combined in this study. (B) The additional reactions constructed in this study for the purpose of translating T7 ODE reaction rates into host metabolite use. Shown at the top for each category is a schematic of metabolite connections to host metabolism, and under it the full stoichiometric reaction, which may be a formula based on nucleotide or amino acid sequence (the gene designations  taking both decimal and integer values in correspondence with the naming of T7 genes [36], a total of n = 59 included). Assumptions made in formulating the reactions are expanded in Methods and SI, and the metabolite abbreviations used are consistent with the FBA model definition.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475664&req=5

pcbi-1002746-g001: Model approaches, scopes, and additions used in the current integration.(A) The computational methods and the organisms represented by previous modeling efforts that are combined in this study. (B) The additional reactions constructed in this study for the purpose of translating T7 ODE reaction rates into host metabolite use. Shown at the top for each category is a schematic of metabolite connections to host metabolism, and under it the full stoichiometric reaction, which may be a formula based on nucleotide or amino acid sequence (the gene designations taking both decimal and integer values in correspondence with the naming of T7 genes [36], a total of n = 59 included). Assumptions made in formulating the reactions are expanded in Methods and SI, and the metabolite abbreviations used are consistent with the FBA model definition.
Mentions: Critically, E. coli and its phage are sufficiently understood to enable the construction of predictive computational models. Phage T7 replication has been described with structured ordinary differential equations (ODEs), that account for the dynamic production of molecular species that comprise the phage during infection [15] (Figure 1A right). This model was used to computationally predict the infection outcome of phage genome modifications [16], [17]. Separately, host E. coli metabolism has been most comprehensively modeled using Flux Balance Analysis (FBA), which uses linear optimization of an objective function to solve a system of steady-state mass balance ODEs [18]. FBA-based models have expanded to account for essentially all of the known metabolic functionality in E. coli (Figure 1A upper left) [19]–[21]; these models capture growth rates and nutrient exhaustion as well as the impact of genome perturbation and evolutionary outcomes over time [22]–[24].

Bottom Line: The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production.For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict.Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

View Article: PubMed Central - PubMed

Affiliation: Chemical Engineering, Stanford University, Stanford, CA, USA.

ABSTRACT
Viral replication relies on host metabolic machinery and precursors to produce large numbers of progeny - often very rapidly. A fundamental example is the infection of Escherichia coli by bacteriophage T7. The resource draw imposed by viral replication represents a significant and complex perturbation to the extensive and interconnected network of host metabolic pathways. To better understand this system, we have integrated a set of structured ordinary differential equations quantifying T7 replication and an E. coli flux balance analysis metabolic model. Further, we present here an integrated simulation algorithm enforcing mutual constraint by the models across the entire duration of phage replication. This method enables quantitative dynamic prediction of virion production given only specification of host nutritional environment, and predictions compare favorably to experimental measurements of phage replication in multiple environments. The level of detail of our computational predictions facilitates exploration of the dynamic changes in host metabolic fluxes that result from viral resource consumption, as well as analysis of the limiting processes dictating maximum viral progeny production. For example, although it is commonly assumed that viral infection dynamics are predominantly limited by the amount of protein synthesis machinery in the host, our results suggest that in many cases metabolic limitation is at least as strict. Taken together, these results emphasize the importance of considering viral infections in the context of host metabolism.

Show MeSH
Related in: MedlinePlus