Cortes2019 - Optimality of the spontaneous prophage induction rate.

Model Identifier
BIOMD0000000884
Short description
Optimality of the spontaneous prophage induction rate. Cortes MG1, Krog J2, Balázsi G3. 1 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA. 2 The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA. 3 Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. Electronic address: gabor.balazsi@stonybrook.edu. Abstract Lysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage λ and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts. Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Format
SBML
(L2V4)
Related Publication
-
Optimality of the spontaneous prophage induction rate.
- Cortes MG, Krog J, Balázsi G
- Journal of theoretical biology , 12/ 2019 , Volume 483 , pages: 110005 , PubMed ID: 31525321
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
- Lysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage λ and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts.
Contributors
Submitter of the first revision: Mohammad Umer Sharif Shohan
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Mohammad Umer Sharif Shohan
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Mohammad Umer Sharif Shohan
Metadata information
Curation status
Curated
Tags
Connected external resources
Name | Description | Size | Actions |
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Model files |
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Cortes2019.xml | SBML L2V4 representation of Cortes2019 - Optimality of the spontaneous prophage induction rate. | 36.80 KB | Preview | Download |
Additional files |
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Cortes2019.cps | COPASI version 4.24 (Build 197) representation of Cortes2019 - Optimality of the spontaneous prophage induction rate. | 63.27 KB | Preview | Download |
Cortes2019.sedml | SEDML L1V2 representation of Cortes2019 - Optimality of the spontaneous prophage induction rate. | 3.67 KB | Preview | Download |
- Model originally submitted by : Mohammad Umer Sharif Shohan
- Submitted: Dec 9, 2019 10:56:09 AM
- Last Modified: Dec 9, 2019 10:56:09 AM
Revisions
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
Reactions
Reactions | Rate | Parameters |
---|---|---|
V => ; L | compartment*(gamma*V+alpha*V*L) | gamma = 0.001; alpha = 1.0E-7 |
=> U | compartment*g*U | g = 1.0 |
=> V; U, L | compartment*((1-p)*b*alpha*U*V+b*delta*L) | delta = 1.0E-4; b = 150.0; p = 0.3; alpha = 1.0E-7 |
U => ; V | compartment*(alpha*U*V+phi*U) | phi = 0.999899; alpha = 1.0E-7 |
=> L; U, V | compartment*(r*L+p*alpha*U*V) | r = 0.99; p = 0.3; alpha = 1.0E-7 |
L => | compartment*(delta*L+phi*L) | delta = 1.0E-4; phi = 0.999899 |
Curator's comment:
(added: 09 Dec 2019, 10:55:27, updated: 09 Dec 2019, 10:55:27)
(added: 09 Dec 2019, 10:55:27, updated: 09 Dec 2019, 10:55:27)
The model has been encoded using COPASI 4.24 (Build 197) and the Figure 1 of the publication has been reproduced using ggplot package in R.
The model included initial concentration of L = 1e4 , U = 99e4 and V = 0. The authors used logarithm on both x axis and y axis for the generation of the figure and as there are certain 0 present in the U (uninfected) data, the logarithm produced Nan Value for them and those are missing in the graph. Apart from them the model figure is reproducible to the one published in the paper