Gulbudak2019.1 - Heterogeneous viral strategies promote coexistence in virus-microbe systems (Lytic)

  public model
Model Identifier
BIOMD0000000845
Short description 
This is a mathematical model describing describing the population dynamics of microbes infected by lytic viruses.
Format
SBML (L2V4)
Related Publication
  • Heterogeneous viral strategies promote coexistence in virus-microbe systems.
  • Gulbudak H, Weitz JS
  • Journal of theoretical biology , 2/ 2019 , Volume 462 , pages: 65-84 , PubMed ID: 30389532
  • Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA. Electronic address: hayriye.gulbudak@louisiana.edu.
  • Viral infections of microbial cells often culminate in lysis and the release of new virus particles. However, viruses of microbes can also initiate chronic infections, in which new viruses particles are released via budding and without cell lysis. In chronic infections, viral genomes may also be passed on from mother to daughter cells during division. The consequences of chronic infections for the population dynamics of viruses and microbes remains under-explored. In this paper we present a model of chronic infections as well as a model of interactions between lytic and chronic viruses competing for the same microbial population. In the chronic only model, we identify conditions underlying complex bifurcations such as saddle-node, backward and Hopfbifurcations, leading to parameter regions with multiple attractors and/or oscillatory behavior. We then utilize invasion analysis to examine the coupled nonlinear system of microbes, lytic viruses, and chronic viruses. In so doing we find unexpected results, including a regime in which the chronic virus requires the lytic virus for survival, invasion, and persistence. In this regime, lytic viruses decrease total cell densities, so that a subpopulation of chronically infected cells experience decreased niche competition. As such, even when chronically infected cells have a growth disadvantage, lytic viruses can, paradoxically, enable the proliferation of both chronically infected cells and chronic viruses. We discuss the implications of our results for understanding the ecology and long-term evolution of heterogeneous viral strategies.
Contributors
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Rahuman Sheriff
Modellers: Rahuman Sheriff, Johannes Meyer

Metadata information

is (2 statements)
BioModels Database BIOMD0000000845
BioModels Database MODEL1911100003

isDescribedBy (1 statement)
PubMed 30389532

hasProperty (2 statements)
Mathematical Modelling Ontology Ordinary differential equation model
Gene Ontology transmission of virus

Curation status
Curated
Modelling approach(es)
ordinary differential equation model
Tags

Connected external resources

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Model files
Gulbudak2019.1.Lytic.xml SBML L2V4 Representation of Gulbudak2019.1 - Heterogeneous viral strategies promote coexistence in virus-microbe systems (Lytic) 29.85 KB Preview | Download

Additional files
Gulbudak2019.1.Lytic.cps COPASI file of Gulbudak2019.1 - Heterogeneous viral strategies promote coexistence in virus-microbe systems (Lytic) 59.70 KB Preview | Download
Gulbudak2019.1.Lytic.sedml SED-ML file of Gulbudak2019.1 - Heterogeneous viral strategies promote coexistence in virus-microbe systems (Lytic) 2.69 KB Preview | Download

  • Model originally submitted by : Johannes Meyer
  • Submitted: Nov 10, 2019 8:12:36 PM
  • Last Modified: Oct 5, 2021 7:57:48 PM
Revisions
  • Version: 5 public model Download this version
    • Submitted on: Oct 5, 2021 7:57:48 PM
    • Submitted by: Rahuman Sheriff
    • With comment: Automatically added model identifier BIOMD0000000845
  • Version: 2 public model Download this version
    • Submitted on: Nov 10, 2019 8:12:36 PM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000845

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
I

infected cell 		0.0 item
S

C14187 		8.3E8 item
V L

C14368 		3.32E7 item
Reactions
Reactions Rate Parameters
I => compartment*eta*I eta = 1.5
S => compartment*d*S d = 0.0416666666666667
=> S compartment*r*S*(1-N/K) r = 0.339; N = 8.3E8; K = 8.947E8
S + V_L => I compartment*phi*S*V_L phi = 1.73925271309261E-10
=> V_L; I compartment*beta*eta*I beta = 20.0; eta = 1.5
V_L => compartment*mu*V_L mu = 0.0866
Curator's comment:
(added: 10 Nov 2019, 20:12:27, updated: 10 Nov 2019, 20:12:27)
Reproduced plot of Figure 1 C in the original publication. Model simulated using COPASI 4.24 (Build 197) and plots produced using Wolfram Mathematica 11.3.