BioModels Database logo

BioModels Database

spacer

BIOMD0000000583 - Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection

 

 |   |   |  Send feedback
Reference Publication
Publication ID: 26230099
Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J.
Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.
PLoS ONE 2015; 10(7): e0134849
The Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory (www.nimml.org), Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.  [more]
Model
Original Model: Systems modeling of intera...
Submitter: Andrew Leber
Submission ID: MODEL1507200000
Submission Date: 20 Jul 2015 18:34:49 UTC
Last Modification Date: 28 Aug 2015 13:18:21 UTC
Creation Date: 08 Apr 2014 14:02:57 UTC
Encoders:  Vijayalakshmi Chelliah
   Andrew Leber
set #1
bqbiol:hasProperty Human Disease Ontology Clostridium difficile colitis
set #2
bqbiol:isVersionOf Gene Ontology obsolete host-pathogen interaction
Gene Ontology immune response
set #3
bqbiol:hasTaxon Taxonomy Mus musculus
Taxonomy Peptoclostridium difficile
Notes
Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection

This model is described in the article:

Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J.
PLoS ONE 2015; 10(7): e0134849

Abstract:

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Model
Publication ID: 26230099 Submission Date: 20 Jul 2015 18:34:49 UTC Last Modification Date: 28 Aug 2015 13:18:21 UTC Creation Date: 08 Apr 2014 14:02:57 UTC
Mathematical expressions
Reactions
Treg Degradation eDC Degradation Th17 Degradation Th1 Degradation
N Degradation E Damage eDC Migration eDC Production
Cdiff Death N Activation/Migration Cdiff Growth Treg Migration
Th1 Migration Th17 Plasticity Th17 Migration E Inflame
E_i Damage M Activation M Death Commensal Regrowth
E Heal tDC Production tDC Migration tDC Degradation
Th17 Differentiation Th1 Differentiation Treg Differentiation Commensal Harmful Death
Commensal Death E_i Natural Death    
Physical entities
Compartments Species
Lumen Cdiff Commensal_Beneficial Commensal_Dead
tDC_LP tDC_MLN Commensal_Harmful
N_Lum    
Epithelium E E_d iDC_E
E_i M_LP  
LP eDC_LP M0 N_LP
Th17_LP Th1_LP iTreg_LP
     
MLN eDC_MLN iTreg_MLN nT
Th17_MLN Th1_MLN  
Reactions (30)
 
 Treg Degradation [iTreg_LP] → ;   {Cdiff} , {iTreg_LP} , {iTreg_LP}
 
 eDC Degradation [eDC_MLN] → ;   {iTreg_MLN} , {eDC_MLN} , {eDC_MLN}
 
 Th17 Degradation [Th17_LP] → ;   {iTreg_LP} , {Th17_LP} , {Th17_LP}
 
 Th1 Degradation [Th1_LP] → ;   {iTreg_LP} , {Commensal_Dead} , {Th1_LP} , {Th1_LP}
 
 N Degradation [N_Lum] → ;   {Commensal_Beneficial} , {N_Lum} , {Commensal_Beneficial} , {N_Lum} , {Commensal_Beneficial}
 
 E Damage [E] → [E_d];   {N_Lum} , {Th17_LP} , {M_LP} , {E} , {N_Lum} , {Th17_LP} , {M_LP} , {E} , {N_Lum} , {Th17_LP} , {M_LP}
 
 eDC Migration [eDC_LP] → [eDC_MLN];   {eDC_LP} , {eDC_LP}
 
 eDC Production [iDC_E] + [Cdiff] → [eDC_LP];   {Commensal_Dead} , {Commensal_Beneficial} , {Cdiff} , {Cdiff}
 
 Cdiff Death [Cdiff] → ;   {M_LP} , {N_Lum} , {Commensal_Harmful} , {Cdiff} , {M_LP} , {N_Lum} , {Commensal_Harmful} , {Cdiff} , {M_LP} , {N_Lum} , {Commensal_Harmful}
 
 N Activation/Migration [N_LP] → [N_Lum];   {Cdiff} , {E_d} , {Th17_LP} , {iTreg_LP} , {N_LP} , {Cdiff} , {E_d} , {Th17_LP} , {iTreg_LP} , {N_LP} , {Cdiff} , {E_d} , {Th17_LP} , {iTreg_LP}
 
 Cdiff Growth [Cdiff] → 2.0 × [Cdiff];   {Commensal_Harmful} , {Commensal_Beneficial} , {Cdiff} , {Commensal_Harmful} , {Cdiff} , {Commensal_Harmful}
 
 Treg Migration [iTreg_MLN] → [iTreg_LP];   {E_i} , {iTreg_MLN} , {iTreg_MLN}
 
 Th1 Migration [Th1_MLN] → [Th1_LP];   {E_i} , {Th1_MLN} , {Th1_MLN}
 
 Th17 Plasticity [Th17_LP] ↔ [iTreg_LP];   {Cdiff} , {Th17_LP} , {Cdiff} , {iTreg_LP} , {Th17_LP} , {Cdiff} , {iTreg_LP}
 
 Th17 Migration [Th17_MLN] → [Th17_LP];   {E_i} , {Th17_MLN} , {Th17_MLN}
 
 E Inflame [E] → [E_i];   {Cdiff} , {E} , {Cdiff} , {E} , {Cdiff}
 
 E_i Damage [E_i] → [E_d];   {N_Lum} , {Th17_LP} , {M_LP} , {E_i} , {N_Lum} , {Th17_LP} , {M_LP} , {E_i} , {N_Lum} , {Th17_LP} , {M_LP}
 
 M Activation [M0] → [M_LP];   {Th17_LP} , {Cdiff} , {iTreg_LP} , {M0} , {Th17_LP} , {Cdiff} , {iTreg_LP} , {M0} , {Th17_LP} , {Cdiff} , {iTreg_LP}
 
 M Death [M_LP] → ;   {iTreg_LP} , {M_LP} , {M_LP}
 
 Commensal Regrowth [Commensal_Beneficial] ↔ [Commensal_Dead];   {N_Lum} , {E_i} , {Commensal_Beneficial} , {N_Lum} , {E_i} , {Commensal_Dead} , {Commensal_Beneficial} , {N_Lum} , {E_i} , {Commensal_Dead}
 
 E Heal [E_d] → [E];   {E_d} , {E_d}
 
 tDC Production [iDC_E] + [Cdiff] → [tDC_LP];   {Commensal_Beneficial} , {Commensal_Dead} , {E} , {E_i} , {Cdiff} , {Commensal_Beneficial} , {Commensal_Dead} , {E} , {E_i} , {Cdiff} , {Commensal_Beneficial} , {Commensal_Dead} , {E} , {E_i}
 
 tDC Migration [tDC_LP] → [tDC_MLN];   {tDC_LP} , {tDC_LP}
 
 tDC Degradation [tDC_MLN] → ;   {iTreg_MLN} , {tDC_MLN} , {tDC_MLN}
 
 Th17 Differentiation [eDC_MLN] → [Th17_MLN];   {eDC_MLN} , {eDC_MLN}
 
 Th1 Differentiation [eDC_MLN] → [Th1_MLN];   {Commensal_Dead} , {Commensal_Beneficial} , {E} , {eDC_MLN} , {Commensal_Dead} , {Commensal_Beneficial} , {E} , {eDC_MLN} , {Commensal_Dead} , {Commensal_Beneficial} , {E}
 
 Treg Differentiation [tDC_MLN] → [iTreg_MLN];   {tDC_MLN} , {tDC_MLN}
 
 Commensal Harmful Death [Commensal_Harmful] → ;   {N_LP} , {E_i} , {Commensal_Harmful} , {N_LP} , {E_i} , {Commensal_Harmful} , {N_LP} , {E_i}
 
 Commensal Death [Commensal_Dead] → ;   {Commensal_Dead} , {Commensal_Dead}
 
 E_i Natural Death [E_i] → [E_d];   {E_i} , {E_i}
 
Functions (11)
 
 KSA lambda(K, S, A, K*S*A)
 
 Rate Law for E damage lambda(v, S, k1, a1, k2, a2, k3, a3, v*S*(k1*a1+k2*a2+k3*a3))
 
 Rate Law for Th17plas lambda(k1, s, k2, m2, p, k1*s-k2*m2*p)
 
 Rate Law for Effector DC Production_1 lambda(k, S, k*S)
 
 Rate Law for eDC lambda(K, S, M1, M2, k2, M3, k1, K*S*M1/(k1*M2+k2*M3))
 
 Rate Law for CD_Lumen death lambda(K, S, A1, m2, A2, m3, A3, K*S*((A1+m2*A2)-m3*A3))
 
 Rate Law for tDC Production lambda(K, S, k1, M1, M2, k2, M3, M4, K*S*(k1*M1/M2+k2*M3/(M4+100)))
 
 Rate Law for M Activation lambda(K, S, e1, A1, A2, e2, I1, K*S*((e1*A1+A2)-e2*I1))
 
 Rate Law for N Activation/Migration lambda(v, S, m, k1, A1, k2, A2, k3, I1, v*S*(m*(k1*A1+k2*A2)-k3*I1))
 
 Rate Law for Commensal Regrowth lambda(k1, S, m1, m2, k2, P, k1*S*m1*m2-k2*P)
 
 Rate Law for Commensal Harmful Death_1 lambda(K, S, m1, A1, m2, A2, K*S*(m1*A1+m2*A2))
 
 Lumen Spatial dimensions: 3.0  Compartment size: 1.0
 
 Cdiff
Compartment: Lumen
Initial concentration: 484.0
 
 Commensal_Beneficial
Compartment: Lumen
Initial concentration: 1.0
 
 Commensal_Dead
Compartment: Lumen
Initial concentration: 5.0E10
 
 tDC_LP
Compartment: Lumen
Initial concentration: 0.0
 
 tDC_MLN
Compartment: Lumen
Initial concentration: 0.0
 
 Commensal_Harmful
Compartment: Lumen
Initial concentration: 1.5E10
 
 N_Lum
Compartment: Lumen
Initial concentration: 0.0
 
 Epithelium Spatial dimensions: 3.0  Compartment size: 4.0
 
 E
Compartment: Epithelium
Initial concentration: 1052500.0
 
 E_d
Compartment: Epithelium
Initial concentration: 0.0
 
 iDC_E
Compartment: Epithelium
Initial concentration: 500000.0
Constant
 
 E_i
Compartment: Epithelium
Initial concentration: 0.0
 
 M_LP
Compartment: Epithelium
Initial concentration: 3250.0
 
 LP Spatial dimensions: 3.0  Compartment size: 0.07
 
 eDC_LP
Compartment: LP
Initial concentration: 0.0
 
 M0
Compartment: LP
Initial concentration: 1714285.71428571
Constant
 
 N_LP
Compartment: LP
Initial concentration: 714285.714285714
Constant
 
 Th17_LP
Compartment: LP
Initial concentration: 0.0
 
 Th1_LP
Compartment: LP
Initial concentration: 0.0
 
 iTreg_LP
Compartment: LP
Initial concentration: 0.0
 
 MLN Spatial dimensions: 3.0  Compartment size: 1.0
 
 eDC_MLN
Compartment: MLN
Initial concentration: 0.0
 
 iTreg_MLN
Compartment: MLN
Initial concentration: 0.0
 
 nT
Compartment: MLN
Initial concentration: 1.2E7
Constant
 
 Th17_MLN
Compartment: MLN
Initial concentration: 0.0
 
 Th1_MLN
Compartment: MLN
Initial concentration: 0.0
 
Treg Degradation (1)
 
   k1
Value: 0.5069887
Constant
 
eDC Degradation (1)
 
   k1
Value: 1.72495199303666E-5
Constant
 
Th17 Degradation (1)
 
   k1
Value: 2.39665140586358
Constant
 
Th1 Degradation (1)
 
   k1
Value: 0.99505694359
Constant
 
N Degradation (1)
 
   K
Value: 2.35932924820229E-7
Constant
 
E Damage (4)
 
   v
Value: 1.59920673150176E-6
Constant
 
   k1
Value: 1.1E-5
Constant
 
   k2
Value: 2.3381277077344E-6
Constant
 
   k3
Value: 62.5911647602982
Constant
 
eDC Migration (1)
 
   k1
Value: 10.5
Constant
 
eDC Production (1)
 
   k
Value: 0.55
Constant
 
Cdiff Death (3)
 
   K
Value: 6.27092296294148E-10
Constant
 
   m2
Value: 594.896546415159
Constant
 
   m3
Value: 0.102702503781515
Constant
 
N Activation/Migration (4)
 
   v
Value: 5.29827880572231E-5
Constant
 
   k1
Value: 0.120935308788409
Constant
 
   k2
Value: 0.171190728888258
Constant
 
   k3
Value: 0.129717307334483
Constant
 
Cdiff Growth (1)
 
   K
Value: 5.0E-11
Constant
 
Treg Migration (1)
 
   k1
Value: 5.5
Constant
 
Th1 Migration (1)
 
   k1
Value: 1.459
Constant
 
Th17 Plasticity (2)
 
   k1
Value: 1.27393226093773
Constant
 
   k2
Value: 0.0020401460213434
Constant
 
Th17 Migration (1)
 
   k1
Value: 2.50454427171444
Constant
 
E Inflame (1)
 
   K
Value: 1.71079818745428E-4
Constant
 
E_i Damage (4)
 
   v
Value: 0.065
Constant
 
   k1
Value: 0.006
Constant
 
   k2
Value: 0.0106698310809694
Constant
 
   k3
Value: 1.16013457036959E-6
Constant
 
M Activation (3)
 
   K
Value: 4.5E-5
Constant
 
   e1
Value: 2.0
Constant
 
   e2
Value: 0.092308585205372
Constant
 
M Death (1)
 
   k1
Value: 20.0
Constant
 
Commensal Regrowth (2)
 
   k1
Value: 4.5E-10
Constant
 
   k2
Value: 0.156287382551622
Constant
 
E Heal (1)
 
   k1
Value: 4000.0
Constant
 
tDC Production (3)
 
   K
Value: 2.0E-4
Constant
 
   k1
Value: 559.297141527983
Constant
 
   k2
Value: 26.8747332769592
Constant
 
tDC Migration (1)
 
   k1
Value: 3.65
Constant
 
tDC Degradation (1)
 
   k
Value: 9.5E-4
Constant
 
Th17 Differentiation (1)
 
   k1
Value: 2255.80469507059
Constant
 
Th1 Differentiation (3)
 
   K
Value: 0.0430096
Constant
 
   k2
Value: 9.65568121975566E-5
Constant
 
   k1
Value: 0.0648415756801505
Constant
 
Treg Differentiation (1)
 
   k1
Value: 53.9130568911728
Constant
 
Commensal Harmful Death (3)
 
   K
Value: 2.33225E-5
Constant
 
   A1
Value: 0.00478
Constant
 
   A2
Value: 0.18
Constant
 
Commensal Death (1)
 
   k1
Value: 0.0933277452272273
Constant
 
E_i Natural Death (1)
 
   k1
Value: 2.5
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000583

Curator's comment: (updated: 21 Aug 2015 18:29:57 GMT)

Figure 3 of the reference publication has been reproduced here. The difference in the y-axis measurement between the plots generated by the model and that of the paper is because the model is designed to be a scale-able representation of a 50 mg section of tissue and in the paper it is the measured values of biological quantities within the in vivo mode.

The model was simulated using SBMLSimulator.

spacer
spacer