Mitrophanov2015 - Simulating extended Hockin Blood Coagulation Model under varied pH

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Short description
Mathematical model of the blood coagulation cascade with new kinetic rates to simulate acidosis. Extended Hockin2002 model. Reused Mitrophanov2011 model with new parameters k: 5, 6, 7, 10, 15, 16, 17, 22, 26, 31, 32, 43 and 44.
Related Publication
  • Mechanistic Modeling of the Effects of Acidosis on Thrombin Generation.
  • Mitrophanov AY, Rosendaal FR, Reifman J
  • Anesthesia and analgesia , 8/ 2015 , Volume 121 , Issue 2 , pages: 278-288 , PubMed ID: 25839182
  • From the *DoD Biotechnology High Performance Computing Software Applications Institute (BHSAI); †Telemedicine and Advanced Technology Research Center; U.S. Army Medical Research and Materiel Command, Ft. Detrick, MD; and Departments of ‡Clinical Epidemiology and §Thrombosis and Haemostasis, Leiden University Medical Center, Leiden, The Netherlands.
  • Acidosis, a frequent complication of trauma and complex surgery, results from tissue hypoperfusion and IV resuscitation with acidic fluids. While acidosis is known to inhibit the function of distinct enzymatic reactions, its cumulative effect on the blood coagulation system is not fully understood. Here, we use computational modeling to test the hypothesis that acidosis delays and reduces the amount of thrombin generation in human blood plasma. Moreover, we investigate the sensitivity of different thrombin generation parameters to acidosis, both at the individual and population level.We used a kinetic model to simulate and analyze the generation of thrombin and thrombin-antithrombin complexes (TAT), which were the end points of this study. Large groups of temporal thrombin and TAT trajectories were simulated and used to calculate quantitative parameters, such as clotting time (CT), thrombin peak time, maximum slope of the thrombin curve, thrombin peak height, area under the thrombin trajectory (AUC), and prothrombin time. The resulting samples of parameter values at different pH levels were compared to assess the acidosis-induced effects. To investigate intersubject variability, we parameterized the computational model using the data on clotting factor composition for 472 subjects from the Leiden Thrombophilia Study. To compare acidosis-induced relative parameter changes in individual ("virtual") subjects, we estimated the probabilities of relative change patterns by counting the pattern occurrences in our virtual subjects. Distribution overlaps for thrombin generation parameters at distinct pH levels were quantified using the Bhattacharyya coefficient.Acidosis in the range of pH 6.9 to 7.3 progressively increased CT, thrombin peak time, AUC, and prothrombin time, while decreasing maximum slope of the thrombin curve and thrombin peak height (P < 10). Acidosis delayed the onset and decreased the amount of TAT generation (P < 10). As a measure of intrasubject variability, maximum slope of the thrombin curve and CT displayed the largest and second-largest acidosis-induced relative changes, and AUC displayed the smallest relative changes among all thrombin generation parameters in our virtual subject group (1-sided 95% lower confidence limit on the fraction of subjects displaying the patterns, 0.99). As a measure of intersubject variability, the overlaps between the maximum slope of the thrombin curve distributions at acidotic pH levels with the maximum slope of the thrombin curve distribution at physiological pH level systematically exceeded analogous distribution overlaps for CT, thrombin peak time, and prothrombin time.Acidosis affected all quantitative parameters of thrombin and TAT generation. While maximum slope of the thrombin curve showed the highest sensitivity to acidosis at the individual-subject level, it may be outperformed by CT, thrombin peak time, and prothrombin time as an indicator of acidosis at the subject-group level.
Submitter of the first revision: Matthew Roberts
Submitter of this revision: Krishna Kumar Tiwari
Modellers: Matthew Roberts, Krishna Kumar Tiwari

Metadata information

is (2 statements)
BioModels Database MODEL1806270001
BioModels Database BIOMD0000000951

isDescribedBy (1 statement)
PubMed 25839182

hasTaxon (1 statement)
Taxonomy Homo sapiens

isVersionOf (2 statements)
Gene Ontology blood coagulation
BioModels Database BIOMD0000000362

occursIn (1 statement)
Brenda Tissue Ontology blood plasma

hasProperty (1 statement)
Experimental Factor Ontology acidosis

Curation status


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Model files

Mitrophanov2015.xml SBML L2V4 representation of Mitrophanov2015 - Simulating extended Hockin Blood Coagulation Model under varied pH 177.19 KB Preview | Download

Additional files

Mitrophanov2015.cps model COPASI file. Reused Mitrophanov2011. Added new kinetic parameters k: 5, 6, 7, 10, 15, 16, 17, 22, 26, 31, 32, 43, 44. k_new = f(pH)*k_old. 208.37 KB Preview | Download
fig2c.png simulation figure 2C 20.74 KB Preview | Download
fig2d.png simulation figure 2D 17.24 KB Preview | Download

  • Model originally submitted by : Matthew Roberts
  • Submitted: Jun 27, 2018 10:44:09 AM
  • Last Modified: May 13, 2020 6:44:49 PM
  • Version: 3 public model Download this version
    • Submitted on: May 13, 2020 6:44:49 PM
    • Submitted by: Krishna Kumar Tiwari
    • With comment: Automatically added model identifier BIOMD0000000951
  • Version: 2 public model Download this version
    • Submitted on: Jun 27, 2018 10:44:09 AM
    • Submitted by: Matthew Roberts
    • With comment: Edited model metadata online.

(*) 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.

: Variable used inside SBML models

Reactions Rate Parameters
TF + VIIa => TF_VIIa compartment_1*(k4*TF*VIIa-k3*TF_VIIa) k4 = 2.3E7; k3 = 0.0031
TF_VIIa + X => TF_VIIa_X compartment_1*(k9*TF_VIIa*X-k8*TF_VIIa_X) k9 = 2.5E7; k8 = 1.05
IXa_VIIIa => VIIIa1_L + VIIIa2 + IXa compartment_1*k25*IXa_VIIIa k25 = 0.001
IXa_VIIIa_X => VIIIa1_L + VIIIa2 + X + IXa compartment_1*k25*IXa_VIIIa_X k25 = 0.001
Xa + Va => Xa_Va compartment_1*(k28*Xa*Va-k27*Xa_Va) k27 = 0.2; k28 = 4.0E8
IIa + ATIII => IIa_ATIII compartment_1*k41*IIa*ATIII k41 = 7100.0
IXa + ATIII => IXa_ATIII compartment_1*k40*IXa*ATIII k40 = 490.0
VIIIa => VIIIa1_L + VIIIa2 compartment_1*(k24*VIIIa-k23*VIIIa1_L*VIIIa2) k23 = 22000.0; k24 = 0.006
mIIa + Xa_Va => IIa + Xa_Va compartment_1*k32_0*mIIa*Xa_Va k32_0 = 2.3E8
Curator's comment:
(added: 13 May 2020, 18:44:43, updated: 13 May 2020, 18:44:43)
Reproduced figure 2A of the literature. Model encoded using COPASI.