The limits of simulation of the clotting system

Objective: To investigate in how far successful simulation of a thrombin generation (TG) curve gives information about the underlying biochemical reaction mechanism. Results: The large majority of TG curves do not contain more information than can be expressed by four parameters. A limited kinetic m...

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Bibliographic Details
Published in:Journal of thrombosis and haemostasis 2006-06, Vol.4 (6), p.1331-1338
Main Authors: WAGENVOORD, R., HEMKER, P. W., HEMKER, H. C.
Format: Article
Language:English
Subjects:
Blood Coagulation - drug effects
Blood Coagulation - physiology
Computer Simulation
Factor V - metabolism
Factor VIII - pharmacology
Factor VIII - therapeutic use
Factor X - metabolism
Hemophilia A - blood
Hemophilia A - drug therapy
Hemophilia A - metabolism
Humans
mathematical simulation
model discrimination
Models, Biological
Protein C - metabolism
Prothrombin - metabolism
Thrombin - biosynthesis
thrombin generation
Thrombomodulin - metabolism
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Summary:Objective: To investigate in how far successful simulation of a thrombin generation (TG) curve gives information about the underlying biochemical reaction mechanism. Results: The large majority of TG curves do not contain more information than can be expressed by four parameters. A limited kinetic mechanism of six reactions, comprising proteolytic activation of factor (F) X and FII, feedback activation of FV, a cofactor function of FVa and thrombin inactivation by antithrombin can simulate any TG curve in a number of different ways. The information content of a TG curve is thus much smaller than the information required to describe a physiologically realistic reaction scheme of TG. Consequently, much of the input information is irrelevant for the output. FVIII deficiency or activation of protein C can, for example, be simulated by a reaction mechanism in which these factors do not occur. Conclusion: A model that comprises not more than six reactions can simulate the same TG curve in a number of possible ways. The possibilities increase exponentially as the model grows more realistic. Successful simulation of experimental data therefore does not validate the underlying assumptions. A fortiori, simulation that is not checked against experimental data lacks any probative force. Simulation can be of use, however, to detect mistaken hypotheses and for parameter estimation in systems with fewer than five free parameters.
ISSN:1538-7933
1538-7836
1538-7836
DOI:10.1111/j.1538-7836.2006.01967.x