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Best Pract Res Clin Anaesthesiol. 2011 Dec;25(4):499-510. doi: 10.1016/j.bpa.2011.08.006.

In silico modelling of physiologic systems.

Author information

1
Department of Anesthesiology, University of Virginia Health System, Charlottesville, VA 22908, USA.

Abstract

In silico modelling, in which computer models are developed to model a pharmacologic or physiologic process, is a logical extension of controlled in vitro experimentation. It is the natural result of the explosive increase in computing power available to the research scientist at continually decreasing cost. In silico modelling combines the advantages of both in vivo and in vitro experimentation, without subjecting itself to the ethical considerations and lack of control associated with in vivo experiments. Unlike in vitro experiments, which exist in isolation, in silico models allow the researcher to include a virtually unlimited array of parameters, which render the results more applicable to the organism as a whole. In silico modelling is best known for its extensive use in pharmacokinetic experimentation, the best-known example of which is the development of the three-compartment model. In addition, complex in silico models have been applied to pathophysiological problems to provide information which cannot be obtained practically or ethically by traditional clinical research methods. These experiments have led to the development of significant insights in subject matters ranging from pure physiology to congenital heart surgery, obstetric anaesthesia airway management, mechanical ventilation and cardiopulmonary bypass/ventricular support devices. The utility of these models is based on both the validity of the model framework as well as the corresponding assumptions. In vivo experimentation has validated some, but not all of the in silico strategies employed. We present a review illustrating by example how in silico modelling has been applied to a number of cardio-respiratory problems in states of health and disease, the purpose of which is to give the reader a sense of the complexity and assumptions which underlie this diverse and underappreciated research strategy, as well as an introduction to a research strategy that will likely continue to grow in importance.

PMID:
22099916
DOI:
10.1016/j.bpa.2011.08.006
[Indexed for MEDLINE]

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