A stochastic model for metabolizing systems with computer simulation

Abstract

A stochastic model for a first-order metabolizing system which was studied in the deterministic sense by Branson and others is formulated and a detailed study of the random integral equation arising in the probabilistic model is presented. The equation is used to describe the evolution in time of the amount of metabolite present in the system. Specifically we present a study of the random integral equation of the Volterra type given by

$$M\left( {t; \omega } \right) = M\left( {0; \omega } \right)e^{ - et} + \int_0^t {R\left( {\tau ; \omega } \right) e^{ - e\left( {t - \tau } \right)} d\tau , } t \geqslant 0$$

whereM(t; ω) is an unknown random function giving the amount of metabolite in the system at time t ≥ 0. This equation can be expressed in the general form

$$x\left( {t; \omega } \right) = h\left( {t; \omega } \right) + \int_0^t {k\left( {t, \tau ; \omega } \right) f\left( {\tau , x\left( {\tau ; \omega } \right)} \right) d\tau } t \geqslant 0$$

which is of a type whose theoretical aspects have recently been studied by the present authors using as a basis the techniques of probabilistic functional analysis. Conditions are derived under which there exists a unique random solution to the above equation. The usefulness of the model is illustrated using computer simulation by considering a one-organ model, an organ-heart model, and a multicompartment model.