Summary: The purpose of the project is to define common
characteristics of mathematical modelling in biomedicine, and to construct
a cybernetic (feedback) controlled multicompartmental model for simulation
of stochastic and deterministic processes in biosystems. Construction of
specific models and their application were also investigated in different
fields of biomedicine: 1. dynamics of infectious diseases, 2. lung
ventilatory functions and bronchial reactivity, 3. in vivo transfer through
biological barriers and studying migration in connection with population
structure. The investigated models would find application in sanitary
politics of Republic Croatia
Keywords: Key words: deterministic-stochastic; models-..(pharmaco) kinetic, ..factor, ..migrational, ..of chronic and infectious diseases, epidemiological; (de)convolution methods, Monte Carlo simulation, immunological status
Research goals: Mathematical modelling has a specific role in
biomedicine giving theoretical ground for organization of investigation in
this field. At the same time models present a basis for understanding
biomedical causality. Recently applied models in
different fields of biomedicine have been characterized by some aspects of
cybernetic feed- back control. The models serve to estimate the state of
given substructure and to determine the dynamics of interaction.For this
purpose compartmental analysis is usually applied through iterative
computer program. The application of convolution integral is a promising
extension in this type of analysis. The proposed investigations have a
two-fold purpose: A) to give an outline of the scope and common
characteristics of modelling in biomedicine and B) construction of
specific models and their application. Common characteristics of the models
will be analysed through the construction and application of both
deterministic and stochastic models. Deterministic models affects the
conception of stochastic models applied in biomedicine. This fact is
specially relevant to the current epidemiological investigations, results
of which are deduced from multivariate statistical analyses. We believe
that such models will make important contribution to the better
understanding of causality of diseases and to preventing their
propagation. The existing software for simulation, modelling and
statistical analyses will be complemented by the software developed
specifically by members of the staff.