The dynamical system theory has been extensively used in the contemporary ecology. Such theory is used to describe biological systems and their main features, to predict their behaviour under certain conditions, to find suitable explanations to biological phenomena, etc. One of the advantages of the mathematical design is that models can depend only on a small number of parameters, still possessing capacity to describe biological systems adequately. There are several types of mathematical models that are used. The most commonly applied type of models is differential and difference equations that describe dynamics of populations of given species. Such models can serve as powerful tools to describe theoretical features of dynamics of populations. However, detailed data that allow estimation of parameters for such models are not always available. Another type of models is developed via implementation of Markov chains that describe stochastic dynamics of populations of given species. Parameters of such models can be estimated based on census data on the number of species that are usually more available. Obviously, such models incorporate stochastic nature of life environments and stochastic nature of regulation processes. Here we show how differential models can be extended to describe both underlying deterministic population dynamics and stochastic transitions between the observed states. To perform our proposal approach we deal with the classic Lotka-Volterra equation for the dynamics of predator-prey system analysis. Assuming stochastic switching for some parameters we analyze this dynamical system as the ergodic Markov chain. Applying statistical approach jointly with MATHEMATICA, R, and MATLAB as the statistical software tools, we estimate the Markov transition probabilities and parameters of the steady-state stationary distribution. Our mathematical design can be used to explore both theoretical features of mathematical models and their compliance with the real data.