New understanding of steady states in biological systems

Within the existing theory of dynamic systems, steady states of any dynamic system are described as $dx/dt=0$, for the state vector of the system $x=x(t)=(x_{1}, x_{2},{\dots}, x_{m})^{T}$ in the $m$-dimensional state phase space. From the stochastic point of view, the preservation of the statistical distribution function $f(x)$ or its properties (statistical mathematical expectation $<x>$, statistical variance $Dx^*$, the spectral density of the signal, autocorrelation, etc.) within certain (statistical) assumptions is sufficient for the system to remain unchanged. However, in the living nature any parameters $x_{i}(t)$ of the entire state vector $x(t)$ of the biosystem show continuous, chaotic motion in the state phase space. There are no statistical stability in $x_{i}(t)$ samples, which is called the Eskov–Zinchenko effect. We introduce the concept of a pseudoattractor similar to the Heisenberg uncertainty principle and define two types of uncertainties: 1${}^{st}$ and 2${}^{nd}$. As a result, we inverse the concepts: what in physics (and biomedicine) is now considered to be a steady state is kinematics (the motion of $x(t)$ in the state phase space), and the motion of biosystems is (for them) a steady state.