  In this work we formalize a method to compute the degree of importance of
biological agents that participates on the dynamics of a biological phenomenon
build upon a complex network. We call this new procedure by theoretical
knock-out (KO). To devise this method, we make two approaches: algebraically
and algorithmically. In both cases we compute a vector on an asymptotic state,
called flux vector. The flux is given by a random walk on a directed graph that
represents a biological phenomenon. This vector gives us the information about
the relative flux of walkers on a vertex which represents a biological agent.
With two vector of this kind, we can calculate the relative mean error between
them by averaging over its coefficients. This quantity allows us to assess the
degree of importance of each vertex of a complex network that evolves in time
and has experimental background. We find out that this procedure can be applied
in any sort of biological phenomena in which we can know the role and
interrelationships of its agents. These results also provide experimental
biologists to predict the order of importance of biological agents on a mounted
complex network.
