  The stochastic dynamics of biochemical reaction networks can be accurately
described by discrete-state Markov processes where each chemical reaction
corresponds to a state transition of the process. Due to the largeness problem
of the state space, analysis techniques based on an exploration of the state
space are often not feasible and the integration of the moments of the
underlying probability distribution has become a very popular alternative. In
this paper the focus is on a comparison of reconstructed distributions from
their moments obtained by two different moment-based analysis methods, the
method of moments (MM) and the method of conditional moments (MCM). We use the
maximum entropy principle to derive a distribution that fits best to a given
sequence of (conditional) moments. For the two gene regulatory networks that we
consider we find that the MCM approach is more suitable to describe multimodal
distributions and that the reconstruction is more accurate if conditional
distributions are considered.
