  Key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications,
computational models of reaction networks are used extensively to elucidate
their non-linear dynamics. Model dynamics are crucially dependent on parameter
values which are often estimated from observations. Over past decade, the
interest in parameter and state estimation in models of (bio-)chemical reaction
networks (BRNs) grew considerably. Statistical inference problems are also
encountered in many other tasks including model calibration, discrimination,
identifiability and checking as well as optimum experiment design, sensitivity
analysis, bifurcation analysis and other. The aim of this review paper is to
explore developments of past decade to understand what BRN models are commonly
used in literature, and for what inference tasks and inference methods. Initial
collection of about 700 publications excluding books in computational biology
and chemistry were screened to select over 260 research papers and 20 graduate
theses concerning estimation problems in BRNs. The paper selection was
performed as text mining using scripts to automate search for relevant keywords
and terms. The outcome are tables revealing the level of interest in different
inference tasks and methods for given models in literature as well as recent
trends. In addition, a brief survey of general estimation strategies is
provided to facilitate understanding of estimation methods which are used for
BRNs. Our findings indicate that many combinations of models, tasks and methods
are still relatively sparse representing new research opportunities to explore
those that have not been considered - perhaps for a good reason. The paper
concludes by discussing future research directions including research problems
which cannot be directly deduced from presented tables.
