  In this paper, we propose a new method to identify biochemical reaction
networks (i.e. both reactions and kinetic parameters) from heterogeneous
datasets. Such datasets can contain (a) data from several replicates of an
experiment performed on a biological system; (b) data measured from a
biochemical network subjected to different experimental conditions, for
example, changes/perturbations in biological inductions, temperature, gene
knock-out, gene over-expression, etc. Simultaneous integration of various
datasets to perform system identification has the potential to avoid
non-identifiability issues typically arising when only single datasets are
used.
