Advances in molecular biology provide an opportunity to develop detailed models 
of biological processes that can be used to obtain an integrated understanding 
of the system. However, development of useful models from the available 
knowledge of the system and experimental observations still remains a daunting 
task. In this work, a model identification strategy for complex biological 
networks is proposed. The approach includes a state regulator problem (SRP) that 
provides estimates of all the component concentrations and the reaction rates of 
the network using the available measurements. The full set of the estimates is 
utilised for model parameter identification for the network of known topology. 
An a priori model complexity test that indicates the feasibility of performance 
of the proposed algorithm is developed. Fisher information matrix (FIM) theory 
is used to address model identifiability issues. Two signalling pathway case 
studies, the caspase function in apoptosis and the MAP kinase cascade system, 
are considered. The MAP kinase cascade, with measurements restricted to protein 
complex concentrations, fails the a priori test and the SRP estimates are poor 
as expected. The apoptosis network structure used in this work has moderate 
complexity and is suitable for application of the proposed tools. Using a 
measurement set of seven protein concentrations, accurate estimates for all 
unknowns are obtained. Furthermore, the effects of measurement sampling 
frequency and quality of information in the measurement set on the performance 
of the identified model are described.