  Nonlinear dynamic models are widely used for characterizing functional forms
of processes that govern complex biological pathway systems. Over the past
decade, validation and further development of these models became possible due
to data collected via high-throughput experiments using methods from molecular
biology. While these data are very beneficial, they are typically incomplete
and noisy, so that inferring parameter values for complex dynamic models is
associated with serious computational challenges. Fortunately, many biological
systems have embedded linear mathematical features, which may be exploited,
thereby improving fits and leading to better convergence of optimization
algorithms.
  In this paper, we explore options of inference for dynamic models using a
novel method of {\it separable nonlinear least-squares optimization}, and
compare its performance to the traditional nonlinear least-squares method. The
numerical results from extensive simulations suggest that the proposed approach
is at least as accurate as the traditional nonlinear least-squares, but usually
superior, while also enjoying a substantial reduction in computational time.
