  Mathematical modeling with Ordinary Differential Equations (ODEs) has proven
to be extremely successful in a variety of fields, including biology. However,
these models are completely deterministic given a certain set of initial
conditions. We convert mathematical ODE models of three benchmark biological
systems to Dynamic Bayesian Networks (DBNs). The DBN model can handle model
uncertainty and data uncertainty in a principled manner. They can be used for
temporal data mining for noisy and missing variables. We apply Particle
Filtering algorithm to infer the model variables by re-estimating the models
parameters of various biological ODE models. The model parameters are
automatically re-estimated using temporal evidence in the form of data streams.
The results show that DBNs are capable of inferring the model variables of the
ODE model with high accuracy in situations where data is missing, incomplete,
sparse and irregular and true values of model parameters are not known.
