Dynamic model identification of Lactococcus lactis metabolism time-series
Systems Biology is an emerging field within bioscience, that uses holism,a global and integrative perspective rather than reductionism to explain the biological system's behavior. This approach is particularly useful to quantitatively characterize and predict the systems dynamic.In our application multivariate time-series of Lactococcus lactis metabolite concentrations are measured in perturbation experiments. Prior knowledge about the metabolic network topology is represented in the form of parametrized nonlinear ordinary differential equations. Our goal is to identify appropriate models and parameters to the network.
In this talk two different approaches will be introduced for parameter estimation: Bayesian filtering and unified modeling of Glucose uptake. We concluded that Bayesian approach offers powerful tools for identifying parameters of such networks if the identifiability is granted. Taking several different experiments in account may results model parameters that can describe the systems behavior in various conditions.