In vivo Intracellular Metabolite Dynamics Estimation by Sequential Monte Carlo Filter

The quantitative comprehension of a metabolic system in its dynamic state is a prerequisite for purposed strain improvement and enzymatic regulation. It is therefore crucial to accurately obtain the extracellular and intracellular metabolite concentrations in vivo in the time scale faster than typical metabolite turn-over rate. Though intracellular metabolite dynamics are addressable by latest rapid sampling technology, the measurements are not satisfactory due to the low percentage of intracellular volume to the total sample volume and often the low concentration levels of most intracellular metabolites. When the examined system is observable, a possible solution to this problem is by means of available statistical estimation approach. Hence, in this paper, the sequential Monte Carlo filter is applied to estimate the intracellular metabolite concentrations with the knowledge of extracellular metabolite concentrations. The application of this algorithm in a synthetic system with simulated data illustrates the applicability of this approach. All the intracellular metabolite concentrations are accurately estimated and the extracellular states are reconstructed from their noisy measurements. The dynamic flux distributions are also obtained and their underlying biological meanings are described