How to use DFBSIM

Using DFBSIM can be divided in three tasks: Writing the dFBA model, setting up the simulation and running the simulation.
An overview of how to construct a dFBA model and setup a simulation is given here. A technical description of how to use DFBSIM is provided through an annoted simulation of E. coli bacterium growing on glucose and xylose medium, considered in Höffner et al. 2012 [1], in the example section. This example is distributed with DFBSIM. Additional examples will be added as they are completed. In the example provided it is assume that the structure of the dFBA model is based on the Dynamic Multispecies Metabolic Modeling (DMMM) framework [3]. An alternative model structure can easily be implemented by changing the dynamic equations for the biomass and external metabolites.

Write a dFBA model

Dynamic flux balance analysis is an extension of FBA that has the advantage that it enables analysis of interactions between the metabolism and the environment. The structure of the dFBA model that is assumed here has the following elements:

  • A flux balance model of each of the microorganism.
  • A list of external metabolite.
  • Description of the interaction between FBA models through uptake and production of external metabolites.
  • The dynamic equations that are integrated by DFBSIM.

where N is the number of biological species and M is the number of extracellular chemical species. The variable xj is the biomass concentration of the jth metabolism, si is the extracellular concentration of the ith chemical species, Fini=1MFi(t) with Fi is the feed rate of chemical species i, Di=1MFi /V is the dilution rate, vij is the uptake or production rate of chemical species i due to species j and V is the volume of the bio-reactor. If the function vij describes the production rate of the ith external metabolite by the jth metabolism, then its value can be determined by secondary optimization for the production rate.

Setup the simulation

The simulation parameters are determined by the batch condition of the experiments. A minimal set of parameter consists of

  • The (maximal) batch time.
  • The initial conditions for the biomass, substrates and total volume.
  • Definition of the external control inputs such as feed rate and composition and other environmental changes that are should be implemented during the batch.
  • Optional definition of minimal growth conditions to determine when the simulation should stop prior to reaching the final batch time.