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In dynamic FBA (dFBA) [doi: 10.1016/S0006-3495(02)73903-9] we have an issue when going from a step to the next regarding the non uniqueness of the FBA solutions. In the DFBAlab approach they address this challenge using lexicographic LP [doi: 10.1186/s12859-014-0409-8].
In dingo we could give it a shot for a dFBA module where the non-uniqueness challenge would be addressed by sampling at each cycle and select/pick the sample that is closest to the flux distribution of the previous one.
The boundaries and the biomass are then updated as in all the dFBA implementations.
For the first cycle, we could get the mean of the flux samples.
You may also have a look at the COMETS papers for applications and alternative implementations of the dFBA
" a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. "
To my understanding it deserves to investigate dFBA implementations and check whether sampling could contribute.
In dynamic FBA (dFBA) [doi: 10.1016/S0006-3495(02)73903-9] we have an issue when going from a step to the next regarding the non uniqueness of the FBA solutions. In the DFBAlab approach they address this challenge using lexicographic LP [doi: 10.1186/s12859-014-0409-8].
In
dingo
we could give it a shot for a dFBA module where the non-uniqueness challenge would be addressed by sampling at each cycle and select/pick the sample that is closest to the flux distribution of the previous one.The boundaries and the biomass are then updated as in all the dFBA implementations.
For the first cycle, we could get the mean of the flux samples.
You may also have a look at the COMETS papers for applications and alternative implementations of the dFBA
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