Dynamic flux balance analysis uses a quasi-steady state assumption to calculate URLanism's metabolic activity at each time-step of a dynamic simulation, using the well-know technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that 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 , instead of the full optimization problem. Using our method, we can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution remains feasible ). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We show using an eight species community that this is an efficient and robust method for computing dynamic flux balance analysis simulations, and so is capable of simulating communities URLanisms. We demonstrate that the method gives an approximately 85 \\%DIF < speed-up URLanism over the standard and widely used method. Our method has been implemented in the Python language and source code is available at URL and in the Python Package Index (PyPI) as surfinFBA.abstract %DIF > fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at URL
Dynamic flux balance analysis uses a quasi-steady state assumption to calculate URLanism's metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that 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 . We can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program ). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91 \\%DIF < speed-up URLanism over the standard and widely used method. Our method has been implemented in the Python language and source code is available at URL and in the Python Package Index (PyPI) as surfinFBA.abstract %DIF > fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at URL
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show using an eight species community that this is an efficient and robust method for computing dynamic flux balance analysis simulations, and so is capable of simulating communities URLanisms. We demonstrate that the method gives an approximately 85
demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91