Minimizing the number of optimizations for efficient community dynamic flux balance analysis.
Minimizing the number of optimizations for efficient community dynamic flux balance analysis.
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DOI：
10.1371/journal.pcbi.1007786
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发表时间：
2020-09
影响因子：
4.3
通讯作者：
Chia N
中科院分区：
文献类型：
--
作者：
Brunner JD;Chia N
动态通量平衡分析采用准稳态假设，利用众所周知的通量平衡分析技术，计算生物体在动态模拟的每个时间步的代谢活动。对于微生物群落，这种计算特别昂贵，涉及在每个时间步为群落的每个成员解决线性约束优化问题。然而，这是不必要和低效的，因为可以使用先前的解决方案来通知未来的时间步长。在这里，我们表明，可以为群落中的每个微生物选择一个内部通量空间的基，并且这个基可以用来通过在大多数时间步长求解相对便宜的线性方程组来模拟正演。只要由此产生的代谢活动保持在优化问题的约束范围内(即，线性方程组的解对于线性规划来说仍然是可行的)，我们就可以使用这个解。当解变得不可行时，它首先成为优化问题的可行但退化的解，我们可以解决不同但相关的优化问题，以选择合适的基础来继续进行正向模拟。我们通过在一个四物种群落上与目前使用的方法进行比较，证明了该方法的有效性和稳健性，并表明我们的方法需要求解的优化次数至少减少了91%。为了重现性，我们使用了Python制作了该方法的原型。源代码可在https://github.com/jdbrunner/surfin_fba.上获得动态通量平衡分析(FBA)领域的标准方法计算成本高得令人望而却步，因为它需要在每个时间步长求解一个线性优化问题。我们开发了一种新的方法来产生这个动力系统的解，这大大减少了必须解决的优化问题的数量。我们从数学上证明了我们可以一次解决优化问题，并在一定时间间隔内将系统模拟为常微分方程组(ODE)，并且该常微分方程组的解提供了优化问题的解。最终，系统达到一个容易检查的条件，这意味着必须解决另一个优化问题。我们将我们的方法与通常使用的动态FBA方法进行比较，以验证它在提供等价解的同时需要更少的线性规划解。
Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism’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% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba. The standard methods in the field for dynamic flux balance analysis (FBA) carry a prohibitively high computational cost because it requires solving a linear optimization problem at each time-step. We have developed a novel method for producing solutions to this dynamical system which greatly reduces the number of optimization problems that must be solved. We prove mathematically that we can solve the optimization problem once and simulate the system forward as an ordinary differential equation (ODE) for some time interval, and solutions to this ODE provide solutions to the optimization problem. Eventually, the system reaches an easily check-able condition which implies that another optimization problem must be solved. We compare our method against typically used methods for dynamic FBA to validate that it provides equivalent solutions while requiring fewer linear-program solutions.
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