在识别接近Hopf分岔的系统时,消除初始值参数。

G Cedersund
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引用次数: 0

摘要

在对生物系统进行系统识别时,最大的问题之一是很少有可能测量超过变量总数的一小部分。如果是这种情况,则必须从模拟开始的初始状态以及速率表达式中出现的动力学参数进行估计。这通常是通过引入额外的参数来完成的,描述初始状态,消除它们的一种方法是从稳定状态开始。我们将这种方法推广到所有从中心流形开始,接近Hopf分岔的系统。已有的生化系统已经收集了这样的数据,例如酵母中的糖酵解。对于其他参数估计的每一步,在优化子问题中求解初始值参数。对于从平稳振荡开始的系统,子问题以一种直接的方式求解,不需要微分方程的积分,也不需要局部极小值问题。这是可能的,因为中心流形和范式约简的结合,这揭示了Hopf分岔的特殊结构。该方法的优越性在Brusselator上得到了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Elimination of the initial value parameters when identifying a system close to a Hopf bifurcation.

One of the biggest problems when performing system identification of biological systems is that it is seldom possible to measure more than a small fraction of the total number of variables. If that is the case, the initial state, from where the simulation should start, has to be estimated along with the kinetic parameters appearing in the rate expressions. This is often done by introducing extra parameters, describing the initial state, and one way to eliminate them is by starting in a steady state. We report a generalisation of this approach to all systems starting on the centre manifold, close to a Hopf bifurcation. There exist biochemical systems where such data have already been collected, for example, of glycolysis in yeast. The initial value parameters are solved for in an optimisation sub-problem, for each step in the estimation of the other parameters. For systems starting in stationary oscillations, the sub-problem is solved in a straight-forward manner, without integration of the differential equations, and without the problem of local minima. This is possible because of a combination of a centre manifold and normal form reduction, which reveals the special structure of the Hopf bifurcation. The advantage of the method is demonstrated on the Brusselator.

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