A computational framework for finding parameter sets associated with chaotic dynamics.

Q2 Medicine
S Koshy-Chenthittayil, E Dimitrova, E W Jenkins, B C Dean
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引用次数: 0

Abstract

Many biological ecosystems exhibit chaotic behavior, demonstrated either analytically using parameter choices in an associated dynamical systems model or empirically through analysis of experimental data. In this paper, we use existing software tools (COPASI, R) to explore dynamical systems and uncover regions with positive Lyapunov exponents where thus chaos exists. We evaluate the ability of the software's optimization algorithms to find these positive values with several dynamical systems used to model biological populations. The algorithms have been able to identify parameter sets which lead to positive Lyapunov exponents, even when those exponents lie in regions with small support. For one of the examined systems, we observed that positive Lyapunov exponents were not uncovered when executing a search over the parameter space with small spacings between values of the independent variables.

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寻找混沌动力学相关参数集的计算框架。
许多生物生态系统都表现出混沌行为,这可以通过相关动力系统模型中的参数选择进行分析,也可以通过实验数据分析进行实证。在本文中,我们使用现有的软件工具(COPASI、R)来探索动态系统,并发现存在混沌的 Lyapunov 指数为正的区域。我们通过几个用于模拟生物种群的动态系统,评估了软件优化算法发现这些正值的能力。这些算法能够找出导致正李雅普诺夫指数的参数集,即使这些指数位于支持度较小的区域。对于其中一个被研究的系统,我们观察到,在对自变量值之间间隔较小的参数空间进行搜索时,并没有发现正的李亚普诺夫指数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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