Department of Physics and Astronomy, University of Padova, CNISM and INFN, 35131, Padova, Italy.
Sci Rep. 2017 Sep 26;7(1):12323. doi: 10.1038/s41598-017-12521-1.
The increasing volume of ecologically and biologically relevant data has revealed a wide collection of emergent patterns in living systems. Analysing different data sets, ranging from metabolic gene-regulatory to species interaction networks, we find that these networks are sparse, i.e. the percentage of the active interactions scales inversely proportional to the system size. To explain the origin of this puzzling common characteristic, we introduce the new concept of explorability: a measure of the ability of an interacting system to adapt to newly intervening changes. We show that sparsity is an emergent property resulting from optimising both explorability and dynamical robustness, i.e. the capacity of the system to remain stable after perturbations of the underlying dynamics. Networks with higher connectivities lead to an incremental difficulty to find better values for both the explorability and dynamical robustness, associated with the fine-tuning of the newly added interactions. A relevant characteristic of our solution is its scale invariance, i.e., it remains optimal when several communities are assembled together. Connectivity is also a key ingredient in determining ecosystem stability and our proposed solution contributes to solving May's celebrated complexity-stability paradox.
不断增加的具有生态和生物学相关性的数据揭示了生命系统中广泛存在的新兴模式。通过分析从代谢基因调控到物种相互作用网络等不同数据集，我们发现这些网络是稀疏的，即活性相互作用的百分比与系统规模成反比。为了解释这种令人费解的共同特征的起源，我们引入了新的可探索性概念：衡量相互作用系统适应新介入变化的能力的度量。我们表明，稀疏性是一种涌现特性，源于对可探索性和动力稳健性的优化，即系统在底层动力学发生扰动后保持稳定的能力。具有更高连通性的网络会导致在可探索性和动力稳健性方面找到更好值的难度逐渐增加，这与新添加的相互作用的微调有关。我们的解决方案的一个重要特征是其标度不变性，即在组装在一起的多个社区中仍然是最优的。连通性也是确定生态系统稳定性的关键因素，我们提出的解决方案有助于解决 May 著名的复杂性-稳定性悖论。
