Socially driven negative feedback regulates activity and energy use in ant colonies.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Maurizio Porfiri, Nicole Abaid, Simon Garnier
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引用次数: 0

Abstract

Despite almost a century of research on energetics in biological systems, we still cannot explain energy regulation in social groups, like ant colonies. How do individuals regulate their collective activity without a centralized control system? What is the role of social interactions in distributing the workload amongst group members? And how does the group save energy by avoiding being constantly active? We offer new insight into these questions by studying an intuitive compartmental model, calibrated with and compared to data on ant colonies. The model describes a previously unexplored balance between positive and negative social feedback driven by individual activity: when activity levels are low, the presence of active individuals stimulates inactive individuals to start working; when activity levels are high, however, active individuals inhibit each other, effectively capping the proportion of active individuals at any one time. Through the analysis of the system stability, we demonstrate that this balance results in energetic spending at the group level growing proportionally slower than the group size. Our finding is reminiscent of Kleiber's law of metabolic scaling in unitary organisms and highlights the critical role of social interactions in driving the collective energetic efficiency of group-living organisms.

社会驱动的负反馈调节着蚂蚁群落的活动和能量使用。
尽管对生物系统的能量学进行了近一个世纪的研究,我们仍然无法解释社会群体(如蚁群)的能量调节。在没有中央控制系统的情况下,个体如何调节其集体活动?社会互动在群体成员之间分配工作量方面起着什么作用?群体又是如何通过避免持续活动来节约能量的?我们通过研究一个直观的分区模型,并与蚂蚁群落的数据进行校准和比较,对这些问题提出了新的见解。该模型描述了个体活动驱动的社会正反馈和负反馈之间以前从未探索过的平衡:当活动水平较低时,活跃个体的存在会刺激不活跃个体开始工作;然而,当活动水平较高时,活跃个体会相互抑制,从而有效地限制了同一时间内活跃个体的比例。通过对系统稳定性的分析,我们证明了这种平衡会导致群体层面的能量消耗增长比群体规模的增长成比例地慢。我们的发现让人联想到单位生物的克莱伯代谢缩放定律,并突出了社会互动在推动群居生物集体能量效率方面的关键作用。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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