Evolution in the Debian GNU/Linux software network: analogies and differences with gene regulatory networks. Issue 163 (26th February 2020)

• Record Type: Journal Article
• Authors: Villegas, Pablo; Muñoz, Miguel A.; Bonachela, Juan A.
• Is Part Of: Journal of the Royal Society interface. Volume 17:Issue 163(2020)
• Publication Date: 2020-02-26
• Subjects: evolving networks -- network resilience -- information transmission; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2019.0845
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : Biological networks exhibit intricate architectures deemed to be crucial for their functionality. In particular, gene regulatory networks, which play a key role in information processing in the cell, display non-trivial architectural features such as scale-free degree distributions, high modularity and low average distance between connected genes. Such networks result from complex evolutionary and adaptive processes difficult to track down empirically. On the other hand, there exists detailed information on the developmental (or evolutionary) stages of open-software networks that result from self-organized growth across versions. Here, we study the evolution of the Debian GNU/Linux software network, focusing on the changes of key structural and statistical features over time. Our results show that evolution has led to a network structure in which the out-degree distribution is scale-free and the in-degree distribution is a stretched exponential. In addition, while modularity, directionality of information flow, and average distance between elements grew, vulnerability decreased over time. These features resemble closely those currently shown by gene regulatory networks, suggesting the existence of common adaptive pathways for the architectural design of information-processing networks. Differences in other hierarchical aspects point to system-specific solutions to similar evolutionary challenges.