Nagata S, Kikuchi M (2020) Emergence of cooperative bistability and robustness of gene regulatory networks. PLoS Comput Biol 16(6): e1007969. https://doi.org/10.1371/journal.pcbi.1007969
Living systems have developed through a long history of Darwinian evolution. They acquired characteristic properties distinct from other physical systems; one is biological function. Another important property, which is overlooked by non-experts, is robustness to noise and mutation. Here, robustness means that a system does not lose its functionality when exposed to disturbances. Then, how do they relate to each other? In this paper, we explored this question using a toy model of gene regulatory networks (GRNs). While evolutionary simulations are usually used for such purposes, we instead generated GRNs randomly and classified them according to functionality. By requiring sensitive responses to environmental change as a function, we found that bistability emerges as a common property of highly-functional GRNs. Since this property does not depend on a particular evolutionary pathway, if the evolution was rewound and repeated over and over again, phenotypes with the same property would always evolve. At the same time, such bistable GRNs were robust to noise. We also found that GRNs robust to mutation were not extremely rare among the highly-functional GRNs. This implies that mutational robustness would be readily acquired through evolution.
Magda Fontana, Martina Ioric, Fabio Montobbio, Roberta Sinatra
Volume 49, Issue 7, September 2020, 104063
Novelty indicators are increasingly important for science policy. This paper challenges the indicators of novelty as an atypical combination of knowledge (Uzzi et al., 2013) and as the first appearance of a knowledge combination (Wang et al., 2017). We exploit a sample of 230,854 articles (1985 – 2005), published on 8 journals of the American Physical Society (APS) and 2.4 million citations to test the indicators using (i) a Configuration Null Model, (ii) an external validation set of articles related to Nobel Prize winning researches and APS Milestones, (iii) a set of established interdisciplinarity indicators, and (iv) the relationship with the articles’ impact. We find that novelty as the first appearance of a knowledge combination captures the key structural properties of the citation network and finds it difficult to tell novel and non-novel articles apart, while novelty as an atypical combination of knowledge overlaps with interdisciplinarity. We suggest that the policy evidence derived from these measures should be reassessed.
Juniper Lovato, Antoine Allard, Randall Harp, Laurent Hébert-Dufresne
Personal data is not discrete in socially-networked digital environments. A single user who consents to allow access to their own profile can thereby expose the personal data of their network connections to non-consented access. The traditional (informed individual) consent model is therefore not appropriate in online social networks where informed consent may not be possible for all users affected by data processing and where information is shared and distributed across many nodes. Here, we introduce a model of "distributed consent" where individuals and groups can coordinate by giving consent conditional on that of their network connections. We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy. Distributed consent is of course not a silver bullet, since it does not follow data as it flows in and out of the system, but it is one of the most straightforward non-traditional models to implement and it better accommodates the fuzzy, distributed nature of online data.
Phys. Rev. E 102, 012303
Today’s society faces widening disagreement and conflicts among constituents with incompatible views. Escalated views and opinions are seen not only in radical ideology or extremism but also in many other scenes of our everyday life. Here we show that widening disagreement among groups may be linked to the advancement of information communication technology by analyzing a mathematical model of population dynamics in a continuous opinion space. We adopted the interaction kernel approach to model enhancement of people’s information-gathering ability and introduced a generalized nonlocal gradient as individuals’ perception kernel. We found that the characteristic distance between population peaks becomes greater as the wider range of opinions becomes available to individuals or the more attention is attracted to opinions distant from theirs. These findings may provide a possible explanation for why disagreement is growing in today’s increasingly interconnected society, without attributing its cause only to specific individuals or events.
J.C. Correa, H. Laverde-Rojas, F. Marmolejo-Ramos, J. Tejada, Š. Bahník
Citations are often used as a metric of the impact of scientific publications. Here, we examine how the number of downloads from Sci-hub as well as various characteristics of publications and their authors predicts future citations. Using data from 12 leading journals in economics, consumer research, neuroscience, and multidisciplinary research, we found that articles downloaded from Sci-hub were cited 1.72 times more than papers not downloaded from Sci-hub and that the number of downloads from Sci-hub was a robust predictor of future citations. Among other characteristics of publications, the number of figures in a manuscript consistently predicts its future citations. The results suggest that limited access to publications may limit some scientific research from achieving its full impact.