Studying biochemical networks at a planetary scale reveals a deeper level of organization than what has been understood so far.

Significance: Known examples of life all share the same core biochemistry going back to the last universal common ancestor (LUCA), but whether this feature is universal to other examples, including at the origin of life or alien life, is unknown. We show how a physics-inspired statistical approach identifies universal scaling laws across biochemical reactions that are not defined by common chemical components but instead, as macroscale patterns in the reaction functions used by life. The identified scaling relations can be used to predict statistical features of LUCA, and network analyses reveal some of the functional principles that underlie them. They are, therefore, prime candidates for developing new theory on the “laws of life” that might apply to all possible biochemistries.

On May 28th and 29th, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in network science and epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in network science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found.

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.

Inferring properties of exoplanets from their atmospheres presents technical challenges in data collection due to low resolution and low signal-to-noise ratio (S/N) and theoretical challenges in the predictions made from forward-modeling due to errors introduced via incomplete or inaccurate assumptions in atmospheric physics and chemistry. The combination of these factors makes developing techniques to identify the most predictive features robust to low S/N and model error an increasingly important challenge for exoplanet science. Here we implement a multivariate approach to identify optimal predictors of the state of disequilibria. As a case study we focus on the prediction of vertical mixing (parameterized as eddy diffusion) in hot Jupiter atmospheres. We use multivariate information contained in molecular abundances, reaction network topology, and Gibbs free energy to demonstrate the variation in prediction efficacy of the vertical mixing coefficient (K _zz) from different model information. While current approaches target inferring molecular abundances from spectral data, our results indicate that the set of optimal predictors of K _zz varies with planetary properties such as irradiation temperature and metallicity. In most cases, multivariate data composed of network topological variables, which capture system-level features, perform as well as the set of optimal predictors and better than any individual variable. We discuss future directions, where identifying the set of optimal predictors should be useful for quantitatively ranking atmospheres in terms of their distance from thermochemical equilibrium, provide target variables for the development of new tools for inverse modeling, and provide applications to the longer-term goal of detection of disequilibria associated with life.