From Prediction to Prescription: Evolutionary Optimization of Nonpharmaceutical Interventions in the COVID-19 Pandemic.

Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with nonpharmaceutical interventions, such as social distancing restrictions and school and business closures. This article demonstrates how evolutionary AI can be used to facilitate the next step, i.e., determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription, it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. Early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. They also demonstrate that results of lifting restrictions can be unreliable, and suggest creative ways in which restrictions can be implemented softly, e.g., by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.

Probing chromatin accessibility with small molecule DNA intercalation and nanopore sequencing.

Genome-wide identification of chromatin organization and structure has been generally probed by measuring accessibility of the underlying DNA to nucleases or methyltransferases. These methods either only observe the positioning of a single nucleosome or rely on large enzymes to modify or cleave the DNA. We developed adduct sequencing (Add-seq), a method to probe chromatin accessibility by treating chromatin with the small molecule angelicin, which preferentially intercalates into DNA not bound to core nucleosomes. We show that Nanopore sequencing of the angelicin-modified DNA is possible and allows visualization and analysis of long single molecules with distinct chromatin structure. The angelicin modification can be detected from the Nanopore current signal data using a neural network model trained on unmodified and modified chromatin-free DNA. Applying Add-seq to Saccharomyces cerevisiae nuclei, we identified expected patterns of accessibility around annotated gene loci in yeast. We also identify individual clusters of single molecule reads displaying different chromatin structure at specific yeast loci, which demonstrates heterogeneity in the chromatin structure of the yeast population. Thus, using Add-seq, we are able to profile DNA accessibility in the yeast genome across long molecules.

Flavor-cyber-agriculture: Optimization of plant metabolites in an open-source control environment through surrogate modeling.

Food production in conventional agriculture faces numerous challenges such as reducing waste, meeting demand, maintaining flavor, and providing nutrition. Contained environments under artificial climate control, or cyber-agriculture, could in principle be used to meet many of these challenges. Through such environments, phenotypic expression of the plant-mass, edible yield, flavor, and nutrients-can be actuated through a "climate recipe," where light, water, nutrients, temperature, and other climate and ecological variables are optimized to achieve a desired result. This paper describes a method for doing this optimization for the desired result of flavor by combining cyber-agriculture, metabolomic phenotype (chemotype) measurements, and machine learning. In a pilot experiment, (1) environmental conditions, i.e. photoperiod and ultraviolet (UV) light (known to affect production of flavor-active molecules in edible plants) were applied under different regimes to basil plants (Ocimum basilicum) growing inside a hydroponic farm with an open-source design; (2) flavor-active volatile molecules were measured in each plant using gas chromatography-mass spectrometry (GC-MS); and (3) symbolic regression was used to construct a surrogate model of this chemistry from the input environmental variables, and this model was used to discover new combinations of photoperiod and UV light to increase this chemistry. These new combinations, or climate recipes, were then implemented in the hydroponic farm, and several of them resulted in a marked increase in volatiles over control. The process also led to two important insights: it demonstrated a "dilution effect", i.e. a negative correlation between weight and desirable chemical species, and it discovered the surprising effect that a 24-hour photoperiod of photosynthetic-active radiation, the equivalent of all-day light, induces the most flavor molecule production in basil. In this manner, surrogate optimization through machine learning can be used to discover effective recipes for cyber-agriculture that would be difficult and time-consuming to find using hand-designed experiments.