Biology is the root of variability: cautionary tales in Caenorhabditis elegans biology

Caenorhabditis elegans as an emerging model in food and nutrition research: importance of standardizing base diet

As a model organism that has helped revolutionize life sciences, Caenorhabditis elegans has been increasingly used in nutrition research. Here we explore the tradeoffs between pros and cons of its use as a dietary model based primarily on literature review from the past decade. We first provide an overview of its experimental strengths as an animal model, focusing on lifespan and healthspan, behavioral and physiological phenotypes, and conservation of key nutritional pathways. We then summarize recent advances of its use in nutritional studies, e.g. food preference and feeding behavior, sugar status and metabolic reprogramming, lifetime and transgenerational nutrition tracking, and diet-microbiota-host interactions, highlighting cutting-edge technologies originated from or developed in C. elegans. We further review current challenges of using C. elegans as a nutritional model, followed by in-depth discussions on potential solutions. In particular, growth scales and throughputs, food uptake mode, and axenic culture of C. elegans are appraised in the context of food research. We also provide perspectives for future development of chemically defined nematode food ("NemaFood") for C. elegans, which is now widely accepted as a versatile and affordable in vivo model and has begun to show transformative potential to pioneer nutrition science.

Uncertainty quantification in drug design

Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.

Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs

Population aging is one of the largest challenges of the 21st century. As more people live to advanced ages, the prevalence of age-related diseases and disabilities will increase placing an ever larger burden on our healthcare system. A potential solution to this conundrum is to develop treatments that prevent, delay or reduce the severity of age-related diseases by decreasing the rate of the aging process. This ambition has been accomplished in model organisms through dietary, genetic and pharmacological interventions. The pharmacological approaches hold the greatest opportunity for successful translation to the clinic. The discovery of such pharmacological interventions in aging requires high-throughput screening strategies. However, the majority of screens performed for geroprotective drugs in C. elegans so far are rather low throughput. Therefore, the development of high-throughput screening strategies is of utmost importance.