The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health - Eetemadi, Ameen; Rai, Navneet; Pereira, Beatriz Merchel Piovesan; Kim, Minseung; Schmitz, Harold; Tagkopoulos, Ilias (Frontiers in Microbiology, 2020)
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to be fully understood. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records, provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods and describe new scenarios to be unlocked in the next few years in the context of the current knowledge.