Jamie Lee , Hiu Lam Athena Wu , Ahmad A. Mannan , Yuumi Nakamura , Masayuki Amagai , Alan D. Irvine , Reiko J. Tanaka
{"title":"Toward the Next Generation of In Silico Modeling of Dynamic Host-Microbiota Interactions in the Skin","authors":"Jamie Lee , Hiu Lam Athena Wu , Ahmad A. Mannan , Yuumi Nakamura , Masayuki Amagai , Alan D. Irvine , Reiko J. Tanaka","doi":"10.1016/j.xjidi.2025.100385","DOIUrl":null,"url":null,"abstract":"<div><div>Understanding how the skin microbiota contributes to skin health and disease requires knowledge of the dynamic interactions between the skin and its resident microbes. In silico modeling complements in vivo and in vitro experiments by enabling a systems-level understanding of dynamic skin-microbiota interactions. However, the number of published in silico skin microbiota models remains limited. This paper provides the first comprehensive exploration of in silico skin microbiota modeling. We identify current challenges, learn from leading experimental validation approaches adopted in in silico gut microbiota research, and propose ways to enhance the predictive power of in silico skin microbiota models.</div></div>","PeriodicalId":73548,"journal":{"name":"JID innovations : skin science from molecules to population health","volume":"5 5","pages":"Article 100385"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JID innovations : skin science from molecules to population health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667026725000414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding how the skin microbiota contributes to skin health and disease requires knowledge of the dynamic interactions between the skin and its resident microbes. In silico modeling complements in vivo and in vitro experiments by enabling a systems-level understanding of dynamic skin-microbiota interactions. However, the number of published in silico skin microbiota models remains limited. This paper provides the first comprehensive exploration of in silico skin microbiota modeling. We identify current challenges, learn from leading experimental validation approaches adopted in in silico gut microbiota research, and propose ways to enhance the predictive power of in silico skin microbiota models.