The recent advances in high through-put data generation for DNA/RNA, proteins and metabolites has resulted in a paradigm shift in how we seek to answer some of the fundamental questions of biology. Over the past decade, significant amounts of these large data sets encompassing resident microbial communities (microbiome), specific host responses and environmental conditions have been generated. To date the integration and exploitation of these complex datasets in a structured way has been highly problematic. However recent advancements in in-silico methodologies can for the first time help to unlock the full potential of these data, facilitating improved understanding of and discovery of novel interventions for host-microbiome interactions. With the advent of these technologies it has become apparent that interactions between environmental, host and microbial factors give rise to the various changes in skin homeostasis that result in cosmetic conditions such as dry skin and dandruff. Dandruff and dry skin are widespread conditions impacting over 50% of the world's population affecting quality of life including self/body confidence and their treatment is the basis of a sector worth over 10bn Euros annually. In this study, in collaboration with our industrial partners, Unilever, we will investigate the physiological changes of normal, dry skin and dandruff through unique integration of computational biology and modelling with microbiology. We will develop a computational and experimental platform for skin host-microbiome interactions to reveal the microbial mechanisms involved in different skin states. Using this approach, we will identify and evaluate new therapeutic targets as well as reveal the underlying physiological events in skin homeostasis. Using a combination of skin samples collected by tape strips from normal, dry skin and dandruff, as well as data generated from reconstituted skin models and keratinocyte monolayers, we will generate data that accurately describes skin-microbe interactions. we will also identify the key species and strains of Malassezia, Staphylococcus and Cutibacterium associated with different skin states. In parallel by using the available multi-omics data from Unilever and the public domain, we will generate computational models for microbes and host skin tissue and cells. Having both in-silico and in-vitro set ups, we will investigate the impact of key metabolites and anti-metabolites on the relationship between the skin and key microbes and microbial communities. Finally, we will explore the impact of key host factors, such as cytokines (e.g. IL-36, IL-1, IL-17, IL-20 family) and antimicrobial peptides (e.g. beta-defensins, S100, LL-37) on the resident microbial communities. We will then categorize these therapies based on their mode of action on skin-microbiomes interactions. The new therapeutic targets generated and validated through this combination of both computational and experimental techniques can then be tested for host toxicity and efficacy. This cutting-edge integrative platform could be easily extended to identify new targets or drugs for different microbial constituents in human body, their association with a range of hosts and pathologies. As such it will delineate an entirely novel approach to investigating host-microbiome interactions that will have broad applicability across a wide range of sectors, including medical, veterinary, cosmetic and agricultural.