Human Microbiome Modulators: A Computational Biology Approach
An In Silico Approach Towards Modulating Human Microbiome Activity with Natural Products
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Beschreibung
This book presents a timely and comprehensive examination of the evolving relationship between the human microbiome, pharmacology, and computational science. This volume delves into how computational tools are transforming our understanding of microbiome-host-drug interactions and enabling the development of microbiome-targeted therapeutics. It explores in silico methodologies that predict microbial responses, model drug-induced dysbiosis, and design precision interventions to improve health outcomes.
The book is grounded in the recognition that the microbiome plays a critical role in immunity, chronic disease progression, and therapeutic efficacy. With detailed attention to the gut-brain axis, cancer prevention, personalized nutrition, and the metabolic activity of phytochemicals, the chapters offer a rich analysis of natural and synthetic modulators. It highlights how probiotics, prebiotics, and synbiotics influence health and disease, particularly when assessed through computational models.
In its second half, the volume shifts focus to the technological and analytical tools that power modern microbiome research. Topics include machine learning, virtual screening, molecular docking, systems biology, and multi-omics integration. Together, these tools offer scalable and predictive insights that can guide the discovery of novel therapies. The final section addresses ethical and regulatory challenges, clinical translation, and future directions in personalized microbiome medicine. A pioneering resource bridging biology, pharmacology, and computation, this book is intended for researchers, clinicians, pharmaceutical scientists, and graduate students engaged in microbiome science, drug discovery, computational biology, and precision medicine.
This book presents a timely and comprehensive examination of the evolving relationship between the human microbiome, pharmacology, and computational science. This volume delves into how computational tools are transforming our understanding of microbiome-host-drug interactions and enabling the development of microbiome-targeted therapeutics. It explores in silico methodologies that predict microbial responses, model drug-induced dysbiosis, and design precision interventions to improve health outcomes.
The book is grounded in the recognition that the microbiome plays a critical role in immunity, chronic disease progression, and therapeutic efficacy. With detailed attention to the gut-brain axis, cancer prevention, personalized nutrition, and the metabolic activity of phytochemicals, the chapters offer a rich analysis of natural and synthetic modulators. It highlights how probiotics, prebiotics, and synbiotics influence health and disease, particularly when assessed through computational models.
In its second half, the volume shifts focus to the technological and analytical tools that power modern microbiome research. Topics include machine learning, virtual screening, molecular docking, systems biology, and multi-omics integration. Together, these tools offer scalable and predictive insights that can guide the discovery of novel therapies. The final section addresses ethical and regulatory challenges, clinical translation, and future directions in personalized microbiome medicine. A pioneering resource bridging biology, pharmacology, and computation, this book is intended for researchers, clinicians, pharmaceutical scientists, and graduate students engaged in microbiome science, drug discovery, computational biology, and precision medicine.
Bridges microbiome science, pharmacology & computational biology while addressing technological and regulatory frontiers Highlights machine learning and multi-omics techniques to model and predict microbiome-drug interactions with precision Explores phytochemicals, probiotics, and prebiotics as microbiome modulators for targeted therapeutic strategies
Autor*in
Anupam Das Talukdar
Themen in »Human Microbiome Modulators: A Computational Biology Approach«
Pharmacomicrobiomics Microbiome-Based Drug Discovery In Silico Microbiome Modeling Computational Pharmacology Natural Product-Microbiome Interactions Machine Learning in Microbiome Research