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Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics

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ISBN: 9783038429753 9783038429760 Year: Pages: VIII, 286 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Chemical Engineering
Added to DOAB on : 2018-07-04 13:24:50
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Investigating the microbiome is on the frontier of science. It has the potential to resolve many issues in the realms of energy, food, human health, the environment, and biotechnology. Scientists are trying to understand, in a fundamental way, how microbes influence each other and how they organize into interaction networks. These are the keys to predicting and engineering community function and properties of the microbiome. In this regard, mathematical modeling and computational analysis play an increasing role. This book is a collection of contributions from lead scientists in the field. It provides innovative approaches and fresh perspectives for modeling environmental communities and engineered microbial consortia. Reading this book will give researchers a solid look at cutting-edge science in microbial community modeling, and at the remaining challenges such modeling poses.

Biological Networks

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ISBN: 9783038974338 9783038974345 Year: Pages: 174 DOI: 10.3390/books978-3-03897-434-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering --- Internal medicine --- Biology
Added to DOAB on : 2019-01-10 11:14:23
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Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems.

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