Skip to main content
eScholarship
Open Access Publications from the University of California

From molecules to dynamic biological communities.

  • Author(s): McDonald, Daniel
  • Vázquez-Baeza, Yoshiki
  • Walters, William A
  • Caporaso, J Gregory
  • Knight, Rob
  • et al.
Abstract

Microbial ecology is flourishing, and in the process, is making contributions to how the ecology and biology of large organisms is understood. Ongoing advances in sequencing technology and computational methods have enabled the collection and analysis of vast amounts of molecular data from diverse biological communities. While early studies focused on cataloguing microbial biodiversity in environments ranging from simple marine ecosystems to complex soil ecologies, more recent research is concerned with community functions and their dynamics over time. Models and concepts from traditional ecology have been used to generate new insight into microbial communities, and novel system-level models developed to explain and predict microbial interactions. The process of moving from molecular inventories to functional understanding is complex and challenging, and never more so than when many thousands of dynamic interactions are the phenomena of interest. We outline the process of how epistemic transitions are made from producing catalogues of molecules to achieving functional and predictive insight, and show how those insights not only revolutionize what is known about biological systems but also about how to do biology itself. Examples will be drawn primarily from analyses of different human microbiota, which are the microbial consortia found in and on areas of the human body, and their associated microbiomes (the genes of those communities). Molecular knowledge of these microbiomes is transforming microbiological knowledge, as well as broader aspects of human biology, health and disease.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
Current View