Chapter 12: Engineering Cell-to-Cell Communication to Explore Fundamental Questions in Ecology and Evolution

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Synthetic biology has created countless examples of gene circuits that lead to novel behavior in cells. While the technological applications of these circuits, in terms of their use in medicine, industry, and to study systems biology has been acknowledged, synthetic biology is increasingly used to explore questions in evolution and ecology. Traditionally, evolutionary and ecological studies have taken two separate approaches to address scientific questions. One traditional approach uses mathematical modeling to capture the essential aspects of the dynamic or relationship under study. Research is performed in silico , allowing the researcher to explore multiple parameters in a well-defined system, as compared to studying the relationship in its natural setting. However, predictions generated by mathematical models are often not verified experimentally, leading to questions regarding their validity. On the other hand, studying a single dynamic in a natural setting offers its own set of challenges. Here, the single dynamic of interest may be subject to multiple interacting factors, which may obscure its true contribution to the relationship under study. Synthetic biology thus offers a well-rounded intermediate between these two approaches; modeling predictions are verified in living, experimental systems. This dual approach has allowed for the study of ecological and evolutionary dynamics that would be nearly impossible to study in the natural environment. Indeed, the number of studies that have utilized synthetic biology to study such relationships is growing quickly.

Original languageAmerican English
Title of host publicationChapter 12: Engineering Cell-to-Cell Communication to Explore Fundamental Questions in Ecology and Evolution
StatePublished - Jan 1 2015

Disciplines

  • Biological and Chemical Physics
  • Biology
  • Biophysics
  • Computational Biology

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