About

Arthur Prindle is an Associate Professor of Chemical and Biological Engineering at Northwestern University, with a courtesy appointment in Biomedical Engineering. He holds a Ph.D. in Bioengineering from the University of California, San Diego, and a B.S. in Chemical Engineering with honors from the California Institute of Technology. His research focuses on understanding how molecular and cellular interactions give rise to collective behaviors in microbial communities. He employs techniques such as synthetic biology, quantitative microscopy, and microfluidics to investigate the molecular mechanisms behind cell communication across broad spatial and temporal scales. His goal is to apply these principles to develop new synthetic biology approaches to address biomedical problems through microbiome engineering.

Research topics

• Artificial Intelligence
• Computer Science
• Biology
• Biological system
• Biophysics
• Political Science
• Genetics
• Cognitive science
• Biochemistry
• Neuroscience
• Cell biology
• Data science
• Engineering ethics
• Ecology
• Psychology
• Computational biology
• Chemistry
• Biochemical engineering
• Engineering

Selected publications

• A Two-Dimensional Model of Potassium Signaling and Oscillatory Growth in a Biofilm

  Bulletin of Mathematical Biology · 2021 · 6 citations

  • Computer Science
  • Artificial Intelligence
  • Biological system

  Biofilms are complex communities of bacteria that exhibit a variety of collective behaviors. These behaviors improve their ability to survive in many different environments. One of these collective behaviors seen in Bacillus subtilis is the ability for starving cells to stop the growth of other cells using potassium signaling and voltage changes. This signaling produces an oscillatory growth pattern so that during periods of low growth the nutrients diffuse deeper into the biofilm and reach the nutrient-starved, interior regions of the biomass. In this paper, we develop a mathematical model to describe this oscillatory behavior, and we use this model to develop a two-dimensional simulation that reproduces many of the important features seen in the experimental data. This simulation allows us to examine the spatial patterning of the oscillatory behavior to better understand the relationships between the various regions of the biofilm. Studying the spatial components of the metabolic and voltage oscillations could allow for the development of new control techniques for biofilms with complex shapes.

  PublisherDOI

• Bioelectrical understanding and engineering of cell biology

  Journal of The Royal Society Interface · 2020 · 76 citations

  • Computer Science
  • Artificial Intelligence
  • Political Science

  , the understanding of cell behaviours emerging from molecular genetics must be complemented with physical and physiological ones, focusing on the intracellular and extracellular conditions within and around cells. Here, we argue that such a combination of genetics, physics and physiology can be grounded on a bioelectrical conceptualization of cells. We motivate the reasoning behind such a proposal and describe examples where a bioelectrical view has been shown to, or can, provide predictive biological understanding. In addition, we discuss how this view opens up novel ways to control cell behaviours by electrical and electrochemical means, setting the stage for the emergence of bioelectrical engineering.

  DOI

• Encoding Membrane-Potential-Based Memory within a Microbial Community

  Cell Systems · 2020 · 148 citations

  • Biology
  • Biophysics
  • Cell biology
  DOI

Frequent coauthors

• Gürol M. Süel

  University of California, San Diego

  23 shared

• Jordi García‐Ojalvo

  Pompeu Fabra University

  17 shared

• Sangeeta N. Bhatia

  16 shared

• Jintao Liu

  Center for Life Sciences

  15 shared

• Jeff Hasty

  University of California, San Diego

  12 shared

• Tal Danino

  Columbia University

  11 shared

• Sangeeta N. Bhatia

  Harvard–MIT Division of Health Sciences and Technology

  8 shared

• Rosa Martinez-Corral

  Harvard University

  7 shared

Labs

• Prindle LabPI