SURF 2017: Synthetic modules for control of bacterial growth

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2017 SURF project description

  • Mentor: Richard Murray
  • Co-mentor: Reed McCardell

Complex consortia of microbes occur nearly everywhere in nature, including in the human body, and are implicated in a number of significant environmental and human physiological processes. The composition of a microbial community, particularly the resident community in the human GI tract, is dynamic in response to small and large changes in the environment outside and inside the host. In an attempt to build understanding and control of the dynamics of microbial community composition, we are working to design communities with various population level behaviors effected by synthetic controllers of bacterial growth.

The approach we are taking to this goal involves cell-cell communication between bacteria in culture, genetic circuits that translate population information into signals inside each cell and synthetic growth controllers activated or repressed by these intracellular signals. Together, modules performing each of these functions build a community of bacteria capable of reacting to its composition by slowing or speeding up growth of community members.

At the moment, genetic toxin/antitoxin systems, particularly ccdB/ccdA, are the most well-characterized and well-used growth controllers [1,2]. These are few and do not act with much dynamic range or diversity of mechanism. One big focus of our project is the development of a collection of growth control systems acting with different mechanisms, dynamic range and effect sizes. We envision using these controllers in combination or individually to enable various population level dynamics in bacterial communities.

Of particular interest are two types of growth control that are very relevant to our understanding and control of natural microbial communities. One is control via secreted antibiotics, or bacteriocins (which has seen some investigation previously [3]), and the other is control via metabolic interdependency and resource availability. Metabolic population control has been achieved using genetically-modified auxotrophs [4], but we plan to investigate ways around genome modification in favor of plasmid based modules for metabolic control.

This SURF project will investigate one of these mechanisms of growth control with goals:

  • Clone at least one effector and all its required component parts (bacteriocin +/- resistance gene; metabolic enzyme inhibitor/activator)
  • Assess dynamic range, effect size off/on dynamics of growth controller(s)
  • Develop a mathematical model for the action of growth controller(s)
  • Think about other mechanisms of controlling bacterial growth (described below)

The specific growth control topics presented above are ideas from the mentor and co-mentor, but we are open to mechanisms of growth control of all types. Students are encouraged to design controllers of their own with their mentors and investigate the parameters of action.

Required skills: Students should be comfortable with basic molecular biological techniques for cloning of genes and bacterial transformation. The work will be performed in bacteria and students should be familiar with bacterial culture. Quantification of growth control parameters is very important for efforts to computationally model growth control, and skills in programming and data analysis are required. Modeling of growth control will be based on differential equations; students should be familiar with the basics of differential equations mathematics.


  1. You, L., Cox, R. S., Weiss, R. & Arnold, F. H. Programmed population control by cell–cell communication and regulated killing. Nature 428, 868–871 (2004).
  2. Balagaddé, F. K. et al. A synthetic Escherichia coli predator-prey ecosystem. Mol. Syst. Biol. 4, 187 (2008).
  3. Weber, M. F., Poxleitner, G., Hebisch, E., Frey, E. & Opitz, M. Chemical warfare and survival strategies in bacterial range expansions. J. R. Soc. Interface 11, 20140172 (2014).
  4. Kerner, A., Park, J., Williams, A. & Lin, X. N. A programmable escherichia coli consortium via tunable symbiosis. PLoS One 7, 1–10 (2012).