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Programmable Microbe Motility

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Living organisms exhibit pattern formation in diverse processes including coordinated cell movement in response to self-generated signals. Understanding the mechanism behind these processes is key to understanding and predicting morphogenesis and embryonic development [1]. However the underlying principles of pattern formation and control are often buried in an overwhelmingly complex physiological context. One major question is how to decipher simple principles from these complex processes. In the words of Richard Feynman: 

"What I cannot build, I do not understand." 

The ability to rewire biomolecular circuits in living cells via synthetic biology provides a solution to this question. The idea is to explore possible mechanisms of pattern formation by constructing synthetic systems in bacteria and assessing whether they exhibit the high-level principles of biological self-organization. This research also has vast potential benefits for building "microrobotic" swarms of engineered bacteria with sophisticated centralized and decentralized control schemes. One particularly powerful genetic circuit paradigm is quorum sensing [2], which is a population-density dependent form of cooperative-feedback control. To this end, researchers have used quorum sensing (QS) molecules to couple bacterial cell density and motility, essentially regulating cell aggregation or clustering.  

Our research program is interested in extending previous QS synthetic genetic circuit designs of spatial/temporal self-organization in E. coli to explore alternative mechanisms of morphogenesis and to spark development of microbe-powered biorobots. 


Our genetic circuit implementation will manipulate multiple mechanisms involved in bacterial cell motility to create bi-stable responses in which the QS signal can cause cells to disseminate or accumulate based on the signal strength. 

In addition, we wish to create a modeling framework capable of accurately representing the associated dynamics and predicting emergent robust patterns in growing bacterial populations. 

By capturing the many interacting aspects of the system in a formal model we can ensure that we are reasoning properly about its behavior, especially in the presence of uncertainty. This will require substantial effort in building models that capture the relevant dynamics at the proper scales  as well as building an analytical framework for answering questions of biological relevance.  The ability to model the genetic circuit is crucial in order to explore design principles and manipulate centralized and decentralized control strategies. 

[1] J. Davies, Development. 144, 1146–1158 (2017).

[2] M. B. Miller, B. L. Bassler, Annu. Rev. Microbiol. 55, 165–199 (2001).