Memory is central to all living organisms from single-celled bacteria to humans and yet very little is known about how memory is controlled in biology. This project proposes to uncover this long-standing mystery by building circuits that are capable of memory in bacteria. Bacterial systems are ideal for this study because they are more accessible than multi-cellular organisms, and they allow for ultra-high precision measurements. The project relies on innovative biologically friendly chips to collect streaming data from live bacteria. Specific questions address in this study are (1) How is memory inherited through hundreds of generations in bacteria? (2) How might memory be robust enough to withstand large changes at the molecular level such as synthesis and degradation of proteins? The project will have a large impact on the teaching of science; many courses have already adopted preliminary examples from this work. More broadly, since the goal of this project is to demonstrate that engineered circuits can accurately perform predefined tasks in living cells, this effort will fundamentally change the way we develop new tools for medicine and other uses.
One of the main goals of synthetic biology is to construct genetic circuits whose properties are not only robust on average as conditions change, but which can operate accurately in single cells without being corrupted by stochastic fluctuations. In many cases the dynamics also need to play out on a multi-generational time scale, which is particularly challenging because the states must be remembered even though the molecular hardware is replaced. Natural systems often face similar challenges, and though epigenetic memory is typically associated with higher organisms, bacteria must also decide between alternative states that can be maintained for tens or even hundreds of generations. Based on insights from stochastic theory for chemical reactions, and encouraged by substantial preliminary successes, this project will build synthetic oscillators and switches with multigenerational dynamics and highly precise timing in single cells. It will also characterize the on- and off-states of natural genetic circuits that exhibit slow switching, with a particular focus on DNA looping. Finally, a combination of classic bacterial genetics, microfluidic engineering, synthetic genetic oscillators, pulse generators based on DNA looping, and mathematical analyses will be used to probe the slow dynamics of flagellar promoters, which again reveal intriguing multi-generational effects. Thus, the project takes a broad systems and synthetic approach to quantify multigenerational switching at the level of individual bacterial cells.
