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90 Building bio-latches and checkpoint controls 如何製造出細胞週期

Hello everyone! Today I am going to introduce an exciting work that successfully engineered E. coli with customizable checkpoint control systems! I highly recommend reading the original article to get more details.

Suggested reading:
Andrews, L. B., Nielsen, A. A., & Voigt, C. A. (2018). Cellular checkpoint control using programmable sequential logic. Science, 361(6408). doi:10.1126/science.aap8987.

Cells without checkpoint control will be disastrous: they proliferate haphazardly rather than synchronously, respond equally to signals and noises, and fail to integrate memories and new inputs. To equip cells with desired functions, it is imperative to develop a method that allows scientists to build customizable checkpoint control. In a recent study published on Science, Andrews and colleagues borrowed the concepts of sequential logic and proposed a way to design checkpoint control systematically.

The concept of sequential logic goes like this: the system can hold memories about its previous state, receive new inputs from the environment, take both its memories and input signals into consideration, update its current state and produce correct outputs. Since a system with checkpoint control will stay the same until it receives the correct signal, they can be suitably described with sequential logic. These circuits contain flip-flops or latches: both of them store memories about the previous states of the circuits and change their states in response to external signals such as “set” or “reset.” To build a biological latch or flip-flop, the genetic or metabolic circuits must have two stable states and be able to switch between them in response to external signals – a property called “bi-stability.”

The scientists chose gene repressors whose activities could be modulated by specific inputs such as arabinose in E. coli, modeled the gene output regulated by repressors with an ordinary differential equation (ODE), calculated the steady-state solution as Hill function – an empirical function that can describe cooperativity, and compared the regulation system to a NOT gate. The steady state solution corresponds to a curve on the phase plane with axes being the values of input and output, or the “nullcline”, since the derivatives of ODE vanish on this curve. The researchers then combined two
mutually-regulated repressors, in the hope of forming a bi-stable system. Yet the outcome of the system depends on the relative strength of the two repressors: if one of the repressors out-performs the other one too much, the nullclines of the two repressors will intersect on the only fixed point of the system. On the other hand, if the strengths of the two repressors match, the nullclines of the two repressors will intersect on three fixed points – two being stable and one being unstable – and act as a biological latch.

Andrews and colleagues then measured the output activities with yellow fluorescent proteins and validated the behavior of biological latches with specially designed input sequences: their biological latches can switch between states in response to changes in external signals and hold a one-bit memory up to 48 hours when all signals are turned off. The research team then combined 10 different repressor genes, analyzed the systems on phase plane, and built 19 functional latches. Combining multiple latches yields a system with multiple-bit memory and multiple states. The research team then analyzed the truth table of the circuit and drew diagrams that show how the system will change from one state to another. Specifically, the team built a three-state system that changes their state unidirectionally in response to specific signals, acting like a cell cycle.

Andrews and colleagues successfully provided a design backbone with unlimited applications. By changing the input sensors, we can regulate the growth and development of cells synchronously with light or temperature. We can also make bacteria produce degradation enzyme when it encounters certain pollutants. This work is definitely a major step toward engineering cells into living computers that will revolutionize our world.

(This work was originally submitted to "Writing in the Sciences" on Coursera as my essay homework.)