What is directed evolution?

In protein engineering, the method of directed evolution is used to harness the power of natural selection. This is to evolve certain proteins or RNA with desirable properties not found in nature. Rather than swapping out different components of the structure, directed evolution can be used to manipulate the behaviour of these components. Then, by screening or selection, we can obtain the desired phenotype.

There are three typical steps involved that include:

 

1. Diversification: The gene that encodes the desired protein is mutated and recombined at random to create a large library of gene mutants. Techniques commonly used in this step involve PCR and DNA shuffling.

 

2. Selection: The library is tested for the existence of mutants possessing the property of interest using screening or selection.

       a. Selections automatically remove all mutants that do not function.

       b. Screening enables the researcher to manually detect and isolate the high-performing mutants.

 

3. Amplifications: The mutations identified int he selection or screening process are replicated several times so it allows the researchers to sequence their DNA and recognize what mutations have occurred.

For optimizing efficiency, altering more than one parameter would be preferable in order to obtain the desired protein. Therefore, we accululate mutations over multiple generations.

In order to examine how changes in the parameters were affecting the stability of the steady states, we could do uniform bifurcation analysis e.g. determining th regions of stability.

In the experiments, we were able to use the FACS (fluorescence activated cells sorting) machine to separate the proteins with the brightest colours and thus use the strongest ones. Once we had this, we could put the proteins through the system again as initial conditions and then we would have a more distinctive switch.

Pros

Directed evolution can systematically perturb and distinguish components then, using these perturbed components, we can gain further insight into the functionality of the normal circuit.

If some of the pathways enhance the desired performance, then iterative rounds of random mutation of the circuit and screening for the desired properties may provide steady improvement. This only works if the initial design is close to a better design.

Why use maths to model directed evolution?

There are three primary limitations that mathematical modelling can overcome:

 

1. The evolutionary space for a genetic circuit is too large to explore efficiently.

 

2. Detuning parameters (reducing function) is much easier than improving function.

 

3. Although selecting for independent properties is possible, it usually requires setting up multiple rounds of screening or selection.

Procedure

 

1. Using a programme which integrates the equations modelling the system over a given period of time, begin with parameters either given in literature or determined from experiment.
 

2. Start with one protein dominant, and induce a switch by flooding the system with the promoter for the other protein.
 

3. Take the final conditions as the initial conditions and vary one parameter, with the outcome being the final conditions of the system for each value of the variable parameter.
 

4. Taking the values which produced the most dramatic switching behaviour to be the new initial conditions, vary a different parameter.
 

5. Repeat these steps for each parameter in the system to evenutally produce a clearly-defined switch that will somewhat resemble the original system.

Results

Fig 1. shows the behaviour of the original system over a set period of time.

As shown, the system has been integrated over a long enough timespan such that it has reached equilibrium.

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