Synthetic Biology Interdisciplinary Challenge 8
What is the role of evolution and evolvability in synthetic biology?

Challenge Summary
To circumvent the time-consuming, ad hoc nature of constructing new biological systems, some investigators have advocated efforts to ‘standardize’ biological parts in such a way that their behavior in novel assemblies or environments becomes more predictable. The notorious complexity and context-dependency of the behavior of biological parts and systems, however, makes such standardization extremely challenging.  For example, a biological device that is functional in one cell type may not exhibit the same behavior in another, even closely-related cell type. The stochastic nature of biochemical systems also presents a hurdle for prediction and standardization.  It is unlikely in fact that biological parts can ever be fully standardized, and engineering methods that enable rapid optimization of synthetic biological systems will be needed. Nature’s optimization algorithm is evolution: evolution fine-tunes the functions of parts in new contexts and optimizes their assemblies in nature. Can directed evolution be used to do the same in synthetic biology?  Evolution is also the source of all biological parts—can directed evolution reliably generate useful parts, especially those unlikely to be found in Nature?

All biological systems evolve under the pressure of mutation and natural selection.  Natural selection, however, leads to the destruction of synthetic systems that place the organism at a selective disadvantage relative to dysfunctional mutants. Synthetic biology will have to confront this ubiquitous feature of living systems.

A hallmark of biological systems is their ability to adapt to changing environments and challenges.  Modularity appears to be a useful feature of evolvable, rapidly-adapting systems some biological systems and even components are highly modular, such that components and sub-components can be rapidly swapped in and out to generate new functions. Eukaryotic signaling systems are a good example, but prokaryotes rely on much less modular systems that nonetheless serve them very well.  Are there costs of evolvability in terms of system performance? 

Key Questions

• When and how can evolutionary methods contribute to design of synthetic systems? 
• How can evolutionary methods be best integrated with “rational” design, including computational design?  What is the role of modeling?
• Are there design objectives that can be addressed only through evolutionary strategies?  Are there objectives for which evolutionary strategies are unnecessary?
• What are the best targets for evolutionary optimization? Molecules? Circuits? Organisms?
• What technologies and tools will be needed for rapid, efficient evolutionary optimization?
• What strategies can we use to overcome the tendency of synthetic biological systems to mutate and escape programmed control?
• How do we design systems and host organisms to ensure genetic stability?
• How can we best understand mechanisms and consequences of mutation and develop routes for repair that enable designed functionality to be maintained?
• To what extent is it important to pursue strategies for designing evolvable systems? What are the key features?