Phenotypic Resilience

In evolving biological populations, robustness equates to maintaining the features and traits of the population, also referred to as the phenotype. The term phenotype encompasses a vast array of traits from obvious external features to biochemical properties. Phenotypes or traits are determined by the interaction of an organism’s genetic makeup (also referred to as its genotype) and its environment. It is these genes that are inheritable and are passed on across generations, not the traits. However the process of natural selection operates directly upon the phenotype, not on the genotype, i.e. it is the traits of the organism that influence whether the organism can survive and reproduce in a given environment. Phenotypic robustness requires not only robustness with respect to changes in the external environment but robustness with respect to changes/mutations in the DNA/genotype.

Like other complex adaptive systems, the primary problem faced by biological systems is the need to explore for new innovations while preserving the old well-adapted functionality until something new and better can be found. This process also needs to be undertaken in an efficient manner with minimal resource usage. Through the metaphor of the fitness landscape (see diagram below) where higher peaks represent fitter populations, the problem can be restated as follows: how does an evolving population move from a hill such as point A to a higher peak such as point B when incremental changes will move it into a valley with lower fitness than point A?1 How can the system evolve in a transformative and radical manner to create what Andreas Wagner2 has called ‘game-changers’ such as photosynthesis and complex nervous systems?

Although the task appears insurmountable, the conflict between robustness, innovability and efficiency disappears when we allows ourselves to consider a few key characteristics of real-life genotype-phenotype mapping:

• Micro-fragility: Phenotypic robustness does not depend upon genotypic robustness. There is ample evidence that phenotypes are fairly robust with respect to both genetic mutations as well as environmental change3. Phenotypic robustness by definition implies that the phenotype does not change with most mutations in the genotype. This would seem to rule out the sort of phenotypic variability/evolvability that is required for transformative change – after all, how can new phenotypes be explored if genetic alterations usually have no impact on the phenotype?
• Distributed Robustness: Most biological systems contain a much larger number of genotypes than phenotypes. Even key functions in biological systems can be maintained in many different ways4. This robustness is distributed from the presence of diverse multi-functional components with partial functional overlap rather than the presence of redundant copies of each gene, a property known as degeneracy56.
• Cryptic Variation: Genotypes with the same phenotype form networks and most changes in such genotype networks are “nearly neutral” and are only weakly selected for. Movement across this network allows the accumulation of cryptic, underground variation7 that serves as a reservoir that enables transformative phenotypic change.

Robustness and innovability are therefore not only consistent with each other, robustness is in fact a precondition that enables transformative complex change. The distributed nature of the system’s robustness enables such a system to be maintained in a near-optimal manner without requiring an excessive usage of resources.