Catching the drift of experimental evolution.

Blog author Doc Edge is a graduate student in Noah Rosenberg's lab.

Blog author Doc Edge is a graduate student in the Rosenberg lab.

One picture of science, popular in dramatic depictions of scientific history, shows an isolated theorist working out the implications of a bold idea. In this picture, the theorist relies, somewhat indifferently, on the ingenuity of an unknown, unspecified experimentalist who will someday—perhaps many years later—test the theorist’s ideas against unforgiving reality. It’s trite to point out that this picture isn’t a good representation of actual scientific practice. The relationship between theory and experiment both is and should be more bidirectional, more collaborative, and more nuanced than this, as philosophers of science, sociologists of science, and scientists themselves have pointed out for decades. A recent paper by CEHG graduate student Arbel Harpak (Pritchard lab) and Guy Sella (Stanford PhD 2001 from Marc Feldman’s group, in the less-sunny, pre-CEHG days) is a lesson in how experimentally-motivated theoretical work can inform both future theory and future experiment.

(Neutral) Evolution in a Test Tube

Harpak and Sella take up one of the most interesting contexts for the study of evolution: the serial-transfer experiment. In serial-transfer experiments, microorganisms (usually E. coli or yeast) are allowed to divide in an isolated container with some nutrients. Once the population in a container reaches a pre-specified size (which Harpak and Sella call N2), a sample of a pre-specified size (N1) is taken from the original container and transferred to a new one. This process of growth, sampling, and transfer is then repeated many times.

A schematic of a serial transfer experiment (from Sprouffske et al., 2012) [Note: in a previous version this image was mistakenly attributed to A. Harpak.]

Both experimenters and theorists have focused on understanding selection in serial-transfer experiments. This is sensible: the serial-transfer scenario allows researchers to manipulate the parameters that influence adaptation—population size, selective pressure, etc.—and to study the dynamics of adaptation. Harpak and Sella start from a premise that has been less widely-appreciated in the serial-transfer context: namely, that the other forces of evolution, including drift and demography, are also active in serial-transfer contexts. These selectively-neutral forces are ever-present, running in the background of experiments designed to study selection. It is not that Harpak and Sella claim that neutral forces are as or more important than selective forces in a typical serial-founder experiment—they acknowledge that selective forces likely predominate. But they rightly point out that we cannot fully understand what selection does without understanding the context in which it acts. Population geneticists have known this for a long time, but until now, they have not formally applied this insight to serial-transfer experiments.

Harpak and Sella develop a fully-articulated model for neutral evolution in a serial-transfer context. In population-genetic terms, the outcome of a serial-transfer experiment is the product of repeated periods of growth followed by bottlenecks. They build on the intuitions that population geneticists already have about population growth and bottlenecks to sketch a picture of what serial transfers will do to measurements of genetic diversity. Diversity slowly builds up as the population grows and as more distinct lineages appear in the population. But when a small subset of the population is removed and transferred to a new container, only the diversity that is represented in that small subset will be present in the next phase of the population’s growth. When the experiment involves many cycles of serial transfer, the genetic diversity ends up looking like that of a population with a constant size of N1*log2(N2/N1), where N1 is the number of organisms transferred during each cycle and N2 is the size to which the population grows just before the end of the cycle. This is a neat result—as has been found in other contexts, the genetic diversity is more strongly influenced by extremity of the bottleneck than it is by the maximum size to which the population grows.

Relaxing the Assumptions

This result agrees with population geneticists’ intuitions about demographic changes and genetic diversity. Nonetheless, there are two reasons why it is unrealistic for actual serial-transfer experiments. First, most serial-transfer experiments are not long enough to reach the predicted equilibrium state. Second, the equilibrium prediction assumes that all the microorganisms in the sample divide at the same time, but in reality, there is variation in the length of time that a cell persists before dividing. Harpak and Sella adjust their model to deal with both of these issues. In considering the shorter time scales typical of serial-transfer experiments, they find that the shorter the experiment is, the more the genetic diversity will be dominated by rare variation—genetic differences seen only in a single individual, for example. They also derive results for another version of their model in which the length of time to the next cell division is random. When cells vary in how long they take to divide, the average number of differences between any two cells decreases. This is because cells that divide quickly are likely to leave more offspring in subsequent generations. This means that any two cells in a subsequent generation are more likely to be recently related, since there is an increased chance that they both descend from some quickly-dividing recent ancestor.

Harpak and Sella’s paper is a step toward a fully-realized population-genetic framework for serial transfer experiments and for experimental evolution more generally. We have long known that models of evolution have to account for selectively-neutral forces in addition to selection, and it is only sensible to think of evolutionary experiments in the same way. By responding to a frequently-used experimental setup, Harpak and Sella have done a service for experimental evolution, providing a better understanding of the selectively neutral processes that act in serial transfer experiments. At the same time, Harpak and Sella perform a service that is perhaps even more important for population-genetic theory. Evolutionary experiments are an unprecedented opportunity to learn about the actions of evolutionary forces at a large scale. Moreover, experiments offer researchers the power to control the setting in which evolution takes place and to repeat evolutionary processes. Population-genetic theorists, who until recently have had to make do with the single iteration of evolution that nature has provided, ought to jump at the chance to test their ideas in real-time. Harpak and Sella’s paper is an example of how theorists attending closely to experimental methods can lay the ground for improved experiments and improved theories.


Harpak A, Sella G (2014) Neutral null models for diversity in serial transfer evolution experiments. Evolution. DOI: 10.1111/evo.12454

Sprouffske K, Merlo LM., Gerrish PJ, Maley CC, & Sniegowski PD (2012). Cancer in light of experimental evolution. Current Biology22(17), R762-R771, DOI: 10.1016/j.cub.2012.06.065.

Paper author Arbel Harpak is a graduate student in the Pritchard lab.

Paper author Arbel Harpak is a graduate student in the Pritchard lab.


2 thoughts on “Catching the drift of experimental evolution.

  1. Pingback: What we’re reading: The creosote-eating gut microbes of wood rats, the molecular taxonomy of bats’ diets, and drift in experimental evolution | The Molecular Ecologist

  2. Pingback: Stuff online, untrammeled woodrat guts edition | Denim & Tweed

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