Natural selection in compartmentalized environment with reshuffling

  • A. S. ZadorinEmail author
  • Y. Rondelez


The emerging field of high-throughput compartmentalized in vitro evolution is a promising new approach to protein engineering. In these experiments, libraries of mutant genotypes are randomly distributed and expressed in microscopic compartments—droplets of an emulsion. The selection of desirable variants is performed according to the phenotype of each compartment. The random partitioning leads to a fraction of compartments receiving more than one genotype making the whole process a lab implementation of the group selection. From a practical point of view (where efficient selection is typically sought), it is important to know the impact of the increase in the mean occupancy of compartments on the selection efficiency. We carried out a theoretical investigation of this problem in the context of selection dynamics for an infinite non-mutating subdivided population that randomly colonizes an infinite number of patches (compartments) at each reproduction cycle. We derive here an update equation for any distribution of phenotypes and any value of the mean occupancy. Using this result, we demonstrate that, for the linear additive fitness, the best genotype is still selected regardless of the mean occupancy. Furthermore, the selection process is remarkably resilient to the presence of multiple genotypes per compartments, and slows down approximately inversely proportional to the mean occupancy at high values. We extend out results to more general expressions that cover nonadditive and non-linear fitnesses, as well non-Poissonian distribution among compartments. Our conclusions may also apply to natural genetic compartmentalized replicators, such as viruses or early trans-acting RNA replicators.


Directed evolution Co-compartmentalization Group selection Frequency-dependent selection Acellular genotype-phenotype linkage 

List of symbols

\({\mathbb {N}}\)

We assume \(0 \in {\mathbb {N}}\)

\({\mathbb {R}}_+\)

The nonnegative semiaxis: \({\mathbb {R}}_+ = [0,+\infty ) \subset {\mathbb {R}}\)


Space of continuous functions with compact support


Space of nonnegative functions from \(C_c\)


Space of generalized functions on \(C_c\) (Radon measures)


Subset of nonegative generalized functions

\({\mathbb {P}}\)

Subset of probability densities: \({\mathbb {P}} = \{\rho \in C_{c+}'\,|\,\langle \rho ,1\rangle = 1\}\)

\({\mathbb {P}}_p\)

Finite point-mass densities: \({\mathbb {P}}_p = \{\rho \in {\mathbb {P}}\,|\,\rho = \sum \limits _{k=1}^n a_k \delta _{x_k}\)}

\({\mathscr {I}}\)

Some very large closed interval: \({\mathscr {I}} = [0,{\mathscr {L}}]\)

\({\mathbb {P}}^{\mathscr {I}}\)

Densities in \({\mathscr {I}}\): \({\mathbb {P}}^{\mathscr {I}} = \{\rho \in {\mathbb {P}}\,|\, {\text {supp}}\rho \subset {\mathscr {I}}\}\)

\({\mathbb {P}}^{\mathscr {I}}_p\)

Finite point-mass densities in \({\mathscr {I}}\): \({\mathbb {P}}^{\mathscr {I}}_p = {\mathbb {P}}_p \cap {\mathbb {P}}^{\mathscr {I}}\)

\(\chi _A\)

Indicator function of the set A: \(\chi _A(x) = {\left\{ \begin{array}{ll}1,&{}x \in A\\ 0,&{}x\notin A\end{array}\right. }\)


Binomial coefficient \(\dfrac{n!}{k!(n-k)!}\)

\(\langle \rho , \varphi \rangle \)

The action of the generalized function \(\rho \) on the test function \(\varphi \)

\(\langle \rho , \varphi (x) \rangle \)

Implicitly \(\langle \rho (x),\varphi (x)\rangle \), where x is the internal variable

\(\langle \rho , \varphi (x,y) \rangle \)

Implicitly \(\langle \rho (y),\varphi (x,y)\rangle \), where y is internal and x is external

\(\langle \rho _x, \varphi (y) \rangle \)

Implicitly \(\langle \rho _x(y),\varphi (y)\rangle \), where y is the internal variable and x is a parameter of the distribution family \(\{\rho _x\}\)


a shortcut for \((1 - e^{-x})/x\)

\(\delta _a\)

\(\delta \)-function concentrated at a: \(\langle \delta _a, \varphi \rangle = \varphi (a)\)

\({\text {supp}}\varphi \)

Support of the function \(\varphi \): the closure of \(\{x \in {\mathbb {R}}\,|\,\varphi (x) \ne 0\}\)

\({\text {supp}}\rho \)

Support of the generalized function \(\rho \): \({\text {supp}}\rho = {\mathbb {R}} {\setminus } O_\rho \), where \(O_\rho \) is the largest open subset \(O \subset {\mathbb {R}}\) such that \(\rho |_O = 0\)

\(\bigotimes \limits _k \rho _k\)

Tensor product \(\rho _1 \otimes \rho _2 \otimes \ldots \)

\(\rho ^{\otimes n}\)

n-th tensorial power: \(\underbrace{\rho \otimes \rho \otimes \ldots \otimes \rho }_{n\text { times}}\)

Open image in new window

Convolution product \(\rho _1 * \rho _2 * \ldots \)

\(\rho ^{*n}\)

n-th convolution power: \(\underbrace{\rho *\rho *\ldots *\rho }_{n\text { times}}\)

\(f_\star \)

Pushforward of a generalized function by the map f of the domain: \(\langle f_\star \rho , \varphi \rangle = \langle \rho , \varphi \circ f\rangle \)

\({\mathrm {Corr}}(\rho _1,\rho _2)\)

Cross-correlation of densities \(\rho _1\) and \(\rho _2\)

\(\rho \)

Probability density of the phenotypes (in the model description and application)

\(\sigma \)

Probability density of the fitness in a compartmentalized population

\(\sigma _x\)

Probability density of the fitness conditioned on phenotype x


Mean phenotypic trait: mathematical expectation of the function \(x\mapsto x\) with respect to the phenotype distribution, \(\langle \rho ,x\rangle \) (in the model description and application)


The n-th moment of the phenotypic trait: mathematical expectation of the function \(x\mapsto x^n\) with respect to the phenotype distribution, \(\langle \rho ,x^n\rangle \) (in the model description and application)


Mean fitness of an individual in a compartmentalized population: \(\langle \sigma ,x\rangle \) (in the model description and application)


Mean fitness of an individual with pheontype x in a compartmentalized population: \(\langle \sigma _x,y\rangle \) (in the model description and application)

\({\text {ch}}x\)

Hyperbolic cosine of x: \({\text {ch}}x = (e^x + e^{-x})/2\)

\(\lambda \)

Poisson parameter: the mean number of individuals per compartment

\(\wedge \), \(\Rightarrow \), \(\lnot \)

Logical conjunction, implication, and negation, respectively

Mathematics Subject Classification

46F99 46N60 92D15 



The authors are grateful to David Lacoste and Luca Peliti for stimulating discussions and especially to Ken Sekimoto for numerous discussions and for critically reading the manuscript. We also would like to thank an anonymous reviewer for pointing out a noncritical but unpleasant mathematical mistake in the manuscript.

Supplementary material

285_2019_1399_MOESM1_ESM.nb (3 mb)
Supplementary material 1 (nb 3027 KB)
Supplementary material 2 (Mod 7 KB) (6 kb)
Supplementary material 3 (py 5 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Gulliver, ESPCI Paris, PSL University, CNRSParisFrance
  2. 2.Chimie Biologie Innovation, ESPCI Paris, CNRS, PSL UniversityParisFrance

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