Modeling functional specialization of a cell colony under different fecundity and viability rates and resource constraint

PLoS One. 2018 Aug 8;13(8):e0201446. doi: 10.1371/journal.pone.0201446. eCollection 2018.

Abstract

The emergence of functional specialization is a core problem in biology. In this work we focus on the emergence of reproductive (germ) and vegetative viability-enhancing (soma) cell functions (or germ-soma specialization). We consider a group of cells and assume that they contribute to two different evolutionary tasks, fecundity and viability. The potential of cells to contribute to fitness components is traded off. As embodied in current models, the curvature of the trade-off between fecundity and viability is concave in small-sized organisms and convex in large-sized multicellular organisms. We present a general mathematical model that explores how the division of labor in a cell colony depends on the trade-off curvatures, a resource constraint and different fecundity and viability rates. Moreover, we consider the case of different trade-off functions for different cells. We describe the set of all possible solutions of the formulated mathematical programming problem and show some interesting examples of optimal specialization strategies found for our objective fitness function. Our results suggest that the transition to specialized organisms can be achieved in several ways. The evolution of Volvocalean green algae is considered to illustrate the application of our model. The proposed model can be generalized to address a number of important biological issues, including the evolution of specialized enzymes and the emergence of complex organs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biological Evolution
  • Cell Communication / physiology*
  • Cell Differentiation / physiology*
  • Cell Survival / physiology
  • Chlorophyta / cytology
  • Chlorophyta / physiology*
  • Fertility / physiology*
  • Germ Cells, Plant / physiology
  • Models, Biological*

Grants and funding

The paper was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics of Moscow (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project “5–100”. The work was conducted by the International Laboratory of Decision Choice and Analysis (DeCAn Lab) of the National Research University Higher School of Economics of Moscow. This work was also supported by Le Fonds Quebecois de la Recherche sur la Nature et les Technologies (grant no. 173878 to VM) and Natural Sciences and Engineering Research Council of Canada (grant no. 249644 to VM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.