The influence of the composition of tradeoffs on the generation of differentiated cells

Amado, André; Campos, Paulo R. A.

doi:10.1088/1742-5468/aa71d8

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…On the other hand, an increased cost c has as an outcome that a smaller number of aggregates can be beared by the population at stationarity. A decreased number of groups with increased tradeoff costs is in agreement with previous studies (Amado and Campos 2017;Gavrilets 2010). Except for small costs c, the number of aggregates is peaked at small value of stickiness σ.…”

Section: Simulation Resultssupporting

confidence: 92%

A mechanistic model for the evolution of multicellularity

Amado

Batista

Campos

2017

Preprint

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Through a mechanistic approach we investigate the formation of aggregates of variable sizes, accounting mechanisms of aggregation, dissociation, death and reproduction. In our model, cells can produce two metabolites, but the simultaneous production of both metabolites is costly in terms of fitness. Thus, the formation of larger groups can favor the aggregates to evolve to a configuration where division of labor arises. It is assumed that the states of the cells in a group are those that maximizes organismal fitness. In the model it is considered that the groups can grow linearly, forming a chain, or compactly keeping a roughly spherical shape. Starting from a population consisting of single-celled organisms, we observe the formation of groups with variable sizes and usually much larger than two-cell aggregates. Natural selection can favor the formation of large groups, which allows the system to achieve new and larger fitness maxima.

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Section: Simulation Resultssupporting

confidence: 92%

A mechanistic model for the evolution of multicellularity

Amado

Batista

Campos

2017

Preprint

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“…Tradeoff relationships are a critical factor in controlling the degree of ecological specialization in natural populations (Michod et al 2006). From an evolutionary perspective, this issue has been largely debated within the context of germ-soma differentiation (Michod et al 2006;Leslie et al 2017), and also discussed within the context of the evolution of multicellularity (Gavrilets 2010;Ispolatov et al 2012;Amado and Campos 2017;Amado et al 2018). A central feature about the tradeoffs is their structure (Jessup and Bohannan 2008;Saeki et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Ecological specialization under multidimensional tradeoffs

Amado

Campos

2018

Preprint

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Although tradeoffs are expected to play an essential role in shaping the diversity in a community, their effects remain relatively nebulous and notoriously difficult to assess. This is especially true when multiple tradeoffs occur simultaneously. When dealing with single tradeoffs some information can be predicted based on their curvature. Does the same happen when dealing with multiple tradeoffs? What happens if the tradeoffs have opposing curvatures? To address these issues, we develop a resource-based model that encompasses multiple tradeoffs mediated by the acquisition and processing of the resources. The model considers a spatially structured population of microbial organisms that can grow on an arbitrary number of resources, which come into the system at a constant rate and diffuse through the environment. The individuals can adopt a variety of strategies through mutation constrained by tradeoffs, which renders the model adaptive. We assess population sizes and levels of ecological specialization. We find that when multiple tradeoffs are considered the classical intuition developed for single tradeoffs does not hold. The outcome can depend significantly not only on the curvature of the tradeoffs but also on resource availability.

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“…There are some mathematical models that try to describe the evolution of specialization among somatic functions [ 5 , 9 ]. For example, Ispolatov et al have considered the process of formation of two-cell aggregates [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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

Tverskoi

Aleskerov

2018

PLoS ONE

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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.

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The influence of the composition of tradeoffs on the generation of differentiated cells

Cited by 7 publications

References 47 publications

A mechanistic model for the evolution of multicellularity

A mechanistic model for the evolution of multicellularity

Ecological specialization under multidimensional tradeoffs

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

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