Symbiotic Cell Differentiation and Cooperative Growth in Multicellular Aggregates

Abstract

As cells grow and divide under a given environment, they become crowded and resources are limited, as seen in bacterial biofilms and multicellular aggregates. These cells often show strong interactions through exchanging chemicals, as evident in quorum sensing, to achieve mutualism and division of labor. Here, to achieve stable division of labor, three characteristics are required. First, isogenous cells differentiate into several types. Second, this aggregate of distinct cell types shows better growth than that of isolated cells without interaction and differentiation, by achieving division of labor. Third, this cell aggregate is robust with respect to the number distribution of differentiated cell types. Indeed, theoretical studies have thus far considered how such cooperation is achieved when the ability of cell differentiation is presumed. Here, we address how cells acquire the ability of cell differentiation and division of labor simultaneously, which is also connected with the robustness of a cell society. For this purpose, we developed a dynamical-systems model of cells consisting of chemical components with intracellular catalytic reaction dynamics. The reactions convert external nutrients into internal components for cellular growth, and the divided cells interact through chemical diffusion. We found that cells sharing an identical catalytic network spontaneously differentiate via induction from cell-cell interactions, and then achieve division of labor, enabling a higher growth rate than that in the unicellular case. This symbiotic differentiation emerged for a class of reaction networks under the condition of nutrient limitation and strong cell-cell interactions. Then, robustness in the cell type distribution was achieved, while instability of collective growth could emerge even among the cooperative cells when the internal reserves of products were dominant. The present mechanism is simple and general as a natural consequence of interacting cells with limited resources, and is consistent with the observed behaviors and forms of several aggregates of unicellular organisms.

Emergence of cooperativity in a model biofilm

“…From Fig. S2 we can see that even when spatial effects are included, the cells population can spontaneously differentiate into two subpopulations, consistent with the results from the non-spatial model (Yamagishi et al, 2016).…”

“…We first derived a generic spatial cell differentiation model similar to (Yamagishi et al, 2016), illustrating the spontaneous emergence of cell cooperation and differentiation. Each cell in the population contains the same chemical reaction network with some shared chemicals (Fig.…”

“…The underlying dynamics of cooperation within multicellular communities are subjected to many systems-level studies (Picioreanu et al, 2004). One study created a model involving cells that contain simple chemical reaction networks with shared components to study differentiation of cell aggregates (Yamagishi et al, 2016). The study showed that differentiated cells, defined as cells with distinct chemical compositions but the same underlying catalytic networks, could arise from a homogenous population of cells due to chaotic behaviours of internal chemical dynamics.…”

“…In this study, we aim to investigate the underlying mechanism of the emergence of cooperation between microbes within a multicellular community, using the metabolic codependence in the B. subtilis biofilm as an example (Liu et al, 2015). We extended the minimal model of (Yamagishi et al, 2016) by incorporating spatial effects. In contrast to the minimal model of interior and peripheral cells (Liu et al, 2015), our model reveals that an aggregate of homogenous cells can form cooperative subpopulations as a natural consequence of resource restriction.…”

Noise-driven cell differentiation and the emergence of spatiotemporal patterns

“…Here, we propose that a stochastic model can provide a satisfactory explanation of how organization can emerge from noise. Proposing a stochastic model of cell differentiation is not an entirely novel concept, e.g., see [8,9] as examples of an impressive body of work produced by Kunihiko Kaneko and his colleagues on this subject and [10,11] as similar proposals regarding the possible role of stochasticity in generating phenotypic diversity. We argue that our approach differs from theirs and similar ideas in certain important aspects: firstly, our model assumes that cell fate is determined when the cell is born, and secondly, that stochastic fluctuations in the cell, and the effect of signals from neighboring cell in the multicellular case, drive the phenotype of the cell towards one attractor rather than another during cell division.…”