Development of cell differentiation in the transition to multicellularity: a dynamical modeling approach

Abstract

Multicellularity has emerged and continues to emerge in a variety of lineages and under diverse environmental conditions. In order to attain individuality and integration, multicellular organisms must exhibit spatial cell differentiation, which in turn allows cell aggregates to robustly generate traits and behaviors at the multicellular level. Nevertheless, the mechanisms that may lead to the development of cellular differentiation and patterning in emerging multicellular organisms remain unclear. We briefly review two conceptual frameworks that have addressed this issue: the cooperation-defection framework and the dynamical patterning modules (DPMs) framework. Then, situating ourselves in the DPM formalism first put forward by S. A. Newman and collaborators, we state a hypothesis for cell differentiation and arrangement in cellular masses of emerging multicellular organisms. Our hypothesis is based on the role of the generic cell-to-cell communication and adhesion patterning mechanisms, which are two fundamental mechanisms for the evolution of multicellularity, and whose molecules seem to be well-conserved in extant multicellular organisms and their unicellular relatives. We review some fundamental ideas underlying this hypothesis and contrast them with empirical and theoretical evidence currently available. Next, we use a mathematical model to illustrate how the mechanisms and assumptions considered in the hypothesis we postulate may render stereotypical arrangements of differentiated cells in an emerging cellular aggregate and may contribute to the variation and recreation of multicellular phenotypes. Finally, we discuss the potential implications of our approach and compare them to those entailed by the cooperation-defection framework in the study of cell differentiation in the transition to multicellularity.

Keywords: cooperation; defection; differentiation; dynamical patterning modules; multicellularity; multiscale modeling.

Figure 1

Schematic description of the mathematical model for cellular coupling via different modes of communication. (A) Internal network composed by an Activator (A) and an Inhibitor (I) in each cell. The A/I ratio determines the color of the cell (if A≥ I: blue cell, if I > A: red cell). In this figure we represent the concentration of A and I in arbitrary units (a.u.) of five cells in a filament, where each cell is blue or red depending on the A/I relation. (B) Different types of cellular communication. Cells can communicate in three ways: (i) direct communication mediated by the diffusion of a signal to adjacent contacting cells (green), (ii) indirect communication in which the signal can be diffused to and sensed from the medium (orange), and (iii) a mixed scenario in which both direct and indirect communication are allowed.

Figure 2

Patterns of differentiated cells emerging in different scenarios of cell-to-cell communication and for different degrees of adhesion among cells. We show a control scenario of individual cells where, independently of the communication type, cells always reached a steady state where A ≥ I (blue cells). Another control scenario shows the simulation of a population of cells without adhesion. In this case, when indirect communication is allowed, different cell states are reached (blue cells: A ≥ I; red cells: I > A), without any patterning. When cellular adhesion is introduced and is high, three different patterns of differentiated cells are attained. If adhesion is low (i.e., cellular movement allowed) these patterns are qualitatively similar, but are not stable.

Figure 3

Detail of the cells and the external medium in a bi-dimensional lattice. Five different cells are depicted (τ = 1, 2, 4, 5, 7) belonging to two different cell types (σ = blue, red), a non-flux frontier (in green) and the external medium (in black).