Cooperative differentiation through clustering in multicellular populations

Abstract

The coordinated development of multicellular organisms is driven by intercellular communication. Differentiation into diverse cell types is usually associated with the existence of distinct attractors of gene regulatory networks, but how these attractors emerge from cell-cell coupling is still an open question. In order to understand and characterize the mechanisms through which coexisting attractors arise in multicellular systems, here we systematically investigate the dynamical behavior of a population of synthetic genetic oscillators coupled by chemical means. Using bifurcation analysis and numerical simulations, we identify various attractors and attempt to deduce from these findings a way to predict the organized collective behavior of growing populations. Our results show that dynamical clustering is a generic property of multicellular systems. We argue that such clustering might provide a basis for functional differentiation and variability in biological systems.

Complex and unexpected dynamics in simple genetic regulatory networks

“…6) revealed a significant enlargement (≈ 50%) of the IHSS stability interval in comparison to the minimal case of N = 2 coupled oscillators (for details see Ref. 61). This is a result of clustering, or more specifically, of the increased number of possible distributions of the oscillators between the two stable protein levels.…”

“…The first one leads to phase attractive coupling for robust synchronized oscillations, 54 whereas the latter one evokes phase-repulsive influence, [57][58][59] which is the key to multi-stability and rich dynamics including chaotic oscillations. [60][61][62] Thus, through a single rewiring in the connection between the basic repressilator and the additional quorum sensing feedback loop, the entire dynamics of the cellular population are significantly altered. As a consequence, the previously favored in-phase regime becomes unstable.…”

“…30,71,79 The three-cluster decomposition dominates, with a nearly equal number of oscillators in each one, and a distinct phase relation between separate clusters (for details see Table 2 in Ref. 61). This phenomenon dominates for large system sizes, over wide ranges of coupling, Q.…”

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“…in laser [9], neuronal systems [10], [11], etc. In spite of this, it has found applications in diverse field [7], [12], [13].…”

Revival of oscillation from mean-field-induced death: Theory and experiment

“…This has many potential applications: For example, the AD state is important to suppress unwanted oscillations that hinder a certain process, e.g., in laser systems, chattering in mechanical drilling process, etc [1]. Similarly, the OD state has a significant role in understanding of many biological processes, e.g., synthetic genetic oscillator [4], cardiovascular phenomena [5], cellular differentia- * tbanerjee@phys.buruniv.ac.in tion [6], etc. On the other hand, the research in the topic of revival of oscillation is important because many physical, environmental, biological and social processes require stable and robust oscillations for their proper functioning: Examples include, El Niño/Southern Oscillation in ocean and atmosphere [7], brain waves in neuroscience [8], electric power generators [9], cardiopulmonary sinus rhythm of pacemaker cells [10], etc.…”