Investigating the role of intercellular communication on spatial differentiation through agent-based modeling
Glen, Chad Michael
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The initiation of heterogeneity within a population of phenotypically identical progenitors is a critical event for the onset of morphogenesis and differentiation patterning. Information flow between adjacent cells informs cell fate decisions and can occur by a number of mechanisms. Gap junction communication within multicellular systems produces complex networks of intercellular connectivity that result in heterogeneous distributions of intracellular signaling molecules. In this work, an agent-based computational model of ESC collective behavior was designed to prompt the state change of individual cells through intracellular accumulation of molecular differentiation cues throughout a colony. The model yielded complex, dynamic transport networks for delivery of differentiation cues between neighboring cells, reproducing the distribution and variety of observed morphogenic trajectories that result during retinoic acid–induced mouse ESC differentiation. Furthermore, the model correctly predicted the delayed differentiation and preserved spatial features of the morphogenic trajectory that occurs in response to perturbation to intercellular communication. The relationship between intercellular communication and neural differentiation was further interrogated through the CRISPRi-mediated knockdown of connexin43 (Cx43), the predominant gap junction protein in pluripotent cells. The selective removal of Cx43 during the differentiation of human induced pluripotent cells (hiPSCs) reiterated the role of intercellular communication in the temporal control of differentiation by delaying neural commitment. These findings suggest an integral role of gap junction communication in the temporal coordination of emergent patterning during early differentiation and neural commitment of pluripotent stem cells. To facilitate future studies of emergence in multicellular systems, a multiscale communication agent-based model generator (MsCAMgen) was developed in Python. MsCAMgen provides a framework for modeling various spatial aspects of a multicellular network without requiring explicit programming by the user. Each model is capable of accounting for cell division and growth, state changes between different cell types, extracellular diffusion of molecules that are secreted and consumed by cells, intercellular communication of small molecules between neighboring cells, and intracellular gene/protein networks. The ability to quickly add and remove these features at the discretion of the user makes MsCAMgen an ideal platform for investigating emergence in biological systems. Furthermore, the ease of simulating diverse morphological structures that can include and integrate each of these processes distinguishes MsCAMgen as a uniquely suited tool for optimizing the design of engineered living systems. In summary, this thesis interrogated the intercellular network within pluripotent colonies, described the spatiotemporal trajectory of early neural differentiation using an agent-based intercellular transport model, and developed an adaptable application to facilitate accelerated design of engineered living systems such as organoids by enabling the analysis of multiscale communication within cell populations of any morphology or organization.